“Good is the enemy of great … It is the key reason why so few become great” – Jim Collins

It is puzzling that employee experience (EX) has only recently gained traction. After all, customer experience (CX) has been around for decades, so why has it taken organizations so long to look inward—at their people—who make things happen? 

The evidence for EX is strong, yet many organizations are failing at it and are unable to achieve this state in practice. Only a select few firms have executed people-first approaches, excelled at placing their employees at the core, attained the ‘elite’ status, and, in turn, are rewarded with a highly engaged, productive workforce and increased revenue and profits.

What is an “Elite”—a HPO? How do we describe this rare, unique, and often elusive state of exclusivity?

High Performing Organizations (HPOs), aka the ‘Elites,’ “are considered the best or most powerful compared to others of a similar type” (Cambridge Dictionary). They possess core traits that set them apart from the rest and typically achieve “financial and non-financial results that are exceedingly better than those of its peer group over a period of time, by focusing in a disciplined way on that which really matters to the organization” (de Waal, 2019, p. 5). Instead of concentrating solely on outputs, such as revenue and profits, the emphasis is typically on the employees who make those outputs possible. Consequently, these firms have found the right balance between people and financial value.

n.b. It is important to note that sustainable desired results over a prolonged period characterize high performance. An ‘elite’ organization does not perform well out of sheer luck but because of an effective strategic plan with an average time horizon of three years or more to increase performance and beat the competitive peer group. 

The driving force for adding value is evident in ‘elite’ organizations. Long-term strategic commitment, as opposed to short-term financial goals, is intact. Culture is actively integrated into corporate plans, and there is a conscious attempt to connect culture with people, systems, processes, and behaviors. 

The Modus Operandi

Members of the ‘elite’ club have a common thread. The ‘elites’ know that core organizational capabilities and people competencies drive financial and operational performance and enable strategy. They have figured out what it takes for sustained success. So, they set their sights on the right target—their people—to achieve measurable results. 

The ’elites’ employ a rare combination of people-first leadership and an employee-centered approach and effectively translate business strategy into a robust people strategy. They place the employees front and center and ensure an impactful employee experience (EX) to combat external threats, mitigate competitive forces, and achieve their goals and objectives.  

Also, they are disciplined not to be distracted by the latest leadership fad and remain on track with people-centered strategic initiatives aligned with their core values, purpose, and vision.

Further, the ‘elites’ improve what makes them successful—people, processes, systems, behaviors—and strive for improvement efforts on a continuum to excel in those contexts. 

The primary reason for an ‘elite’ taking a different angle and changing strategy (a deviation from the initial plan) is if an opportunity arises to strengthen its core capabilities and competencies. They are aware that deviating from an established path could directly impact and mean decreased engagement and declining performance, so leadership is usually keen on maintaining discipline (de Waal, 2019; Morgan, 2017; WTW, 2019).

The Key Differentiators

There are many ingredients, granted, to the secret sauce that differentiates the ‘elites’ from the rest; however, the decision is to highlight the essential contexts!

They are:

People First Leadership –Transformational leaders who invoke a people-first philosophy and signal a commitment to meeting the needs of their employees. People-first leaders realize that the employees are the decisive factor for the measurable results, so they use a people-centered design to create an employee-focused culture. It fosters an environment of trust, inspiration, loyalty, commitment, and respect in the workforce.

People-Centered Strategies – As evident, the ‘elites’ wisely focus on their ‘people’ first and places them at the core of their business strategies. They effectively translate business strategy into a robust people stratagem by attracting and retaining the most capable individuals and connecting their people with corporate strategy. It transcends a mere employee value proposition. 

High Engagement Corporate Culture – The organizational culture is not accidental; it is a planned, aligned, established, managed, and regularly monitored environment designed to achieve strategic goals and objectives. As a result, regardless of job roles/positions, employees are motivated to pursue corporate goals actively beyond the call of duty. 

Employee Experience Design –Looking through the lens of the employees and formulating an applicable EX. In short, creating an employee-focused organizational culture/environment based on their needs, desires, and expectations—i.e., an overlap between the employees’ reality and the organizational design, actions, and behaviors aligned with that reality (Morgan, 2017). (see Figure 1)

EX: The intersection of employees’ needs, wants & expectations and a corporate design and actions & behaviors that fulfill those needs, wants & expectations

An ‘elite’ organization knows its employees, recognizes that its employees have expectations, offers what they desire, and delivers an excellent employee experience (EX), the foundation of producing a superior customer experience (CX), and, in theory, exceptional financial performance.

Key differentiators/characteristics have allowed the ‘elites’ to consistently outperform their peers over the long term in both people and financial indices.

So, what exactly is the employee experience—EX?

Defining EX

EX has many definitions; it is a broad and fluid term that is defined differently depending on the context in which the nomenclature is depicted. 

From a human resource management (HRM) perspective, EX refers to the totality of encounters, observations, and emotional responses felt by an employee due to multiple workplace tasks, processes, protocols, and behaviors during his/her tenure in an organization. In essence, it is an employee’s experience through various touchpoints—from the recruitment process, through the employee lifecycle, to the offboarding/outplacement stage.

Dery & Sebastian (2016) view EX “as the work complexity and behavioral norms that influence employees’ ability to create value” (para. 4).

Morgan (2017) describes EX as “the intersection of employee expectations, needs, and wants and the organizational design of those expectations, needs, and wants” (p. 8).

Willis Towers Watson (2019) views EX as (a) “connecting with people and organizational purpose” and (b) “contributing to work and being rewarded accordingly” (p. 2). 

Massachusetts Institute of Technology (MIT) Center for Information Research (2022) defines EX as “The extent to which employees are enabled or constrained by the work environment and work habits to do their jobs today, and to re-imagine their jobs of tomorrow” (para. 6). 

Gartner (2023) declares, “Employee experience is the way in which employees internalize and interpret the interactions they have with their organization, as well as the context that underlies those interactions” (para. 1).

Deloitte (2023) explains EX as “the human connections and mechanisms that create a high level of purpose and meaning between workers and the organization. At the core of any human experience is the desire to belong and feel connected with others and to contribute to something of significance and value” (para. 3).

So, it is evident there is no clear definition; however, the constants are organizational design & behavior, corporate culture, and workplace environment. It is about the overall experience throughout an employee’s lifecycle. A highly positive EX contributes to engagement, motivation, performance, and job satisfaction, whereas a highly negative EX could have an adverse effect.

The EX Factor

So, what does it take to create and foster an excellent employee-focused workplace and deliver the desired EX? Young and Kulesa (2019)—Willis Towers Watson (WTW)—offer some insights! 

Based on extensive and reliable research, WTW developed an evidence-based model of EX, identifying the four factors that matter most to employees. (see Figure 2) 

They are … 

• Purpose – A strong sense of purpose
• People – Connecting with great people and leaders
• Work – Doing great work in a thriving organization
• Total Rewards – Growth & Reward opportunities in return

Note. Adapted from Breakthrough research: Identifying the factors that make a high-performance employee experience (HPEX), by S. Young and P. Kulesa, 2019, Willis Towers Watson, Copyright © 2017 by Willis Towers Watson. 

Each dimension contributes to an organizational culture—its DNA—the beliefs, values, and practices that guide behaviors. Consequently, a firm’s cultural framework directly affects its EX, customer experience (CX), shareholder value, business-partner relationships, and business results.

 Along the four dimensions—People, Purpose, Work & Total Rewards—the EX-factor that tops the employees’ list are: Inspiration, Drive, Trust, Growth & Market Focus. (see Figure 3)

Global High-Performance norm companies vs. Global average (% favorable)

Note. Adapted from Breakthrough research: Identifying the factors that make a high-performance employee experience (HPEX), by S. Young and P. Kulesa, 2019, Willis Towers Watson, Copyright © 2017 by Willis Towers Watson

The ‘elites’ excel in these areas and generally set themselves apart from their peers with the key differentiators shared prior. They are unique because of their people-first leadership, people-centered strategies, high-engagement corporate culture, and EX design, which lead to inspiring employees, building trust, helping employees achieve their potential, and being agile and innovative in the marketplace. 

As evident, the key differentiators go far beyond the basics of rewards and tend to be more philosophically based—a people-first mindset—and woven into the organization’s culture. While rewards and other benefits (tangible & intangible) are vital, they are supplementary to the EX. 

The Data

WTW discovered that the dimensions—People, Purpose, Work & Total Rewards—determine whether an organization will underperform or outperform its peers. There is a direct correlation between EX and business results. EX predicts sustained financial success, and the ‘elites’ have impressive and sustained performance. They outperform their peers for top-line growth, bottom-line profitability, and shareholders’ return. (see Figure 4) 

Note. Adapted from Breakthrough research: Identifying the factors that make a high-performance employee experience (HPEX), by S. Young and P. Kulesa, 2019, Willis Towers Watson, Copyright © 2017 by Willis Towers Watson. 

WTW also found that people-centered organizations are:

• 3x as likely as other organizations to report employees are highly engaged.
• 93% more likely to report significantly outperforming their industry peers financially.
• 10% less likely to report difficulty attracting and retaining key employee segments.

Other Supporting Evidence

Despite a shortage of scientific research, various case studies (including the WTW research) and business reports have documented the vital benefits of EX design and application. 

Research by Jacob Morgan (2017) of 250 diverse organizations reveals companies that invest in EX outperform their competitors. These firms have “more than four times the average profit, more profitable and more than two times the average revenue” (para. 6). Additionally, they outperformed the S&P 500 and the NASDAQ and are often showcased in Fortune’s 100 Best Companies to Work. 

An MIT study by Dery & Sebastian (2016) produced similar findings, observing that EX predicts business performance. Companies in the top quartile for EX are determined to be 25% more profitable, have twice the innovation, and double the CX than competitors.

Also, Gallup (2019) discovered that firms with highly engaged employees have more than four (4)times the earnings-per-share (EPS) growth rate of those with low engagement scores. They also have 21% higher profitability and substantially better customer engagement, productivity, and employee retention.

Additionally, a study by The Josh Bersin Company (2021) uncovered that “Level 4” companies (the ‘elites’)—i.e., those that offer the most robust EX, compared to their peers, have 20% more equitable growth.

Further, McKinsey & Company (2021) research found a strong correspondence between employees’ stated needs and the underlying drivers of their engagement, well-being, and work effectiveness. 

Finally, Gautier et al. (2022), Harvard Business Review, established a causal link between EX, CX, and business outcomes—revenue and profits. The data reveal that EX drives revenue. 

Based on the data, there is a direct linkage between EX, employee engagement, and workforce potential that drives high employee productivity and increases corporate performance and financial returns. There is undeniably a significant return to organizations focusing on EX over the long term, not just short-term employee engagement in the here and now.

So, how do organizations become a member of the ‘elite’ club and achieve ‘excellence’ status? It requires adopting a new mindset, rethinking the work experience, and crafting an appropriate EX strategy!

Designing an EX Strategy

Creating the EX is not a process but a corporate strategy. Since EX includes touchstones that cover the entire employee lifecycle journey, leaders need to see employees as customers and create an environment that allows them to thrive in the workplace and in their careers. A well-designed and executed EX strategy could fit the bill. To that end, when drafting an EX strategy, the key is enhancing the workforce experience spanning multiple functions/departments/units/teams/workgroups. It is an organizational-wide employee initiative, not for a select few! 

A core framework with the vital elements of an EX strategy is essential to serve as a blueprint for success. This article provides a basic framework. (see Figure 5) 

Align: Develop experience vision aligned with talent and business strategies

Focus: Define and brand the end-to-end employee experience

Execute: Implement prioritized initiatives roadmap with targeted people & business strategies

Measure: Capture the value and find and implement enhancements

Monitor: Review progress daily, identify risks, and offer solutions to unexpected outcomes 

n.b. The implementation and measurement of EX are like an audit of the corporate culture and current workplace environment. It is a continuous process, not a one-time initiative.

Design an EX-framework to meet the employees’ needs, desires, and expectations that align with the DNA—core values, culture, goals, mission, and vision of the enterprise. The EX-framework should be company-specific and translated by leaders to the precise organizational situation in its current state. 

Once the strategic framework has been planned and crafted, the implementation and change management journey can begin to ensure a distinct, impactful, and lasting EX effect. 

An EX-Roadmap

“The change management journey has many obstacles and challenges; however, the destination will be reached by staying the course” Karen Wagner-Clarke.

First, it is crucial to preface this roadmap by stating that EX is not an event; it is not a project; it is not a process; it is a cross-functional strategy that covers the entire employee lifecycle. 

Second, assuming one employee-improvement action magically moves the organization to deliver excellent EX is unwise. 

Third, each company has its DNA, so the best practices at one company may not work at your organization. Therefore, the EX design should be company-specific and the best fit for the enterprise. (see Figure 1) 

Fourth, an impactful EX cannot be created and implemented in a day; it is a long-term plan that should align with the firm’s core values, goals, and objectives, be thoughtfully developed, and be adopted over time. 

The goal is to create a corporate mindset, state, and employee reality to build a community of highly engaged, committed employees eager to work towards a common mission and vision. The result will likely be an enriched EX, elevated CX, enhanced productivity, increased profits, and high return on investments (ROI).

n.b. The roadmap intends to serve as a core guide, nothing else! It offers actionable insights and advice for leaders who want to implement effective practices to drive EX excellence.

Recommendations:

Getting out of the comfort zone: Abandoning the traditional idea of wanting to achieve higher turnover, revenue, and profit growth. Instead, broadening the horizon and rethinking the workplace and EX by placing people as the core strategy will eventually translate into increased growth and profitability. 

Instilling shared purpose: Employees want to feel invested in their organization’s purpose and ultimate objective. So, institute regular meetings to discuss emerging issues and create a framework for addressing them and deciding ‘why,’ ‘what,’ ‘how,’ and ‘when.’ Also, create a protocol for sharing the results specifically, timely, and transparently throughout the organization. 

Being employee-centric: Placing the needs and expectations of the employees as the focus of the organization’s operations. An ‘employee-centric’ environment means prioritizing psychological safety, accountability, engagement, autonomy, and creativity. The employees become the top priority!

Creating a workplace culture where everyone feels valued: While every organization has its recipe, prioritizing people’s value over financial value—should always be the secret ingredient. From involving employees in the decision-making process and garnering ‘buy-in’ to providing support to achieve their full potential, leaders should make the workforce feel that their needs, desires, and expectations are prioritized. Creating the right workplace environment and state for employees to excel! 

When employees feel valued and considered more important than profit margins, it increases their motivation, engagement, satisfaction, and performance. Engaged employees work passionately, feel connected to the company, and help move the organization toward excellence. 

Linking Culture with Systems & Processes: It means adapting a collaborative ‘systems thinking’ approach and creating seamless experiences with cross-functional and cross-enterprise (when applicable) workflows to connect people, places, functions, systems, and processes and achieve better business outcomes.

Fostering psychological safety: Having a culture grounded in psychological safety makes it easier for people to take interpersonal risks, raise their concerns, voice their perspectives, and challenge the status quo. In addition, it means creating a high-performance zone of high psychological safety and accountability, which allows employees to create, innovate, and strive for excellence. 

Promoting personal & professional growth & development: Most organizations offer programs to foster professional growth and development; however, employees also want opportunities for personal growth, for example, implementing career coaching, external training courses, certifications, language lessons, and community service programs. Broadening the definition of ‘employee development’ provides opportunities to increase employees’ skills and performance. Learning something new, even when not directly work-related, hones employees’ learning skills. It is a winning proposition for both employees and employers!

Listening to the voice of the employees. Actively listening to employees about their needs, desires, and expectations is imperative. It is best to take the time to monitor behaviors, send out surveys, listen to ideas, and have casual meetings and conversations. Also, take a genuine interest in what employees are doing to gain deeper insights that will help to better inform where the firm’s people-centered approach should be focused. Then, act proactively on the knowledge gained to improve their work experience!

Suggestion: The proposal is to replace annual or biannual employee engagement surveys with ongoing pulse, organizational culture, business process feedback surveys, and open feedback platforms to capture employee feedback on a continuum. Include candidate interviews; engagement, employee satisfaction, and EX surveys; ongoing performance conversations; quarterly, semi-annual, or annual performance reviews; and exit interviews to gather a real-time understanding of employee issues. When reviewing the results, contemplate these questions:

1. How do our employees feel about the workplace culture/environment?
2. Is the organization living up to the employees’ expectations?
3. What have we learned based on the responses?
4. What improvements could be made to meet our employees’ needs and expectations?

EX efforts should build a continuous flow of feedback (a feedback loop) between leadership and the workforce. In this manner, leadership is aware of those aspects of the EX that are working well and areas requiring improvements. 

Emphasizing the employee experience

The ‘people-first’ philosophy and EX initiative should be woven into the company’s DNA and emphasized throughout the employee lifecycle. Leaders should ensure that all programs, processes, protocols, and strategies are developed and implemented, keeping employees front and center to facilitate a culture that takes care of its people from hire to departure/retire. EX is a cross-functional strategy spanning all units, teams, and work groups! 

In Summary

Undoubtedly, the ‘elites’ have discovered ways to enrich the EX, leading to purposeful, productive, meaningful work. They provide employees with an all-encompassing encounter supported by a people-first cross-functional strategy that makes a difference. 

Delivering a firm’s EX-factor, achieving workforce excellence, anddistinguishing an organization as an ‘elite’ is rare but not impossible! Nevertheless, joining the ‘elite’ club and being an employer of choice requires looking beyond the traditional perks and programs companies have long touted as differentiators. Instead, it necessitates focusing on the EX factors that matter to your workforce, delivering the best EX, and meeting the employees’ needs, desires, and expectations. 

After all, “In a world where money is no longer the primary motivating factor for employees, focusing on the employee experience is the most promising competitive advantage that organizations can create!” – Jacob Morgan, The Employee Experience Advantage

References

Cambridge Dictionary. (n.d.). Elite. In Cambridge Dictionary.org. Retrieved May 2, 2023, from https://dictionary.cambridge.org/us/dictionary/english/elite

Collins, J. (2001). Good to Great: Why some companies make the leap and others don’t Hardcover. New York, NY: HarperCollins.

de Waal, A. (2019). What makes a high performance organization: Five validated factors of competitive advantage that apply worldwide. Amsterdam, Netherlands: Warden Press.

Deloitte (2023). Elevating the workplace experience: The importance of employee engagement and meaningful work. https://www2.deloitte.com/us/en/pages/human-capital/articles/elevating-workplace-experience.html

Dery, K. & Sebastian, I. M. (2017). Building business value with employee experience: Massachusetts Institute of Technology Center for Information Systems Research. https://cisr.mit.edu/publication/2017_0601_EmployeeExperience_DerySebastian 

Gallup (2019). Employee Engagement on the Rise in the U.S. https://news.gallup.com/poll/241649/employee-engagement-rise.aspx

Gartner (2023). Gartner Glossary: Employee Experience. https://www.gartner.com/en/human-resources/glossary/employee-experience#:~:text=Employee%20experience%20is%20the%20way,context%20that%20underlies%20those%20interactions.

Gautier, K., Bova, T., Chen, K. & Munasinghe, L. (2022). Research: How Employee Experience Impacts Your Bottom Line. Harvard Business Review. https://hbr.org/2022/03/research-how-employee-experience-impacts-your-bottom-line

Massachusetts Institute of Technology (MIT) (Dec 13, 2022). Exploring the employee experience. https://news.mit.edu/2022/exploring-employee-experience-nick-van-der-meulen-1213

Morgan, J. (2017). Why the Millions We Spend on Employee Engagement Buy Us So Little. Harvard Business Review. https://hbr.org/2017/03/why-the-millions-we-spend-on-employee-engagement-buy-us-so-little.

Morgan, J. (2017). The employee experience advantage: How to win the war for talent by giving employees the workspaces they want, the tools they need, and a culture they can celebrate. Hoboken, NJ: Wiley & Sons. 

Young, S. & Kulesa, P. (2019). Breakthrough research: Identifying the factors that make a high-performance employee experience (HPEX). Willis Towers Watson. https://www.wtwco.com/en-GB/Insights/campaigns/breakthrough-research-on-employee-experience

The post <h1>The ‘EX’ Factor</h1> <h3>What the ‘Elites’ do differently: </h3> appeared first on The Experience Journal by QuestionPro.

Summary

The most successful brands build their marketing strategies from a deep understanding of the people they are trying to resonate with and reach. And for years, marketing research has provided brands with answers to their questions about what consumers are doing in order to gain this deep understanding of people. When this understanding is truly met, brands grow and innovate and remain relevant to their customers. But on the flip side, not all market research has been successful. And thus, some brands have made decisions based on market research that were ultimately fails. For example, did Colgate really think expanding into the Frozen Entrees was acceptable for its brand?¹

Market research has been around for over one hundred years now. It started out with a desire to ask consumers questions. And today, consumer research still mainly relies on consumers to tell us answers to questions we have about their lives, their behaviors, and their actions. This type of insight is still very valuable, but it must be done right. And we believe asking consumers to recall their behaviors should be secondary to leveraging their actual behavioral data. 

With consumers’ busy lives paired with the complexity of the world we live in we can’t expect consumers to accurately recall what they did. Not because they don’t want to tell us the truth, but because with everything someone is doing, it would be hard for any of us to recall exactly what we did yesterday, let alone last week or last month, with precise detail. 

Fortunately, with the explosion of technology and the connected world we live in today, consumer behavior can be recorded in real-time, providing actual consumer behavior versus recalled behaviors. 

Table of Contents

• Summary
• What is Behavioral Data and Specifically Digital Behavioral Data:
• Why Behavioral Data:
• The Right Business Challenges for Behavioral Data:
  • Defining a competitive set and keeping a pulse on key competitors
  • Define an audience 
  • Pinpoint how and where to reach your target customer
  • Uncover areas of confusion or opportunity areas where customers may be struggling to understand or find what they are looking for.
  • Discover the consumer journey
  • Keep a pulse on new trends or foresights
  • Guide Search Term Optimization
• How to be successful with behavioral data:
• Conclusion:
  • • Resources

What is Behavioral Data and Specifically Digital Behavioral Data:

Behavioral data could be described as any data set that describes consumer behaviors, which could include stated behaviors based on recall and collected through surveys. However, I’d argue that type of data is still survey data on behaviors, not behavioral data. 

True behavioral data is captured in real-time when consumers are doing it, without any recall or reliance on their memory of what was happening. Behavioral data is being captured all around you – think about smart devices, GPS/map software, loyalty programs, connected vehicles, and more. 

The type of behavioral data that I am focusing on today is digital behavioral data. There are various panels out there that capture consumers’ digital behaviors, tracking everything they are doing online, where they are going, what they are searching for, how much time they are spending, sites they are shopping, and what they are purchasing – ultimately painting a picture of the online journey and path that consumers are taking. Some provide more than others, and some even provide cross-device, in-person location data, or social advertising data. 

True behavioral data sets are permission-based, meaning consumers have chosen to opt-in and allow for their data to be shared. They do not rely on cookies, meaning it is future-proof. The data is longitudinal, capturing every click, every app open, and every search a consumer is making. 

These data sets are extremely large (terabytes and terabytes), they are unwieldy, and for the average marketer or researcher, may seem impossible to analyze and make sense of. Each line of the data represents a separate consumer event that was captured – a search, a click, a website visit, an app open, an in-person location visit, an ad they saw. 

In addition to just the digital events collected, the data often has many descriptive variables that accompany it. An event timestamp provides the order of events and how long someone spent in an app or looking at a specific page. When someone agrees to share their data, often a short sign-up survey collects key demographics or attitudinal statements that can be appended to the behavioral data. 

The data itself, while it may not seem insightful at first, provides a digital footprint for consumers which when analyzed, can help brands understand who these consumers really are. Not just what they are doing online but who they are as humans. What are their interests, what hobbies do they have, what keeps them up at night, what things are they buying online for use offline, and more? Behavioral data ultimately provides brands with a leg up on being truly consumer-centric and getting to know consumers better than anyone else.

Why Behavioral Data:

The average person spends 6 hours online every day. And today, there are over 200 million active websites and over 3 million apps available in the app store. And it does not stop there… over 2000 new apps and 250,000 websites are released each day!

We live in a digital world; everyone is online all the time. Traditional primary research would ask people to tell us about everything they are doing. While they may try, it would be hard to accurately recall everything they did even if it was just 5 minutes ago. 

Additionally, traditional panels continue to see an increase in bad data – whether that be bots, people rushing through surveys and not really reading them, or even the creation of the survey itself being poor. However, consumers are increasingly interested in signing up for panels that are easy and have a fair exchange. In the case of behavioral data panels, consumers are rewarded with fair incentives or other types of rewards like promotions and coupons in exchange for providing access to their data. The details of what they are sharing are very transparent, and most find it to be a very fair exchange. 

While traditional survey methods can provide a glimpse into consumers’ behaviors, it simply isn’t the most accurate way to understand behaviors. I’ve said it before, but at this point, it’s not really a choice. It’s our responsibility as an industry to stop asking consumers to recall behaviors that we know they can’t articulate. 

Leveraging this rich digital consumer behavior allows for a true understanding of consumers. It can help brands to extract insights to guide digital strategies, uncover how consumers are shopping and cross-shopping brands, keep a pulse on key competitors, and understand consumer research processes and shopping journeys. 

The ability to monitor consumers’ digital engagements with key brands and understand retention, loyalty, and social ad effectiveness in real-time presents unlimited opportunities. 

The Right Business Challenges for Behavioral Data:

Not all business questions are right for any one methodology. But many common business challenges brands come up against aim to understand consumers’ behaviors better. When this is the case, a behavioral-first mindset should be embraced. 

Some of the most common business challenges could be better answered and solved using digital behavioral data. 

Defining a competitive set and keeping a pulse on key competitors

Brand tracking has been around for many years and aims to track consumer perceptions and behaviors longitudinally. A typical brand tracking survey asks consumers about their awareness, current interactions, and future intentions with the brand and key competitors. It may also ask questions about brand perceptions and brand imagery. While consumers may not interact the same way digitally with all brands, ‘digital is not for me’, is no longer an excuse any brand should be using. 

Digital behavioral data can provide a glimpse into those who may be exploring a category or a specific brand but not admit that they are considering – the data provides upstream shopping and research that is happening, those touchpoints that are shaping and influencing future behaviors, that consumers don’t even realize are occurring. 

With digital behavioral data, a true competitive set can be revealed. You may not initially consider these other brands or categories competitors, but ultimately the behavioral data can show you they are competing for your customer’s share of wallet or time. Monitoring these brands, in addition to yours and your traditional key competitors, can ensure you keep a pulse on what is happening in real-time instead of waiting to see the impact happen in the market. You can observe overlap, switch, and even find new entrants before they make a splash in the market.

Digital behavioral data should not replace brand trackers but be sought to be the foundation of competitive and category insights. And when paired with a primary survey to capture those additional data points which cannot be derived from the behavioral data, a holistic picture of the consumer is gained. Connecting the behaviors that drive brand perceptions or connecting to some level of offline behavior can ensure your brand is showing up in the right place and resonating in the right way.

Define an audience

For years we’ve relied on survey data, asking consumers to tell us about their hobbies, their interests, and what things they like to do. But can you remember the last time someone asked you that? Did you know the answer right away or did you have to really think about it? Sometimes I feel like the things I like to do are not even the things I’m able to spend my time doing. So how does that help a brand know how to message directly to me?

The industry made a slight move to appending this type of information from third-party data sets. Much of which is modeled off where you live – assuming that you are like your neighbors. Maybe true, maybe not. These approaches increase lift by attempting to send messages to those who it will resonate with versus sending it out blindly to everyone. 

Profiling an audience based on behavioral data is mainly an untapped resource today. An individual’s behaviors are indicative of who they are as a person. Digital behavioral data analyzed in the right way can tell us what someone’s hobbies and interests are, what they are buying online to use offline, what types of media and advertising attracts them, and more. 

Knowing this type of information can guide messaging content and ad placement strategies. It can be appended to consumer segmentation and a brand’s target segments to better understand who they are, bringing them to life and revealing actionable strategies to reach them. 

Pinpoint how and where to reach your target customer

Ad agencies have access to many different sources of information guiding their digital ad placement recommendations. Many of these include the top websites overall or the top within a category. And many brands embrace an endemic digital marketing strategy placing ads in locations where consumers are engaging with their category. However, with the millions of things, consumers can do online, their engagement with your category might be so small that you’re actually missing them and ultimately wasting your media budget. 

With behavioral data, you can compare your target customer to a representative general population sample and understand those places online where your target is spending more time than the rest of the population. It also can guide strategy, uncovering lesser-known or hidden pockets where you can find your target customer. 

Uncover areas of confusion or opportunity areas where customers may be struggling to understand or find what they are looking for.

How many times have you gone online looking for something, scrolling through the search results and don’t see exactly what you’re looking for. Try a different search, still don’t see it. Maybe you try to click on something that could be it, but no. Go back search again. Get frustrated and go do something else. But you still don’t have an answer, so you try again later. 

Behavioral data can help you see these frustrating consumer journeys. What specifically they are asking for, what things don’t help them, and ultimately help you to curate new content that will answer their questions and guide them to your brand. 

Discover the consumer journey

Brands are all trying to build connections that will last with their customers – gain loyalty, be well-known, differentiate and excel above their competitors, and remain relevant overtime. Understanding the consumer journey and all the touchpoints along the way, including when they are not happy and consider switching is vital to showing up strong and maintaining and growing a brand. 

There are a million different ways to approach a consumer journey study. But why not start with actual observed behaviors? How consumers are interacting with your category, your brand, your competitors. How does that differ based on if that person is new to the category or has been with your brand for years? Or what else are consumers doing when engaging with your brand – let’s say they are in one of your stores, but they are on their phone. What are they doing? Checking your site online? Or a competitor’s site? Or looking for help on what product(s) to purchase?

Digital behavioral data can answer all these questions and more, connecting the dots on the activities and touchpoints that are occurring with your brand and key competitors. Paired with a survey or qualitative interview, gaps in the journey such as how they are feeling, what they are trying to accomplish and why they are doing the things they are doing can be layered in. 

Keep a pulse on new trends or foresights

Brands are always trying to stay on-top of what new trends or fads are happening and incorporate them into their innovation pipelines. But knowing what is going to be the next ‘hot’ trend is hard to uncover. Looking within your own category and keeping a close watch on your competitors is a must. But some also look to other categories for early-adopters and take cues from them. 

Leveraging behavioral data to watch for upticks in keywords can provide an indication of when the market is really starting to adopt or move on something. Whether it’s the next new cool ingredient, or package sustainability, or something else. Monitoring digital behavioral trends can ensure you’re staying in the forefront of foresights.  

Guide Search Term Optimization

How many times do you start with search versus going directly to a site. A search engine is an easy way to navigate to a website without remembering the exact site you’re looking for. But what if you think you know where you want to go but the results show something different… Did you switch your original plan? 

All brands, whether small or large, want to appear in consumers’ search results. Even if a consumer has already made up their mind where they want to go, if they use the search and see a brand or a product, the opportunity is there for the consumer to change their mind and click on anything that appears. Behavioral data can easily uncover traffic-driving keywords for brands and their key competitors. 

How to be successful with behavioral data:

TIP #1: Know not all behavioral data sets are the same… make sure you carefully evaluate and choose the right behavioral data set for your needs. 

Behavioral data sets are going to vary based on the device they are capturing data on, the size of the data, and what types of data they collect. It’s important to understand your objectives and your target audience before choosing a data set. 

In 2021, 85% of Americans owned a Smartphone and 15% of Americans were smartphone only Internet users.² And globally in 2020, Internet traffic was reported as 51% mobile, 47% desktop, and 3% tablets. So, consumers are online using both desktop and mobile devices. ³ And what they are doing on each device may differ. Behavioral panels may specialize in just one device, or they may have both, and the best ones have a portion of panelists that are sharing their data on both mobile and desktop. 

With traditional market research, whether qualitative or quantitative, one may be ok with a smaller sample size – say 50 to 300. However, with behavioral data, there are so many different paths and touchpoints that we recommend a larger, more robust sample in most cases. If you’re looking for a niche audience and can get 200-400 people who have exhibited a specific behavior, that is enough to understand who those people are and what they are doing. However, if you’re looking for specific paths people take or want to drill down into various regional or other types of data cuts, a behavioral sample in the thousands is going to get you better insights and ensure you are not missing anything. 

You should also ask about panel representativeness. Now, representation can mean a lot of different things, so make sure you define that before you ask. Do you want a representative within a specific market? Do you want census representativeness? Do you care? Make sure you know the answers to these questions. If you do want representativeness, know all panels skew in some ways. Hence, the question is not ‘Is your panel representative,’ but rather, it should be ‘Can I acquire a representative sample that is analyzable from your panel’. 

The last but one of the most important pieces of information you need to understand is what type of data the behavioral data will provide you with. Some panels track only a certain number of websites, which can be useful to compare brands (if they have your desired sites) but lack the full journey of what consumers are doing online. Others track everything someone is doing online but may be limited to just browser-level data and what is captured in the url. Others may collect the background calls on a page to allow you to see things a consumer is interacting with on a page that are not reflected in a changing url – these typically include pop-ups on a page whether that be videos, advertisements, or even check-out carts. 

In addition to browser-level data, some behavioral panels will have additional behavioral data on consumers. These variables could include geo-location data – venue locations where consumers are visiting, app usage data – what apps consumers have and how often they are using them; in-app data – what people are doing in the app, products viewing, products purchasing, videos watched, etc. 

TIP #2: Ensure you have the right skill set, expertise, and tools to analyze the data. 

One of the biggest hurdles to date with embracing behavioral data has been the ability to wrangle, analyze it, and ultimately make sense of it. 

If you’re looking to purchase access to the raw data, you’re going to need to be able to analyze and work with it in raw form. From acquisition to cleaning and structuring to analysis. Excel or even SPSS is not going to cut it. You will need to be able to work with S3 buckets, data lakes or data warehouses, and parquet or JSON files. All these things will require the right tools and employees. 

You can also hire a company that is an expert in behavioral data and can structure and analyze the data for you. Providing a more manageable output to work with. 

Either way, having a strong analytical plan of what you want to do with the data is important. An analytical-minded individual who is familiar with the data dictionary and the results of the data is extremely important. It’s often hard for someone who has worked mainly with just survey data to grasp the possibilities behavioral data can provide and build out an analytical plan. 

Data platforms to analyze data are extremely helpful. But be careful because many of the data visualization platforms out there today are great for making charts and graphs to show insights. Still, they take a lot of wrangling of the data before importing as many of them are not built to efficiently analyze and visualize time-series data. There are, however, some great intelligence platforms that specialize in time-series data that can cut the data engineering and data wrangling time from weeks to hours. 

TIP #3: Don’t expect to analyze behavioral data like survey data – it’s different. 

With survey data, you have a finite list of options you’re asking consumers to choose from, or you may ask in open-end form and code them, but still, it typically is a manageable number of codes. With behavioral data you might have millions of different search terms, websites, apps, venues, etc. Manual coding could take days, maybe even weeks. Often pre-existing coding is scrapped, and AI models are built to assist with the coding, training the models to get better and better over time. With behavioral data, we must be ok with some gaps in the data, whether that be missing some of the websites or apps that are not as prevalent in consumers’ behavior or some false positives or false negatives appearing in the AI models. Over time, those working with behavioral data will train and improve these models, and they will get better. 

Another difference between survey data and behavioral data is the ability to exhaust your analysis. Behavioral data is so vast, and the possibilities are limitless – which makes it fun. But you also must have a plan of what you want to explore so you’re getting insights before the data becomes outdated and irrelevant to the decisions you want to make. My recommendation is to focus and hone in on answering specific business questions but leave some room to explore and expand beyond there when you find something interesting in the data.

TIP #4: Combine rich behavioral data with other consumer insights to have a holistic view of the consumer – but avoid redundancies. 

Behavioral data is great and provides so much rich detail on exactly what consumers are doing. And we believe it should be the first place any brand researcher, brand marketer, or brand strategist begins. But oftentimes, more questions will arise or more data will be necessary. 

First, consider additional behavioral data sources that can be appended to the behavioral data through a PII match. These include in-store receipt data, VIN numbers, magazine subscriptions, or even a third-party data set like Acxiom, Experian, or Merkle to help facilitate the creation of look-a-like models. This additional layer of behavioral data can provide more insight into how these individuals are behaving, where you can reach them, and how to resonate best with them. 

Secondly, consider a traditional survey or qualitative research to talk to these individuals and marry the behavioral data with rich attitudinal or explanatory data to explain the whys behind the data. Or if there are behaviors that might be occurring that are not captured in a behavioral data set, for example, signing up in-person or over the phone, those are behaviors that we should ask people about. But make sure you’re asking high-level questions and not trying to get into details consumers can’t recall. 

What we shouldn’t do is ask about things that we already know through the observed behavioral data. When asking consumers to recall how much they spent, the order they did something, or what keyword they searched for, we already know they are going to have a hard time getting us the truthful details. So rather, leverage behavioral data that already exists, and marry it with additional data to provide you with a holistic view of the consumer. 

Conclusion:

We live in a digital world, which presents so many opportunities for researchers and marketing folks. However, it can also be scary to know how to leverage all the rich data that exists. And brands don’t want to waste their precious resources – whether that be time or money experimenting with things that won’t help them. But digital behavioral data is not new anymore; it’s just not being leveraged to its full potential. When it is used, it’s changing the way brands do things. It’s improving their understanding of the consumer, it’s helping to grow brand recognition, brand consideration, and brand loyalty.  

BEHAVIORAL DATA IS HERE. IT’S NOW. AND WE SHOULD ALL BE EMBRACING IT. 

Resources

1. The 10 Biggest Market Research Fails of All Time (trendsource.com)

2. Demographics of Mobile Device Ownership and Adoption in the United States | Pew Research Center

3. Desktop and Mobile Internet Usage Statistics – 2022 – High Speed Internet

The post <h1>A Behavioral-First Mindset</h1> <h3>Changing the way brands understand consumers</h3> appeared first on The Experience Journal by QuestionPro.

Introduction

There are several challenges and opportunities when it comes to capturing the true, authentic voice of the customer. Customer Voice is nothing new. It has been around for years, but it’s the way it is approached, the way it is measured, and the way it is shared that is changing. Fortunately, I come from a sales and customer service perspective in the B2C world where the customers are always right, and you NEED to listen, or their business will be taken elsewhere.

That is just as true in the B2B world, but there is more word of mouth and more research that goes into a B2B buying and renewing process than a low ticket B2C ticket item. So it’s even more critical to ensure you not only capture customer voice but share it out there, do not just let the word-of-mouth marketing be the source; contribute to the narrative. Let’s start with the pitfalls so we can all agree on the challenges and then dive into tips to put into practice.

Pitfalls to avoid

Hands down, there is always bias: One of the most significant challenges in capturing the authentic voice of the customer is bias. This can include how questions are phrased or the sample selection that is pulled from the primary source of content. Take the middle person out, ask the customers to self-record, self respond without being asked or begged. Serve it up as an opportunity.

“The customer’s perception is your reality.“

Kate Zabriskie

In addition, another challenge is around lack of participation. Again this comes back to DO NOT beg for favors, but if you had positioned your customers upfront when they partnered with you on what opportunities they would like to be survived up, there are no surprises; they participate enthusiastically.  

“Repeat business or behavior can be bribed. Loyalty has to be earned.” – Janet Robinson

Sometimes, customers may be unwilling or unable to provide feedback, which can make it challenging to capture their authentic voice and find ways they can still participate – even if it’s anonymously. To squash the busy aspect, meet your customers where they are, ask, and partner with the internal champion at your organization that is CLOSEST to the customer to make the ask – do not put another layer of a person into the mix overcomplicating the ask. Huge success if you keep it simple, have the right person ask, and offer up the opportunity the customer is interested in engaging in. 

And there is always the chance of incorrect or Incomplete data. Your CRM is not current with how often the customer has participated in acts of advocacy or shared their voice. Track now, and make it visible to all to avoid burnout of your customers. Then when you need them, they will not be there; and a step further, even when customers provide feedback, it may not be enough to have the probing question, get in and ask the good, the bad, and the ugly. All types of feedback are valuable.

Another pitfall to avoid is an unrepresentative sample. You want to have a variety of customer voice assets from all types of customers—different use cases, different industries, different customer sizes, etc. If the sample of customers providing feedback does not represent the overall customer population, the input may not accurately reach the audience in the way you want. You will miss the mark for specific business segments and or personas. Another layer also is if the sample consists mainly of highly satisfied or highly dissatisfied customers, the feedback may not be representative of the entire customer base. You want to spotlight the satisfied and show the dissatisfied and what you did to turn it around. That’s authenticity! 

You are getting too much Customer Voice, Feedback, and Data. A good problem to have, right? Well, if you do not have the proper processes in place for how the voice of the customer gets put to work, it will fail. You will fail internally and externally with your customers. How many times have you personally shared feedback in any, written, audio, or video, and it went into the black hole of the unknown? No follow-up UNLESS you were outraged, and rarely any acknowledgment. In the competing world, especially in the B2B SaaS world, the time of your customers value it. Value their time and value all the feedback.

If you cannot follow up timely, do not cast such a wide net of customer voice capture. If you have no campaigns for follow-up in social, your website, your community, or your newsletter, build one, make a content calendar, and plug-in customer voice assets regularly. On top of that, sorting through large amounts of feedback can be time-consuming and make it challenging to identify the most critical issues. Work smarter, not harder!! 

Addressing these challenges requires careful planning and execution of customer voice programs, including ensuring that the program has been thoroughly researched, includes customer input, and, most importantly, executive leadership buy-in. It’s also essential to remain open-minded and flexible and to continuously refine the customer program to improve its effectiveness in capturing the authentic voice of the customer. It is not a set it and forgets it. And most definitely not. If you build it, they will come. It takes work, but with the proper planning and rollout, researching, and TALKING to your customer base, you will be set up for success.

Tips to put into practice

So how can businesses leverage the power of the authentic customer voice to drive revenue? Here are some tips:

Use customer voice to help drive product development: Collecting customer feedback on their needs and pain points can help businesses develop better products and services that meet their customers’ needs. You need to be constantly reinventing and always new—and not just new customers for existing customers too. You want to be sure they renew year after year, and they do not go looking at the new shiny thing. So not only showing and collaborating but listening to what your customers want can lead to increased revenue. 

Use customer feedback to improve customer experience. You can do this by listening to customer feedback; businesses can identify areas where they need to improve their customer experience. Do onboarding interviews and collect customer voices at various stages in the customer lifecycle. It’s not just one-and-done for customer voice. It’s ongoing.

Use customer testimonials to build trust. But not the drone shots of the customer headquarters and cheesy elevator music. You want the true authentic voice of your customers in testimonials. Including customer testimonials on your website and in marketing materials can help build trust with potential customers, build FOMO in the market, and create a peer to peer engagement as other customers see who else is using your solution. We know that people are more likely to trust recommendations from their peers than they are to trust advertising or a sales rep. Give the people what they want! True authentic customer voice. No more happy quotes or logo banners on your site. Let’s know the users, the champions, the ones realizing the value and securing their job with the successes of your product solution or services. 

“Make the customer the hero of your story.”

Ann Handley.

What we all talk about, but let’s be honest, do not do the greatest on as for the longest time, the mentality at SaaS-based companies especially is new deals are king. But upselling and cross-selling are Queen, and it’s here to help everyone.   For example, if a customer uses one of your products, use the power of the customer’s voice to share a success story another customer had with that exact use case. Introduce them to that customer so the real, unscripted, authentic chats can happen. 

True authentic customer voice

By leveraging the power of the authentic customer voice, businesses can improve their products and services, improve customer service, build trust, improve the customer experience, and identify opportunities for upselling and cross-selling. Individuals tasked with owning, managing, and reporting on customer voice must carefully plan and execute customer voice programs, remain open-minded and flexible, and continuously refine their approach to ensure they capture the most valuable insights from their customers. 

Conclusion

In conclusion, capturing the authentic voice of the customer is critical in driving revenue for any business. It is essential to listen to what customers say, whether positive or negative and take action based on their feedback. 

“Loyal customers, they don’t just come back, they don’t simply recommend you, they insist that their friends do business with you.”

Chip Bell

Now how are you practicing true authentic customer voice?

The post <h1>Using the Power of Customer Voice to drive revenue:</h1> <h3>Traditional Customer Voice vs the True Authentic Voice of your Customers</h3> appeared first on The Experience Journal by QuestionPro.

Introduction

There have been many interactions about the promise of how generative AI and machine learning will revolutionize the way we conduct business. There is always a promise of what could be the next big thing on the scale of the “internet”. The market research industry, too, has grown by leaps and bounds. We are moving towards technology-reliant insights collection and management that scratches beneath the surface and showcases trends faster. Conversational AI has set us on the path of Research 3.0.

What is Research 3.0? In the words of Ray Poynter and NewMR, a QuestionPro partner, Research 3.0 is the link between things like chatbots, video, extended media, and observational research with non-experts.

The article discusses the use of the AI language model ChatGPT in surveys and the potential impact it may have on data quality and the research industry. Jamin, one of the commentators, believes that ChatGPT can revolutionize the way customer insights are gathered and analyzed by enabling natural conversation and analyzing open-ended questions with more sophistication. 

The other panelists of this paper, Jamin Brazil and Vivek Bhaskaran, discuss how technology like ChatGPT can help with qualitative research, allowing for more efficient and accurate data processing and the opportunity to decouple from the survey in some cases. They also mention potential concerns around the impact of such technology on jobs traditionally part of the back-end field operations and research industry. 

QuestionPro has implemented a system to detect when ChatGPT is used in open-ended responses and flag them for review. The text also mentions a separate project run by QuestionPro that helps non-researchers design surveys using ChatGPT to generate a list of questions around a particular topic.

What is ChatGPT?

ChatGPT is a generative AI engine with a chat interface where you can ask any questions, and it’ll come back with intelligent answers. Assistant-like features are among its functionalities; you can ask it to do things, create content and summarize it. 

The platform works on the model of conservational AI and constantly trains itself to adapt and learn the basis of the persona you give to it. It can continuously evolve and learn from various parameters to be highly adaptive based on the user’s prompts.

It can be used to summarize text, answer questions, make iterative judgments, hypothesize and synthesize text in data, troubleshoot, and more. The possibilities are limitless!

ChatGPT or GPT-3 in the form currently released by OpenAI 175 billion parameters. However, GPT-4, which will soon replace the earlier model, will have 100 trillion parameters. Let that sink in for a second. The processing that is currently mind-boggling is going to pale in comparison when the new version launches.

Nothing defines ChatGPT better than asking itself, though; this is what it says about itself – ChatGPT is an extensive language model developed by OpenAI that is trained on a massive dataset of conversational text. It is designed to generate human-like text in response to a given prompt. It can be used for various natural language processing tasks such as language translation, question answering, and text summarization.

ChatGPT will significantly impact how we gather and analyze consumer data in 2023 more than the internet did from 1996 to 2006.  

The impact of ChatGPT in data gathering and data analysis in 2023

You think about the advantage that the internet provides consumer insights professionals predominantly around speed. You could argue price, maybe, but I think the most significant benefit has been speed and accessibility. Before, we had to intercept people in malls to get them to complete a survey. Now we intercept them in their inboxes to get the same thing done, and certainly, in the period you referenced in the early 2000s. Online surveys were copied and pasted from phone inner phone interviews or model intercepts, so there wasn’t anything unique about it except for the format, the place it was being done, you fast forward to, let’s say, yesterday like pre-ChatGPT. You look at our industry; we are dealing with similar problems that we dealt with 20 or 30 years ago.  

Unstructured data is one of the most significant wastes in consumer insights. 

In the past, it has happened that brands conducting a customer sat survey would get about half a billion completes a year, so a lot of completes. And if they are majorly open-ended questions, that’s a lot of data to churn. Frequently, such brands would randomly select a segment of about 20 responses, hand-code those and say these are relatively representative of the sentiment on the subject in research. This model wasn’t foolproof. 

If you start decoupling the framework, you think about the rationale of the actual survey, and its instrument is quite literally just a conversation at a scale. 

Nobody speaks in Likert Scales; we don’t talk about; a five-point scale around how much you like your coffee. Instead, we have a conversation. This technology employs the same logic of enabling conversations to surface feelings and vectors and generate a highlight reel for us that is very actionable.

Research at Scale

The internet enabled quantitative research due to the survey angle, but qualitative research has primarily been labor intensive. The only reason qualitative research struggled in comparison is that it lacked the back-end technology and the ability to analyze at scale. ChatGPT is changing that paradigm for research. 

GreenBook Research Industry Trends (GRIT) Report has been working with a text analytics provider called Yabble, which has a text analytics component that you know is relatively standard to get to themes and clustering. Leonard Murphy at GreenBookbut mentioned that when they used ChatGPT, he and the team were blown away at how the technology could interrogate the data to ask for a summary. GPT-3 wrote an excellent overview, even if it didn’t cover everything needed. 

In a podcast that Lenny did with Simon Chadwick, they used the tool to summarize the discussion. It wasn’t full of wonderful flowy language, but it was accurate, summarized the salient points, and communicated skillfully and professionally. It did an excellent job of citing examples from the open-ended discussion and filling in the blanks. There was an ability to decouple from the survey in a few instances, but that was not a deal-breaker. 

We have an opportunity; if we combine ChatGPT with other an existing technology stack that we’ve seen in use, like text analysts that get into emotional effect right, not just sentiment but actually to try and understand the emotion and the states of mind that are occurring within that textual conversation, voice analytics all of those things, there is a real opportunity to unlock immense value in discussions whether it’s via text or voice.

Big organizations that do a lot of insights collection and management can tap into this and deploy it at scale. Clients working with several models on the Gen 2 side are working with large-scale research buyers on the future of their insights organizations and developing the ability to unlock qualitative insights at the scale of unstructured data regardless of the form factor. It doesn’t matter whether it’s video or text, or voice; this is a huge priority for them. They want to be able to deploy this because they see the value of understanding people at a deeper level than what a survey can deliver, so it’s exciting times. 

It’s also scary; let’s be honest, this could replace researchers from a process standpoint. I don’t think it’s anywhere close to being able to replace the thinking and the overall analysis. Still, it can streamline many jobs for people who have traditionally been part of the back-end field operations and research industry.

Summarization of insights

Another critical use case of ChatGPT is the ability to summarize information. We spoke about this briefly in the past, but it is one of the most significant use cases of the generative AI model. With a little bit of coercing and assigning a persona, you can get to a level of surfacing important information. 

For example, from a productivity perspective, when you want to summarize something, you have to read an entire blog and then summarize it or read a whole set of reports and summarize it, which is not super intellectual. With, ChatGPT, you can summarize a complete transcript or an entire blog in five points if that’s what you need, which is a true game changer.

Many people in technology and software development believe we no longer need software developers because AI will write software, which is entirely untrue. In the practical, real world, you cannot mock up what a ScreenFlow should do, and then you got code that, you know, yes, there are code assists right now, same thing like summarization. They’re called assist tools. Having said that, if you write the first three words, it’ll write out the entire component. That’s how I think this AI will evolve around an assistant type of model; when you’re trying to do something, it will help you do whatever you’re trying to do at an exponentially faster scale. 

Uses of ChatGPT

The Managing Director of GreenBook shared an interesting story about his children’s school, a specialty stem kind of Charter School in New Jersey; they’re already drawing the line on this type of technology. They say there is a definite benefit of utilizing such technology, but it cannot be used for tests, writing papers, or real deliverables. It is fascinating that its implications would be different from an educational standpoint. How do we put kind of walls around this? And what are the use cases? Because there are a lot of use cases that could be nefarious and detrimental in many aspects of the world, not just within the research space, I think that’s going to be interesting as well.

While we were promoting the special Live with Dan panel discussion episode on the implications of chatGPT, we sent out a few emails to customers, partners, and the general MRX ecosystem, asking them to tune in. Someone wrote back to me, saying, ” Well, this is really, you know, current because we’re facing something like this in their research studies. Other than patterned responses, one thing that stood out was the response for writing excellent complete sentences that were well thought out and, you know, which doesn’t happen all the time. And you know this isn’t human-written. There is a big challenge –  with anything positive, there’s always going to be that negative side too.

The impact of ChatGPT on market research and what QuestionPro is doing about it

While we are speaking about the caveats of ChatGPT and its negative implications, I think there is also a need to talk about how the system impacts market research. 

In our world, for every virus, there’s an antivirus. In market research, survey bots, open ends, and other things fall under the gamut of data quality. And a lot is being done to mitigate the impact of lousy survey data on insights management. 

At QuestionPro, we’ve used ChatGPT to determine whether you’ve used that ChatGPT in our surveys. For our data quality product, we determine if somebody is using ChatGPT to respond to an open-ended response, so we get a pattern matching around it. If you’ve used ChatGPT to respond to an open-ended comment, we will flag that response, so that’s our short-term solution. Currently, that’s a cat-and-mouse game to some extent, but it’s a big deal to have been able to solve that problem natively.

As with every technology, this could lead to whether we found ChatGPT doing an excellent job grading the outcomes on those open-ended questions. And are we dealing with a lot of false positives?

To provide a little more context about how we are using technology to reduce the impact of insights in the path of Research 3.0, the answer lies in the tool itself. When you add a text comment box, we ask how you can answer the same question differently. Think of five different ways of asking the same question, then look at the answers coming out of ChatGPT, and if there is any pattern matching between somebody’s responses stating that they’re copying and pasting from ChatGPT, we flag it. To further prevent it, let’s say we know that these are the possible list of 2 or 3 ways you could answer this on ChatGPT, so when someone who has the survey changes the question and asks the tool that same question, the tool catches that early on.

There is a threshold on what part of the question or an interpretation of the question is asked to the generative AI platform and what probable responses are. If the threshold crosses a specific limit, 70% or 80%, is copied, then the answer gets flagged. We have already built this into the tool and are working with customers to increase the threshold.  

The first part is to build the system to do the catches and start the monitoring. Right now, it’s too early to say how many of these are getting caught since we need a large sample size, but we’ve moved quickly to get this natively ingrained into the system. So in around 30 days, we will share the level of accuracy and what level of importance we are catching right now. In a month or so, we can confidently say, “Well, we’re catching, you know, 80% of the stuff. 

However, this is bad or good, depending on which side of the table you’re sitting on to know the impact of ChatGPT on market research. 

Now the definite good side is a separate project we are running, which helps people design surveys to get to highly impactful surveys as quickly as possible. Not everyone who uses QuestionPro is a researcher. Due to the nature of the tool, it’s easy even for novices and non-researchers to use it to gather the insights they need. We sell to product managers; we sell to entrepreneurs who have an idea to start a company and want to do some research about this new product they want to build. These people who don’t have large budgets need quick turnaround time responses and agile research and will be significantly impacted by this tech. It is easy to plug into that ChatGPT to get a list of questions about the product that you could ask around usability, flexibility or on, price testing, and more, and that comes back. Then we put that into the survey so we can automatically create a survey in as low as 22 seconds about a particular field. As well as you know, it’ll help them, so you know it’s people having specific use cases on product innovation, pricing, or even due diligence.

However, the proliferation of technology in market research hasn’t made people creative thinkers. The MRX industry is so process-oriented that researchers are still doing more of the same things, even with an expansion of technology. 

What is the impact of digitization and ChatGPT on researchers?

Digitization in research and future research depends on how effectively technology aids market research. Each researcher has a different viewpoint and methodology for approaching insights. With time and the use of technology, expertise expands. With people not leveraging technology, there is a lot of potential disruption in the job market for people not growing their expertise.

ChatGPT may not take you the whole way. Still, suppose it saves considerable time to create a survey or analyze responses. In that case, it can free up time to work on more strategic initiatives, including analysis, posturing, data management, and more. 

With technology, you have to be careful that it doesn’t impede you. You have to consider it a way to kill repetitive tasks and improve operational efficacy. But relying solely on it may be counter-productive to your end goal.

This can be best depicted with an example. Content production can be easily automated even before but now so much easier with ChatGPT. It can take on a user persona and make content creation a breeze. However, the flip side of this discussion: can Google recognize that this is ChatGPT reproducing content and, therefore, your SEO strategy won’t work? Nobody knows yet how the algorithm will work. Let’s say you produce a fantastic blog, and Google says oh, this is ChatGPT; therefore, we will downgrade it. At the end of the day, this strategy doesn’t help. 

Making technology do the work for you, but smartly, is critical. 

Another example of driving home is how researchers can think of ChatGPT in day-to-day tasks. It’s around honing your skill around asking the right question and being able to iterate on those questions. When you ask the right questions and ask for the correct answers, you can get ChatGPT to summarize the key points, surface important information, and even do tasks like rank order from most important to least important, etc. This way, you can have your functional research assistant surface rich insights and create a more compelling story, but the process takes minutes. 

Does generative AI impact search-based taxonomy for knowledge management platforms?

Another distinct component of Research 3.0 and the use of technology is knowledge management platforms and insights repositories. With the use of ChatGPT, does it disrupt this model? And should you be worried as a knowledge management platform CEO?

An excellent way to look at this problem statement is that knowledge management (KM) platforms are taxonomies. With ChatGPT, you curate and summarize information reasonably quickly. You can structure the data and put it in the generative AI, and it figures out various ways to slice up data and dress it up any way you like. The implications for every research organization every buyer and organization says that they waste so much previous research that it just goes into a folder and sits there. Companies like Lucy AI have been trying to make a far more efficient process utilizing AI so that you can go through all of your historical data and find railing the points. We’re there now with these technologies allowing you to do just that. 

I can ask in the last year what males aged 18 to 24 think about “topic A,” and it will come up with a point of view based on the data reservoir, and that’s pretty remarkable. The infrastructure is such that you don’t know the technology can functionally be bolted on. Now all those projects you’ve got are the asset moving forward instead of having to have them all scripted and constructed. All of a sudden, you don’t have to worry about creating commonality across projects; you can generate discoverability and accessibility, and those are the two things necessary to implement this technology inside of your research. That’s been the Holy Grail that nobody has achieved, and we are on the precipice of being able to accomplish that.

In larger organizations, a lot of time is spent asking the same questions again for different reasons. Now, there is the ability to be agile and iterative across the entire organization and only explore new dimensions of insights that don’t have an answer. Now it also allows the ability to move past just trying to mine past information and start exploring new things that we haven’t gotten to because we’ve just been stuck in data structure and taxonomy.

The knowledge reservoir kind of model where multiple technologies you can query across numerous platforms cross, and so that’s what will be, is a definite win for the researchers as it becomes the common lexicon slash kind of storage. Cross-functional teams can surface insights across languages, studies, methodologies, and more in a commonality. 

If you can put all of this tribal knowledge into one AI repository and then query that AI repository, then it’s a huge win. With data tangibility, you can pump data into it with text which is the standard data format. This innovation and a query interface that is more a chat but a human conversational interface make this a game changer. 

Knowledge management platforms will be fine until AI repositories across cross-functional tools become a reality. They, however, must evolve to keep up with generative AI and machine learning. 

Negative implications of ChatGPT

We’ve spoken in detail about what ChatGPT can do. However, some negative implications are essential to note. 

One of the most critical components of this tool is plagiarism. The temptation is always to put a query in the system and paste the response. Algorithms are, in a lot of ways, responsible for how we view the world and how we view data. So those biases and prejudices will naturally find their way into these systems as well. So there is an inherent danger to paste without applying the like research expertise on top to try to eliminate as much of that as possible when expressing a population’s point of view.

To take on an exploratory research standpoint on any given topic, we’re seeing immense fragmentation in online channels. Still, a massive push of expert-led niche content is through platforms like Substack. There’s so much content on topics that needs to be curated and a lot of great stuff out there, but there is also this fragmentation. There’s no central point of truth anymore, and that can create a lot of opportunity and confusion. Depending on the persona that a user takes defines the answer, so in that sense, do generative AI and machine learning have ethics? 

Another drawback of ChatGPT is it can really help replace customer service or human beings? A few years ago, chatbots indirectly promised to make customer service reps obsolete. However, recent studies have shown that 97 out of 100 people still want to take to an actual human being. Can you ask your bank a question or if you’re trying to reschedule your flight or ask questions that need emotions? ChatGPT may not have all the answers! Yes, you can request to refine the answer, but the question framing is immaterial if the answer doesn’t exist. 

In its nascent stage, another negative implication of ChatGPT in market research is around technical know-how and costs. Karine Pepin, Vice President / 2CV Research has been extensively using GPT-3 to work out different models to work coding open-ended survey responses. Some of the notable things she encountered are mentioned below in her own words:

• To code open-ended responses, since ChatGPT cannot scour data through Google Sheets, using the API model is the only way forward. Even though the API is relatively simple to use, integrating it into tools still requires some technical assembly, and it is not as straightforward as one might expect. And since the GPT-3 trains on the generally available internet knowledge, it cannot understand tribal knowledge related to the survey. As a result, additional survey-context-sensitive information, such as how to code responses or codes to categorize the reactions (i.e. a coding frame), must be included in every API prompt call. Depending on the underlying model used, the cost differs. However, the cost and time factors are very high at a nascent stage.
• It’s possible to create custom versions of GPT models using sample prompts. It is especially useful for classification exercises like coding, as it allows the AI to understand the context of the task through coding examples (i.e. code frame). The quality of the model, however, is still purely subjective. 
• ChatGPT can help you identify leading survey questions. However, it is inefficient to input prompts one-by-one since GPT-3 is not scalable. OpenAI’s potential is tied to the underlying GPT-3 engine, which requires some technical knowledge to program and train to improve accuracy in a variety of scenarios.

How far back does knowledge need to go to be effective? 

In our panel discussion on LiveWithDan, recently, a user was curious that since the knowledge of ChatGPT or GPT-3 doesn’t go beyond 2021, how does that affect the quality of output? And that brings up an interesting point about how market research used to be highly longitudinal in nature but has now evolved post the pandemic to ad-hoc market research. Comparability across data sets has completely changed because the world has changed how we know it! Going from traditional 70% longitudinal research to high-frequency research and discovery, A lot of the research is also DIY to the end that the frameworks are much more in the context of primary market research being gathered today and analyzing that data in the context of that data set as opposed to pulling from historical points of view.

It does bring up an interesting opportunity for innovation if you combine this technology with other predictive technologies. There’s a lot of opportunity to take the historical information and shift it on its head to be more predictive. Brands and researchers want to know what happens next, and if there’s a way to combine past data with that, it’s great.

Regression analysis is a big part of insights management in 2023, where we take stated purchase intent and compare it to similar purchase intent or historical purchase to like products. Then that gives us the weighted, you know, probability of that outcome.

Is ChatGPT a threat to traditional market research or a superpower?

A pertinent question that we have seen murmurs about in the past few days is with ChatGPT, does this make end users and clients self-sufficient and do more themselves or is this a superpower or a mix of both? The short answer is, it definitely is a superpower and it’s path-breaking. Not on the level of the internet but it’s certainly up there. It is definitely an efficiency accelerator and it improves efficiency by an exponential amount.

There are limitless possibilities to leveraging ChatGPT in market research. It is just going to need the community to come together and brainstorm different ideas to make this work for the industry at large. 

Does generative AI impact panel and research quality?

There is a case to be made for AI bot panels and the general integrity of traditional panels being threatened by such technologies. Traditional panel supplies do 30% of most research. There is already an issue in dealing with fraudulent responses, bot farms, and other data quality issues in insights management. 

Research needs a paradigm shift to move away from purely transactional surveys to more collaborative and conversational forms. Engaging with respondents offers a better experience that’s holistic, iterative and natural and that will go far towards addressing these types of issues.

Conclusion

ChatGPT is fast, easy to use, and highly intuitive in its current form. But it cannot replace researchers and operate in a silo to complete modern tasks like creating a survey to analyze and synthesize insights. There are specific use cases for both qualitative and quantitative market research with a generative AI tool like ChatGPT but the tool is too native, and building models is complicated and not as cost-effective either. This technology definitely puts us well into the path of Research 3.0, but it isn’t perfect yet. Maybe GPT-4 takes us to the next level? All we can currently do is wait and watch. 

References

• https://www.linkedin.com/posts/karine-pepin-1802625_chatgpt-mrx-survey-activity-7018939579596476416-W1LX?utm_source=share&utm_medium=member_desktop
• https://www.linkedin.com/posts/karine-pepin-1802625_chatgpt-mrx-activity-7021113906274193408-yDHF?utm_source=share&utm_medium=member_desktop
• https://www.linkedin.com/posts/karine-pepin-1802625_mrx-chatgpt-survey-activity-7021551433720242176-xczS?utm_source=share&utm_medium=member_desktop
• https://www.linkedin.com/posts/adrian-stanica-56a734_market-research-done-in-minutes-when-i-ugcPost-7017350755930046464-9S6I?utm_source=share&utm_medium=member_ios
• https://newmr.org/blog/chatgpt-is-ushering-in-research-3-but-what-is-research-3/
• https://www.greenbook.org/mr/market-research-technology/chatgpt-and-implications-for-market-research/
• http://www.researchnewslive.com.au/2023/01/16/chatgpt-ai-a-market-researchers-best-friend/

The post <h1>The good, bad & ugly of Generative AI & ChatGPT in the future of insights!</h1> <h3>Generative AI and ChatGPT are moving us toward Research 3.0. But what does that mean for you?</h3> appeared first on The Experience Journal by QuestionPro.

Table of Contents

• Introduction & Executive Summary
• History of CX & VOC Programs
• The Why!
• Root Cause & Systemic Upgrades
• Outer Loop – The Process
• The Intersection
• Conclusion

Introduction & Executive Summary

Identifying and isolating specific customer feedback for follow-up triage and action is familiar, indeed commonplace. Closing the loop with survey respondents who may have expressed dissatisfaction, offered constructive criticism, or experienced friction at a specific touchpoint is considered table stakes in a modern voice of the customer (VoC) program. Such follow-up not only allows an opportunity to course correct and make a bad situation better but also sends a clear message to the respondent. “We’ve listened, considered, and now we are acting on your behalf”, it says, and, in many ways, a straightforward action reaffirms the importance of the feedback shared, making the customer feel valued for their loyalty and direct input given. 

What if you see more significant and systemic themes appear in your customer feedback? What if big and strategic parts of your customer experience must be in sync with your market and customer expectations and go beyond only a few customers or cases? What if solving the root cause most closely linked to this outcry of dissatisfaction went beyond what a single rep could do? What if there was a need to re-engineer a key business process or even a part of your product to truly satisfy, let alone delight? What then? 

We propose that VoC systems connect the Closed Loop systems to an Outer Loop / Systematic process changes/change management system. 

This connection is necessary for systemic and lasting changes to improve the experience at the speed of business and expectation. 

History of CX & VOC Programs

Thinking back to early VoC initiatives, particularly early Net Promoter Score (NPS) programs specifically, those early programs too often fixated on closed-ended feedback – scores, mostly. Even the originator of NPS, Fred Reichheld, theorized we’d only need a single question to assess loyalty, as he wrote in his seminal The Ultimate Question book when he first described NPS to the world. Asking someone, he stated, about the likelihood they’d recommend a brand to a colleague or friend would be the ultimate measure of satisfaction as measured by their advocacy. Indeed, that makes sense, even if slightly misguided.

Knowing your customer rated you as a high scoring “9” or a lower-scoring “3” is directionally exciting and can serve as a good marker for how they feel about their experience. Anyone can see that a 9 is likely a far better and more coveted score than a three and is easily explained and understood. 

Where the notion of an “ultimate question” falls short, however, is that while directionally interesting, a score alone is virtually impossible to act upon and recover from. There could be dozens of reasons a high-scoring promoter loves a brand and another dozen why they might not. The score alone is, at face value, insufficient to direct any meaningful recovery or even more than a surface reaction. 

I’d find it impossible to think of a business story that illustrates this better than to share one from a few years ago. A senior leader at a US-based HCM (Human Capital Management) and business services company with whom I’ve been doing some CX project work called me into his office. He told me, “I just paid a large consulting firm more than 2 million dollars to build out my NPS program, and now I get my scores on a 2x per month scorecard.” He said, “Some months the score goes up. Other months the score goes down,” as he drew in the air, with his finger, what looked like a hospital EKG report with a heartbeat “blips.” Up, down, back up again, back down. 

Here was the punchline. He went on to say, “The months where my score goes up, my boss calls, and I’m a hero. Other months when it goes down, my same boss calls, and I’m a moron.”, he said, grimacing. “I just want to maximize my hero-to-moron ratio.” It was clear that he had no idea what was behind the score he tracked month in and month out, and worse, had no idea of what to do about it, as they followed the Reichheld approach of just pursuing that magic, single question resulting in a score and little else.

Chasing scores was very common in the early days of NPS as it brought executive bragging rights to many, and that’s what people most wanted, it seemed. At least until they could not do much with it other than brag, they started to see how more would be needed. Even Fred Reichheld agreed and revised his original single-question thesis.

The Why!

Reichheld and his Bain colleagues returned to their authoring roots and updated their original writing, now called, The Ultimate Question 2.0, to incorporate more than just the one question and in fact, opened it to a critical and overlooked initially companion question that would unlock the reasons why someone gave the score they did and do this in their own words. The simple power and elegance of a question such as, “tell us why?” It would unleash the true power of NPS and CX more broadly.

The visibility of why someone scored us the way they did proves to even the staunchest naysayer that customer feedback could fuel significant transformation and overall business impact. 

If that old client who could not articulate how to satisfy his customers better and in it, keep his boss on the right side of his day with only a score and nothing else, now could know exactly where to focus his energies and, more so, even consider root cause and which ones would ensure the greatest and most positive impact for all. It was the key that unlocked the true power of great customer experience.

The same team who brought us NPS and, to a large extent, the original methodologies of closing the loop based on understanding the root cause of specific scores and, more so, customer dispositions also envisioned another situation. Early on, they taught us the importance of closing the loop and following up with survey takers who shared either highly negative or positive feedback with appropriate and targeted outreach. Rescuing detractors and amplifying promoters grew in importance and became staples of most VoC initiatives. 

However, it would become clear to many that it was realistically possible to gather feedback and see root causes emerge that were bigger than a series of easily rescued customers or one-off and isolated squeaky wheels. If you heard only a few complaints about your pricing, a small token coupon or discount would satisfy. That could be quickly initiated case-by-case by a single customer service representative as needed. Single customers could be rescued and even delighted by a single recovery action. 

Root Cause & Systemic Upgrades

What if you begin to see larger and more systemic themes appear in your customer feedback? What if big, strategic parts of your customer experience need to be in sync with your market and customer expectations, and they go beyond only a few customers or cases? What if solving the root cause most closely linked to this outcry of dissatisfaction went beyond what a single rep could do, and what if there was a need to re-engineer an essential process or even a part of your business operation, product, or service to satisfy, let alone delight truly? What then?

Reichheld and his colleagues described a systematic approach to vetting and acting on this more strategic feedback as an “outer loop”; in effect, comparing it was what they similarly called an “inner loop,” which described the 1:1 feedback handling and close follow-ups. 

Outer Loop – The Process

The outer loop, as described, is a process of tackling larger-scale, more systemic challenges, suggestions, and opportunities for improvement that go beyond a small segment of customers or problem solvers. Often, the outer loop process can involve a large multifaceted team and if implemented well, can have a lasting and positive impact on many customers. A clear “win-win,” Reichheld wrote. 

Evaluating outer loop feedback for action is complex and to be taken seriously. Efforts to make large-scale and systemic changes to a business and its operation are expensive and impact many. As such, they require input and consideration from diverse collaborators and solution designers. Vetting these ideas in terms of feasibility and impact are crucial underpinnings. These decisions will make the difference between successful change and little more than a money pit of bad ideas.

Outer loop tools have emerged as components of the most advanced VoC and CX platform solutions, closing the gap between feedback solicitation, analytics, and this new collaborative vetting and action management. Some of these systems have taken a crowdsourcing model of idea evaluation with Reddit-style voting for the best ideas and a natural burying of the worst. The best ideas can be prioritized and managed as they mature and take shape based on regular evaluation, consideration, and collaboration. The result has the potential to be transformative and stunning and impact the experience many, if not all of our customers have.

Driving change and not simply problem-solving is fundamental in our customer experience toolbox, and ensuring the changes we choose to pursue are the right ones, at the right time, for the right customers is what the outer loop is truly about and what it enables us to do.

The Intersection

Interestingly, there’s also a natural intersection of both the inner and outer loops. Very often, as we open surveys to feedback, we may see initial traces of feedback that could make us think the input is only a couple of small isolated situations and pursue easy rescues. Only when things emerge to be more systemic and recurrent do we promote them to the outer loop and look at solution options beyond a short-term reactive measure. 

Conclusion

As a final thought, I’ll leave you with this. Simple inner loop feedback handling is very similar to that old carnival game, “Whack a Mole,” where a player has to hit a stuffed mole that pops its head out of one of 9 holes in a wooden playing board with a large mallet. The mole quickly moves from hole to hole and is a genuine reaction time test. Imagine that the mole, in this case, represents your customers sharing feedback.

An inner loop is an effective tool to help satisfy one mole in one hole at one time, but you will forever be firefighting in a reactive mode and unable to pause and consider more scalable solutions for more moles in more holes. By its very nature, the outer loop ensures we are strategic in our problem-solving and innovation and can address all at once if done well and handled thoughtfully.

The power of the pair, inner, and outer loop together is immense and should serve as the foundation of every VoC program from now on. Without a balance of the two, you’ll be forever chasing customers who, like those moles in the game, pop up unexpectedly and move quickly and, without it, risk losing control, let alone the loyalty of your customers served.

The post Connecting the dots – A holistic approach to CX appeared first on The Experience Journal by QuestionPro.

Summary

This article talks about how we can better utilize the data collected at various digital interaction points. It requires a significant amount of time, energy and resources to be invested in the data collection exercise and deriving meaningful insights.

Data collection also comes with three inherent challenges – Scale, silos and bias. This further creates a challenge in turning data into insight. Research repositories are a powerful instrument for helping to solve the data problem. However, they need better integration within the datasets and research methods. Here are the solutions to these 3 challenges.

• Starting from the analytics frees us from allocating research efforts based only on roadmap priorities, pet projects/hunches, and known UX issues.
• Once a problem is identified, start viewing it through layers of filters and segments that eliminate as much of the noise as possible, leaving us with just the most relevant data. 
• By integrating these qualitative and quantitative sides, create a constant iterative loop between observation and investigation.

Table of Contents

• Summary
• Thinking about our relationship with data
• The big 3 problems of experience data
  • Diving in: How 3 big data problems manifest
    • More data, more problems
    • Things you never knew you never knew
    • Oceans apart, day after day
• Better integration leads to better insights
  • The rise (and fall – or transformation?) of research repositories
  • Integrating research from the beginning
    • 1. Solving the problem of bias
    • 2. Solving the problem of quantity
    • 3. Solving the problem of siloing
• Conclusion

Thinking about our relationship with data

Every year, the week spanning from Christmas to New Year’s is the busiest travel period of the year, as people around the world embark to be with family and friends during the holiday season. In America alone, an estimated 112 million people – roughly a third of the total national population – were traveling in this period. And that’s just in one country. 

Just a few weeks later, millions more worldwide were embarking once again on holiday travel for the Lunar New Year. China saw roughly 226 million people traveling in this period, and while it’s the largest nation with major Lunar New Year celebrations (and the accompanying holiday travel), it’s hardly the only one. 

From the data above, it’s safe to say that many of us have been on the road or in the air in the last couple of months; and if you weren’t, there’s a good chance someone else was traveling to visit you instead. That being the case, there’s a shared digital interaction that many of us experienced in that window. Whether you were planning to drive a few hours to see family, or you were looking into flight tickets, or you had relatives visiting from out of town, state, or country… you probably opened your phone’s weather app and checked the forecast. 

Should I drive over in the morning or the afternoon? Friday or Saturday? What are the chances it’s snowing when I do? How likely is it that Aunt Linda’s flight gets delayed due to weather? When will be a good time to take my parents on a walking tour of the neighborhood? 

Whatever questions were floating around in your head, the weather app had an easy answer ready. Meanwhile, though, on the other side of that simple interaction is a meteorologist busy predicting the weather trends for the next several weeks in your region. She’s looking at large swathes of data collected from satellites, radar, surface maps, and more, comparing it to historical patterns, and collating and condensing all of it into simple answers to be displayed to users like you and me on our weather apps. 

All of that meteorological data they’re working with is valuable – but in a practical sense, that immense value only manifests once the meteorologist has analyzed and interpreted it into an accessible weather report. 

If we instead opened our weather apps to find satellite readouts, radar maps, wind speed tables, and decades-spanning historical climatic datasets, 99% of us would be no better prepared to plan that long drive to Grandma’s than we were before. 

* * *

At this point, I could turn this into an article about the value of experts. (Indeed, there is certainly a conversation to be had about the current trends in the data on public trust in experts). However, while that’s definitely part of the picture when it comes to topics of data analysis, my point here is really about the data itself. 

We are in an age now where the amount of data we have access to is frankly staggering. How did we get to this point? Perhaps it started in 2017 with this Economist article calling data the world’s most valuable resource. Maybe it began further back in the 1960s, when we first started using computers for decision-making support systems. Or maybe our obsession with it dates back even to the 19th century, with the advent of time management exercises. Whatever the chain of events, there is so much data around us that we’re practically drowning in it. 

Some might say that the world today runs on data. But I’d say that that’s not exactly the case. Rather, the world runs on the actionable insights gleaned from data. 

Without doing the work to truly understand what our data is telling us, it’s all but useless. 

The big 3 problems of experience data

I have no doubt that everyone reading this has at least a few data collection tools that they regularly use in their work. In the User Experience / Digital Experience space, we rely on many different kinds of data: web analytics, behavioral data, survey responses, usability testing sessions, interview findings, and more. 

It’s also almost certainly not news to you that you have to analyze that data to find the proverbial diamonds in the rough – I know that it’s not a groundbreaking assertion. 

I think it’s important, though, to set up this discussion with the above framework. When we collect any kind of data, it represents a commitment of time, money, and energy to actually get something out of that exercise. So naturally, it’s important that as we engage in data-gathering activities, we have a clear understanding of the inherent problems that can potentially undermine our work, and make it overly time-consuming, inaccurate, or just flat-out wrong. 

So let’s talk about the big problems with the data we collect in the experience research space. In my view, there are 3.

1. Problem of SCALE / QUANTITY: 

The first problem has to do with scale. Many times, the amount of data collected from research activities far outstrips the time in the day that we have to analyze it. What compounds this problem is that within that huge quantity of data, only some of it even contains the answers we’re looking for. By definition, major portions of practically any dataset will consist of noise. That noise doesn’t negate the value of the truly meaningful pieces of data – but it does make it more time-consuming to find those pieces. 

2. Problem of BIAS: 

The second problem is related to bias. Bias can enter the picture at many levels, but one that is often overlooked is the very question of which datasets to collect. It’s easy to forget that this very basic initial decision is itself subject to our biases about what’s important, what we expect to find, and what problems we do or don’t already know about. As useful as a hunch can be, it can also lead us to collect data to investigate a pet project when a more worthwhile issue is flying under the radar. 

3. Problem of SILOS: 

The market for research tools is now filled with a great variety of powerful tools that enable different data collection methods. In many organizations today, each internal team has its own subscriptions to the different tools it has chosen to spend budget on. But with different unique datasets spread between so many different tools used by so many different teams, we’re often missing out on the complete picture. Each of us has only one slice of the whole – like the 6 blind men touching the elephant. 

Diving in: How 3 big data problems manifest

More data, more problems

The problem of quantity is an obvious one, and one you’ve probably faced yourself. Deciding where to dedicate your time as you perform data analysis can make the difference between finding that diamond in the rough, or brushing right past it. Of course, you could always increase the amount of time spent analyzing your data, to ensure that you’ve combed through every corner of it from every angle. 

Unfortunately, that isn’t actually a feasible strategy – we all have other responsibilities to take care of, and spending time on analysis inherently involves a tradeoff for time spent on other things. Instead, the solution must necessarily involve using the same amount of time better, more efficiently. 

Things you never knew you never knew

The question of bias in the datasets we collect, as I’ve argued above, is often overlooked – but I believe it’s an extremely important one. 

Imagine that you’re preparing a usability test to get feedback on your website’s user journey. You write a series of tasks leading the participants from Page A, to Page B, C, then D. You run the test and hear all sorts of opinions from respondents about what could be improved. You spend time analyzing that data, collating the findings, and then updating the designs of your funnel pages based on what your respondents told you. 

But, the A-B-C-D user flow that you tested was based on the ideal-case user journey that your web design intends for users to follow – when in reality, the most common pathway your website visitors follow starts at Page C, and then goes -A-B-A-D-X-Y-Z.

The fact is, it’s often not easy to know what steps actually constitute “our user journey.” It’s often not easy to know where the root of a problem actually lies. If a problem that’s happening deep into the user journey is triggered by an action users can take way back during signup, you may not even know to look there. And then there’s the obvious difficulty of collecting data for a problem you don’t know exists. If you don’t even know it’s there, how can you know the data you should be gathering? 

Oceans apart, day after day

This brings us to the problem of siloing – because sometimes, the answers we need are actually available somewhere – just not to us. Maybe the marketing team has access to data about top entry pages and user pathways that would have helped with that user test setup. Maybe a PM from a different team has a tool that flags rage clicks and errors, but the issues they’re prioritizing don’t overlap with yours, and so no one notices issues that are afflicting your product. 

How many designers would benefit from some of the traffic and behavioral data on Google Analytics, but don’t have access to it? How many have access to it, but just don’t have the advanced Google Analytics knowledge needed to drill down and find the relevant information? After all, it’s not an easy platform to figure out (if it was, there wouldn’t be such a thing as Google Analytics certifications); and if you don’t know your way around, you might never find the data points you need. 

The outcome of all this is that many teams today are making important decisions that affect the organization’s bottom line, based on the incomplete view they’ve gleaned from just their own research projects, tools, and datasets. 

All 3 of these big problems lead back to our overarching problem set out at the beginning: How do we turn data into insight? The overabundance of data fuels inefficiency in finding insights. Bias in the collection process skews the reliability of our insights. And siloed data leaves us to fill in the blanks in our insights with assumptions. 

Better integration leads to better insights

In the above sections, my goal has been to clearly lay out the overlapping challenges we face when relying on data in our work. In this next section, my hope is twofold: to (1) identify in concise terms a trend that is already beginning to take form in our space; and (2) reflect on the best ways forward. 

The trend I’m speaking of is the drive for experience data integration. 

The rise (and fall – or transformation?) of research repositories

One of the clearest reflections of this trend, in my view, is the rise of research repositories as an important tool for UX, product, and marketing teams. Even more specifically, though, this trend has been followed almost immediately by another one you may have noticed: that of established research companies building or acquiring research repositories to supplement their data collection tools. 

We at Trymata, for example, acquired the repository tool Considerly; our competitors at UserZoom acquired EnjoyHQ. Qualdesk was bought up by Miro. QuestionPro, rather than making an acquisition, has built their own research repository in the form of InsightsHub. We can only assume this trend will continue; already the number of independent repository companies has dwindled as one after another follows this very logical outcome. 

Research repositories are a powerful instrument for helping to solve our data problem. By allowing researchers and other data users to aggregate datasets of all kinds and provenances in one place and analyze it in enormous cross-sections, these platforms strengthen the quality of the insights to be gained. Surveys, interviews, user tests, behavioral data, and more can be pulled together and compared, filling in the various gaps that each method by itself leaves. 

Despite these notable benefits, repositories by themselves are not the perfect, heaven-sent solution to our data problems. With a repository, your data is still only united at the end of the collection process. To truly improve our relationship with data, the integration must begin much earlier. 

Integrating research from the beginning

To truly solve our big 3 data problems, we need to look further back in the research process. Functionally, this means using different research methods and data types in a constantly reinforcing cycle, so that they’re never really separate processes at all – just different threads in the same research tapestry. 

We’re already starting to see success with the integrated digital experience research approach we’ve recently undertaken here at Trymata. Late last year, we decided to make a bet on this very idea – the idea that different research methods are better together. So, in addition to our user testing tools, which was until then our sole offering, we unveiled an integrated product analytics suite. Traditionally, many of the data types available from the new suite would be used primarily by marketers, product managers, or even technical teams: for example, web analytics and traffic data like you can find in Google Analytics, and behavioral tagging like rage clicks and error clicks.

We believed, though, that the designers and UX researchers that comprised the majority of our users could benefit greatly from this data, and that, in fact, they should be using it to inform the way they do user research and make design decisions. Not only that, but we believed equally that the marketer and PM type users that would traditionally engage with the product analytics data would benefit just as much from integrating their quantitative datasets and behavioral methods with the in-depth user interviews and feedback sessions available to run through our integrated user testing suite. 

While still in its early stage, this combination of research methods and data types in one place has allowed us – and our customers – to start experimenting with an integrated research process where all of these datasets inform and enhance each other. 

The next 3 sections will address in specific terms how this approach can help to solve each of the 3 big problems with data. 

1. Solving the problem of bias

By starting with web analytics data, we learn about what’s happening in key user flows at a macro level, and objectively evaluate what those flows actually look like, and how they look over time. We can measure performance and set benchmarks for our existing goals using a variety of real usage-based KPIs, and observe improvements over time as we fix known issues. 

Importantly, we can also learn about patterns we didn’t know about. These may be common pathways we didn’t know users were taking, or clusters of frustration indicators like rage clicks or dead clicks. It may be exit rates or bounce rates that are higher than we’d like. It could be differences in behavioral patterns between user segments, or even device or language trends among groups of users. Any of these could be a hint for us to dive deeper into the relevant behavioral data, or to run a usability test to learn more. 

Starting from the analytics frees us from allocating research efforts based only on our roadmap priorities, pet projects/hunches, and known UX issues. We can open our eyes to research opportunities, both known and unknown, and weigh all of them to strategize a more unbiased set of research goals and testing plans. 

What’s different about this approach?

None of these pieces of data are new or revolutionary in and of themselves; you may be checking some of them already in different tools. The key differences here are (1) the intention with which we’re looking at this data, and (2) the application of the knowledge as it relates to other product research and decision-making processes. 

As far as point (1), the idea here is that web analytics should not just be used to track KPIs and measure performance, but to actually constantly assess your research and design priorities. 

If you’re mainly checking your traffic numbers, exit rates, time on page, rage clicks, and other such metrics just when it’s time to report on your KPIs, and not as part of the actual research and discovery process, you’ll almost certainly see different things in the data. Approaching the data with curiosity and open-mindedness, and with the intention of noticing new things, will affect what you get out of that exercise. 

As far as point (2), you can then apply what you notice to your decisions, both about the research projects you choose to run and the design projects you choose to work on. 

If your team currently runs usability tests and other in-depth qualitative studies only when releasing a new feature or evaluating a feature that’s already been slated for design updates on your roadmap, you’re far from alone. Many teams distribute their research efforts in this way. But the fact is, there are many avenues of inquiry that are worthwhile beyond these specific moments. 

If your macro data reveals different pathways that users are following, hidden inefficiencies in your navigational layout, undesirable usage patterns among certain user segments, or dips in the numbers for flows that were thought to be safe – any of these can be followed up on with more thorough and targeted research. Just getting those opportunities on your organizational radar and into the list of possible projects is already a win. 

Lastly, there’s a key difference related to (3) access. As mentioned previously, in many organizations these insights are not distributed to all of the teams that would benefit from them, or considered together in the same decision-making flow. By bringing these data types together in a single research platform, we reframe the relationship between them and, hopefully, the way people conceptualize that. 

2. Solving the problem of quantity

Once a problem is identified within the traffic data (or picked from the list of known issues), we can take that same data and start viewing it through layers of filters and segments that eliminate as much of the noise as possible, leaving us with just the most relevant users/session data. 

Once we’ve reached a scale of data that’s manageable – and on-topic – it becomes much more feasible to start working with behavioral data: for example, examining individual users’ event logs and session replay videos. This kind of analysis is undoubtedly “nitty-gritty,” but it’s also what enables us to analyze and understand issues from a human perspective. 

This is the point where we start reaching a deeper level of insight into our users, and what we can do to improve their experience (and our performance); but it only happens after we’re able to effectively eliminate or ignore huge amounts of the data that’s been collected, in favor of the few handfuls most likely to answer our research question. 

What’s different about this approach?

Chiefly, the difference here is about the transition from the macro-level, quantitative data to the micro-level, qualitative data in the investigative process. 

When those two pieces of the puzzle are separated, housed in different platforms with different terminologies, collection / measurement methods, and base datasets, it’s harder to make the jump. If you’ve ever tried to line up Google Analytics numbers with the corresponding numbers from a referring campaign or other marketing analytics platform, for example, you’ve probably had to deal with significant discrepancies between the data from the two sides. 

With a platform that combines the big-picture analytics of Google Analytics with extensive behavioral data that can be examined at the level of an individual user, there’s essentially a lossless transition. If you want to know why the data shows a drop in throughput, you can open up the exact list of user sessions relevant to that question and watch user sessions, or scan navigational logs and event logs. While many integrations with the usability testing side are yet to come, you can imagine even finding the web analytics for specific test participants and combining those views. 

3. Solving the problem of siloing

At this point, of course, the other thing we can do is to run small-scale, targeted usability tests on the flow we’re investigating. 

We’ve identified the issue(s) we want to explore and explain, and we’ve learned about which users are relevant to the issue and how they’re acting. Depending on what’s come out of the behavioral data, though, we may or may not yet understand why they’re behaving the way they are. 

This is where we jump to the usability testing side of the platform, to set up a scenario and tasks that will replicate the twists and turns of the user journey we’ve observed already, and get really solid qualitative feedback about what’s going on in people’s heads throughout that experience. 

That’s not where the idea of de-siloing ends, though. Once the user tests have been run, the data analyzed, the necessary design changes proposed and executed, we can go right back to our web analytics data and start watching what happens. 

If we optimized our designs to decrease abandonment, do we actually observe a decrease post-release? If we were trying to optimize an inefficient navigational pattern, do we actually observe users following the intended pathway at higher rates? 

By integrating these qualitative and quantitative sides of the equation together, we can create a constant iterative loop between observation and investigation that’s accessible to everyone involved. 

What’s different about this approach?

The big idea here is that iteration is not limited to just the design side of the product sphere. Research too can be constantly optimized, honed, and refocused in tandem with design and engineering. By ensuring that research decisions are an active part of the iterative cycle, we can better ensure that we are optimizing the choice of product questions that are asked, investigated, and solved. 

Again, this is not a revolutionary new discovery. Rather, the point is to encourage a mental framework in which research is not an appendage of roadmaps that are chosen beforehand, but an active driver of the roadmap in itself. By discussing a way of thinking about this process in concrete, well-defined terms, and supporting it with an integrated platform that embodies that way of thinking, I hope to positively influence the way this flow is conceptualized in organizations that still have room to grow in their research processes (which is all of them – ourselves included!)

Conclusion

In summary, a workflow like described here helps to address and eliminate the 3 major problems of data and insights in our industry in multiple, layered ways. 

1. By starting with all of the web data, you can reduce the bias of where you’re looking, and what you’re running user tests on. 
2. Segmenting your product analytics data and collecting small-batch usability testing feedback allows you to focus on a manageable quantity of data to solve a specific problem. 
3. Doing it all together in a platform designed for product and marketing people to use without a high level of expertise makes it easy to get a holistic understanding without leaving out a key piece of the puzzle. 

While our vision of a seamless digital experience research workflow is still in its early stages, we are continuing to develop our platform to support an integrated workflow that’s easy to execute and enables you to reach a higher level of success turning your data into insights. 

The post <h1>The 3 Big Problems with Experience Data – and How to Solve Them</h1> <h3>What we can do to combat problems of scale, silos, and bias</h3> appeared first on The Experience Journal by QuestionPro.

Summary

Delighting a customer can be an uphill task if you don’t constantly innovate. You may ‘satisfy’ customers, but not ‘delight’ them. Innovation at every step of the customer journey – technology, product, process, service, etc. ensures a ‘wow’ factor each time a customer interacts with your brand. Employees form another important touchpoint and so working on employee engagement and motivation can significantly improve customer experience.

This article talks about innovating at various levels, the challenges encountered and how you can gain customer loyalty.

Table of Contents

• Summary
• Introduction
• Technological Innovation
• Product Innovation
• Process Innovation
• Service Innovation
• Innovation Challenges
• Importance of employee engagement
• Design thinking
• Conclusion

Introduction

Delivering the right experience remains the key component of success for companies differentiating their brand and offerings from one another. With an ever-evolving growth and demand pace, Innovation remains the driving force behind a company’s success. Enabling creative ways of engaging customers to deliver unique and memorable experiences requires a shift away from a product focus to a customer-centric focus. How can companies then remain relevant? We look at innovation from two paradigms – the outer view of the organization (listening to customer needs) and the inner view (listening to employees). We can then segregate innovations based on process, product, service and technology. In this article, we will explore the benefits, impacts and barriers faced within each facet of driving innovation. Ultimately the customer journey plays a vital role in understanding what, where and how innovation applies, but for the sake of keeping the reader engaged I won’t go into the customer journey mapping process itself or the importance of customer journey mapping and the tools associated with it.

Technological Innovation

Technology has undoubtedly revolutionized the way companies operate and interact with their customers. On the outer view, there is a widespread use of digital tools with the internet, smartphones and social media, allowing customers to be ever-connected to brands and also driving purchase and interaction decisions. The upside allows companies to personalize experiences with their customers, track customer lifetime values, and understand customer patterns and behaviors, all while keeping them informed.

Let’s take, for example, chatbots and AI-powered virtual assistants that provide support, accessibility and availability to customers at all times. With the inner view, companies can collect, automate and integrate how data flows between platforms providing powerful dashboards with insights that help foster quick decision-making alongside realizing ROI on key initiatives.

Machine learning algorithms and robotic process automation (RPA) allow companies to automate repetitive tasks, which in turn free up resource allocations helping save costs. Another use of emerging technologies lies in the use of virtual reality (VR) and augmented reality (AR) to create exciting and immersive experiences. For example, real-estate companies now provide tools to allow customers a walk-through of properties, or even map out how furniture and artwork can be placed around. Similarly, retail companies provide platforms allowing customers to try out clothes, or shades of cosmetics, virtually to enhance the ease of purchasing online.

Of course, there are downsides to the use of technology, as not everyone can easily maneuver through these platforms. How often have you been frustrated trying to reach your bank with regards to a service only to be greeted by a virtual voice assistant that’s trained only to understand a small subset of the language used across multiple segments and personas of customers by eventually disconnecting your call and not letting you talk to an agent? Well, ChatGPT is a great example of algorithms learned well and with time we can only hope for these services to get trained and be better than the human connect. Or so I think anyway!

Product Innovation

Product Innovation, on the other hand, can be viewed through new product development or enhancement of existing products. By understanding customer needs and preferences, companies can deliver value to their customers in more meaningful ways. Different types of innovation can be used to improve product ease of use and increase accessibility or make the product more intuitive. Design changes to improve user experience, aesthetic appeal, or even grow product attractiveness within specific segments of buyers.

The real impact can be realized by driving long-term customer loyalty when the product is done right, and that’s the reason why companies should focus on listening to customers. Care should be taken when innovation in the new product development space is implemented because this could either enhance cross-sell opportunities or can also come at a price of impacting brand value. For example, a famous luxury brand lost its valuable clientele because it decided to diversify and make the same products (aka handbags) more accessible to a wider range of audience which in turn hurt its image and shareholder value as it lost customers who wanted to associate themselves with exclusivity.

So, while the outer view might dictate a need for a segment of affordability, from an inner view, they should strategize better to rebrand and disassociate. Elongating my point here, there are several high-value luxury brands that only provide a diversified range of products like cosmetics and perfumes that cater to a larger segment of buyers. This strategy allows for the brand association but not the primary products.

Process Innovation

A derivation of total quality management (TQM), the ultimate goal for process innovation remains improving efficiency and productivity while reducing friction in the company, which in turn requires rethinking and redesigning the way work is done within companies. From an outer view, this involves increasing convenience for customer interactions with company services. Simple examples include streamlined logistics services that enable customers to understand exactly when their products will be delivered, including abilities to track the logistics delivery stages or reach the right customer contact agent to clarify doubts, concerns, or issues. Companies then need to have mechanisms to track issues and close the loop.

From the inner view, companies can utilize technology to automate their processes, track process efficiencies and derive information on the productivity of resources. The outerloop is a mechanism that allows for operational efficiencies to be tracked by companies and associate the inner and outer views more efficiently. Order delivery and fulfillment systems are the best examples of improving customer journey by using intuitive and user-friendly interfaces.

Communication remains key, but providing the right messaging that delivers empathy for process-gone-wrong is an important aspect of consideration as well. Further use of data analytics and insights can inform decisions around broken processes that might be unknown to the company. QuestionPro’s NPS+ allows for collecting feedback alongside ideas for improving customer experiences which, at a deeper level, allows companies to realize customer churn. Data can also help analyze customer behaviors and preferences that can help improve the process delivery.

Service Innovation

The most important component of service delivery is creating new and unique ways in which service value is delivered to customers. Personalization in service delivery makes experiences memorable! Equally important is the inclusion of empathy when communicating with customers. In the digital world we live in, digitizing empathy is very important to include the right communication when dealing with issues faced by customers. Service innovation means delivering personalized experiences, for example, offering bespoke packages that are tailored to their preferences and needs, and providing offers and recommendations alongside access to exclusive events and experiences. Service channels can also be expanded to include multiple touchpoints, offering 24/7 support with physical and digital touchpoints spanning across social media and other channels.

Innovation Challenges

While innovation in the customer experience space can have multiple benefits, it comes with potential issues and challenges that need to be addressed. It’s worth noting these down before we tackle how best to address these problems:

1. Lack of resources or costly: It goes without saying it’s expensive to innovate, ideate and incorporate changes within an organization. With the bottom line of adding value to customers, the top line should not suffer the costs that cannot be realized. Dedicated teams, resources and technology come at a cost that can be rewarding if done right.
2. Security, privacy and compliance: Most important to consider in this digital age when using technologies and data analytics is handling how data is captured and stored. Consensus is an important topic in many countries and transparency is key in the use of customer data. Hence security and privacy policies need to stay in compliance with protecting customer data that needs to be customized geographically as well. IT and security systems need to be then robust as any small glitches can hurt the brand, as we’ve seen far too often.
   
3. Integration: Considerable efforts are required to understand the business architecture and integrate it with existing systems remains important for seamless data transition between multiple databases, including CRM, ERP and other business sources. This allows for a unified view of the data but will involve bringing together multiple teams to understand what and why data resides where it does.
4. Siloed departments: Large organizations face difficulties in coordinating efforts for innovation across multiple departments that especially work in silos. Lack of unification and sharing data and information create hindrances to innovating in the right direction and avoiding redundancies.
5. Measuring quality and ROI: If innovation efforts do not have a way to realize their value, how quality is enhanced and maintained and the overall value it creates, then the efforts of innovating are in vain.  Continuous testing and validating these efforts to ensure they are not causing additional hindrances to both the inner and outer view are important.
   
6. Success measurements: Effective means to measure customer experience in the process of innovation are important and companies tend to fail within these aspects. Continuous data collection, analysis and inferences come at additional time and cost to resources.
7. Resistance to change: The biggest parameter to why companies fail to innovate is the resistance to change from employees and the culture to incorporate risk. Usually, a lack of understanding of the real benefits of innovating causes resistance to acting upon these initiatives. Lack of effective communication and building a culture that fosters innovation are key reasons for these failures.
8. Staying relevant: Keeping up with the pace of industry change and uncertainty that is rapidly evolving, understanding customers changing demands and expectations timely, and adopting research, strategy and technology are downfalls to innovating.
9. Lack of management buy-in: Incorporating all the downfalls from the points listed so as to help create a solid business case takes dedicated teams and efforts in getting management buy-ins. This lack of coordination will create issues not just while implementing changes but also while realizing them.
   

Importance of employee engagement

Innovation begins from the inner view, with employees playing the most critical roles in understanding and helping translate customer needs to organizations that can, in turn, empower innovation. How can organizations then empower employees to help elevate the innovation process?

1. Innovation Culture: Fostering a culture of innovation should be the starting point towards encouraging employees to put forth their recommendations, provide ideas, enhance creativity, encourage experimentation and provide clear vision and mission statements to establish support.
2. Recognition: Organizations should not only recognize but reward employee contributions towards the innovation space. This helps encourage bringing new ideas or enhancements to the table, helps enable risk-taking and, with the right support, helps carve out a path to implement valuable proposals. For example, many large organizations focus on patenting and trademarking ideas that are proposed by employees and offer platforms that identify and push ideas through multiple stages. The support extends to providing time and possibly an alternative career roadmap so that employees are encouraged to pursue their ideas without the pressure of doing everything else. 
   
3. Encourage ideation: Clear communication encouraging employees to ideate should be made a part of the primary agenda so that improvements to process, product and service can be put forward. Hackathons, brainstorming sessions and providing suggestions and feedback mechanisms are some ways to encourage ideation.
4. Training and development: Organizations should invest time in training their employees to understand the importance of innovation and provide programs to include the process for innovation. New skills and knowledge sharing can be extremely helpful in elevating how employees think about problem-solving and innovation.
5. Cross-collaboration: Large organizations can face issues with working in silos, and it’s important to bring access to multiple functionalities that exist across different teams so that employees are encouraged to collaborate across the board with the right subject matter expertise and possibly across tiers. These efforts would increase the value of the ideas brought forward as they get better perspectives when they work across teams.
   
6. Encourage and provide constructive feedback: There needs to be considerable encouragement in educating the benefits of innovating. Not all ideas will be feasible, but feedback should be provided constructively so as to continue encouraging innovation.
7. Resources and experimentation: The ideation process can go through multiple stages, including research and experimentation, to assess the viability of implementation. Organizations should consider providing the right tools for innovation, resources and guidance, which include allocating budgets toward innovation.
   
8. Implement agile methodologies: A continuation of experimentation involves rapid prototyping, A/B testing and creating proof of concepts (POC) to get quick management buy-ins.
9. Crowdsource ideas using technology: Platforms like IdeaScale enable organizations to gather both the inner and outer views for enhancing their abilities through the generation of ideas not just from employees but also through customers.
10. Measure success: Many a time, I have found this element to contradict the intentions to grow innovation, but that is only because organizations have not thought through all the different ways to evaluate ideas. In the CX space, it’s easier to go back and check how customers feel about new additions by gauging their feedback and accessing the impact of the idea. However, KPI’s need to be put in place and re-evaluated where required to cater to the diversified ideas that can be brought to the table.

Design thinking

Last but definitely not least, design thinking should be considered an important tool to access innovation in the customer experience space. The principles that govern design thinking should be defined so that the organization uses a ‘Customer-first’ model allowing organizations to remain Customer-centric. By maintaining the target audience in mind, even internal processes can be enhanced so as to enhance the outer view of the customers.

Conclusion

In conclusion, innovation requires planning, needs a clear strategy, ownership and also the right communication so as to encourage stakeholders to ideate, keeping the right objectives in mind. There are bound to be challenges along the way, but if designed well, organizations will benefit by gaining memorable experiences and hence customer loyalty.

The post <h1>Experience Innovation: Give a Memorable Experience to your Customers</h1> <h3>Gain loyalty at multiple stages of customer journey</h3> appeared first on The Experience Journal by QuestionPro.

Summary

The article starts with an AI-generated response to the question, “What does the future of user research look like?” which lacks novelty and innovation in approach. The modeled answer is based on already existing data, rather than the intent to uncover unknown facts about the users.

The author highlights the importance of having a human-centric approach while conducting user research and how AI systems like ChatGPT miss out on various dynamic factors that influence human behavior; hence, a more tailored approach is the best way forward.

Table of Contents

• Summary
• Introduction
• Keeping up with the Kardash-AI-ns
  • ‘User-centricity’ will never change
• The future of user research lies with us: The researchers
  • So where do I think we are headed?
• Conclusion

Introduction

Around the start of a year, business people often discuss plans and aim to forecast the year ahead.  ‘What’s going to happen?’, ‘Where do you see the biggest changes…?’ and frankly, I’m no different!  I’ve been asked a lot recently about the future of user research and where I think it is headed.  And I thought about writing this piece to express my thoughts, but who wants to read about another forecast?!  We’ve all seen rapid change over recent years, and for me, the research looks VERY different than even just 5 years ago.  

Keeping up with the Kardash-AI-ns

So in the spirit of embracing new trends and trying new things, I thought I’d ask the question ‘What does the future of User Research look like’ to the latest AI platform on the block: ChatGPT.  So here is what is said:

“The future of user research is likely to involve a greater emphasis on technology and data analysis. As technology continues to advance, researchers will have access to more data and more sophisticated tools to analyze that data. This will allow researchers to conduct more in-depth and accurate research and gain a deeper understanding of user needs and behaviors.

Additionally, user research is expected to become more integrated into the product development process. This means that researchers will work closely with designers and developers to ensure that products are being developed with the user in mind.

Virtual Reality and Augmented Reality are becoming more prevalent in the field of user research. It allows researchers to conduct research in a more naturalistic and realistic environment, giving them more insight into how users interact with products and services in the real world.

Another trend in user research is remote research, where participants are not in the same physical location as the researchers. Remote research is becoming more common due to the COVID-19 pandemic and advancements in technology, which allow researchers to conduct research remotely using online tools and platforms.

Overall, the future of user research looks promising as it is becoming more data-driven and technology-focused, providing researchers with more tools to conduct more accurate and in-depth research.”

So firstly, I know I cheated by using a robot to provide my content!  But the world never sits still and research should be no different, so why not try something!?  Secondly, I’m actually amazed by this technology.  It really does feel like the next game-changing piece of tech, and my imagination starts to run wild with how this could be used in research.  

But scrutinizing the answer, I’m sure we can all agree it’s pretty safe. Most of this has already arrived and nothing remarkable has arisen here!  So to give my own (human) take on where user research is heading, I’ll start briefly with my own story.

‘User-centricity’ will never change

I studied Sociology (way back in the day now) as a student, and I have always had a fascination for people and the different ways in which people live.  This is often a prerequisite for travelers, and ‘finding yourself’ often in a period of life where you just want to explore and see what else exists in our amazing world.  For me, research allows for this and I think I’m in the best profession there is!

Learning about people and different cultures makes you question things about your life or the environment you are used to.  Which is just endlessly fascinating.  How different people interpret language or services, or behavior creates so many opportunities to understand and figure out ‘why?’  I just don’t get tired of it, and there’s always something new to explore.  

And when it comes to researching people, having a genuine curiosity for them and constantly asking ‘why?’ is key to any great research and at the core of our great profession.  

The future of user research lies with us: The researchers

I do, however, worry that when studying people, the industry is a little stuck in its ways.  There are many ways to conduct research, and thousands of great works explain the wide variety of tried and tested methods.  But if we live in an ever-changing world, then why should our methods stay the same?  I definitely think some anchors should exist within a piece of research, for example, ensuring you are talking to a representative panel. Let’s be honest, you could be the most talented researcher ever to walk the earth, but if your sample is off, then the data you collect won’t mean anything, then your insights won’t stack up.  It’s like trying to build a house on a foundation of quicksand.  

Ethics is also something we should never cut corners on – we need to build trust with our participants, do right by them and show the utmost respect.  Often things they share are personal, private, and sensitive.  That should ALWAYS be respected otherwise, we risk losing the one thing we need in our profession!

If you stick with ethical research and spend time creating a high-quality sample, then go and experiment with methods and try things out.  Don’t stick rigidly to methods you learned as a student, and look outside the box.  We deal with people, so take inspiration from other professions such as journalism, author, design, or even your family and friends.  Particularly with qualitative research, we have to build rapport and a bond with our participants, so they tell us stuff!  Ask journalists how they get the quick scoop, speak with your parents about how they talk with people and even ask your friends how or why they would open up even a little.  Without that, jumping in the cold means a guard is still up, and we don’t get the data we need.  Bring walls down by talking, showing genuine curiosity, and encouraging them to talk.  

Another great exercise is to ask people close to you what you are like and how you come across.  Ask them to be totally honest – which you may not like – but understanding how you come across may help you shape your style when it comes to qualitative research.  

And I believe that as researchers, we should try things and stick with things that work when we find they do.  But keep iterating and moving just as you would for a new product or service.  

For me personally, this change hit home when I began conducting research with designers for the first time.  Participants can only tell you so much, but the feedback becomes much more focused when they have an actual prototype or something they can use.  It shines a light on what people really want, as opposed to what they think they want.  I don’t think I could research without designs now, and I’m able to do so much more thanks to this partnership.

So where do I think we are headed?

Understanding users is complex, with lots of different ways to approach them.  We see a shift away from demographic type data having such a strong influence on how we look at users, and I don’t necessarily think that is wrong, but there is so much more we can learn about people.  Looking at the behaviors of people is very interesting and allows us to create products or services that fit around that.  However, I still think there is a big opportunity in putting a different lens over human behavior, to look less at the individual and more at the external impact in people’s lives which can affect the behavior.

Often, they can’t tell you exactly what they need, but by understanding their circumstances can bring to life who you are designing for.  Take, for example, someone who is at a stage in life where they are expecting their first child.  There is A LOT that will change for them, and often people don’t know what to expect – if it’s their first child, then why would they?  There will be a whole host of things to will need to buy: a pram to walk the baby in, a cot for it to sleep in, creating space in the family home, then planning ahead for new expenses for food or lifestyle changes.  What’s more, the baby’s mother will often take maternity leave, which is crucial for people in this situation. However, household income is very likely to be reduced, which only adds to the change in circumstances.  

Then outwith this impending change to an individual’s life, homeowners can have impending mortgage renewals. With recent changes in the mortgage market, this can create a perfect storm for many people.  

This example only scratches the surface, but if you consider how many users will go through a change like this, you can build a great picture for creating and designing products or services.  A picture that tells you far more than current demographic information.

Understanding the person behind the customer can also tell you more about social trends. Then, start to create an educated guess on where things may lead also.  

Take the current Gen Z or even Gen alpha.  The only cohort of people who have lived through imposed lockdowns through their youth and through periods of their education.  We are only at the tip of the iceberg in terms of understanding the impact on these respective generations and how this will impact the world in the future.  Learning, working, playing, communicating, and generally living through a digital world has never been seen on such a scale: what does that do to the development of a young person?  How has it affected mental health?  How has it affected their view of what they want from their own lives?

I haven’t even touched on social media yet, but the split in people’s lives from an ‘online life’ and an ‘offline life’ is a fascinating dynamic, where people are looking to showcase themselves more and more, but the pressure to keep that up brings challenges that we are only beginning to learn about.  

Conclusion

ChatGPT represents a continuous change in the world, and by embracing these changes as user researchers, we can do so much more.  Our desire to learn about people will never leave and will always remain a constant, and it’s great to see so much emphasis put on human behavior and learning about how people experience.  But those eternal factors for me are so interesting.  The lives of people, how they live, and how their environments impact them.  That’s what we know least about, and I’m excited to learn more!

The post <h1>A Human-centric Approach to User Research in the Age of AI</h1> <h3>What does the future of user research look like</h3> appeared first on The Experience Journal by QuestionPro.

Summary

With EVs disrupting the automotive industry, car manufacturers, dealers, and OEM providers face the eternal challenge of keeping customers happy. However, in times of advanced technology such as AI and global business challenges, it’s no longer a question of customer satisfaction. You need to be a step ahead in the league and delight your customers.

This article talks about:

A. Technological disruption in the automotive industry and how it affects dealers and customers

• With the vast majority of vehicles being software-driven, there is always a possibility for technical errors and cyber attacks. Maintaining customer’s trust is paramount for automotive brands and can be a key differentiator in delighting customers

B. How to utilize strategic, revenue, and risk-management opportunities?

• Through the use of structured analysis and customer journey mapping, present the manufacturer with a visual representation of the interaction. This allows the manufacturer to understand how and when messaging is most likely to be effective.
• Anticipate future customer needs.  For instance, if a customer has locked himself out of the car three times in the past month, trigger communications with tips to avoid getting locked out.
• The ability to provide timely and relevant information to customers can be critical to the customer’s safety. Having permission to provide notifications via text or mobile application increases the effectiveness of communication.

Table of Contents

• Summary
• Introduction
• Disruption in the automotive industry
• Opportunities & Solutions
  • 1. Strategic Opportunity
  • 2. Revenue Opportunity
  • 3. Risk Management Opportunity
• Closing notes
• Reference

Introduction

Over the past two decades, we have witnessed a massive transformation in the automotive industry, driven primarily by technological advances.  When the value in vehicles was once 90% hardware-based, it has shifted to more than 50% software-based. According to Smartcar.com, 91% of new car sales in the U.S. are connected [1]. That’s over 13 million connected vehicles sold in the U.S. alone.  In addition, electric vehicles are now 8.3% of the world’s vehicles.  Self-driving and automated vehicles are now a significant consumer offerings as well, with autonomous vehicles expected to account for about 12 percent of total car registrations by 2030. (Source: Statista.com).  

Like many industries that go through a major evolution, the new technology in the automotive industry has arrived long before infrastructure can be put in place to support the innovations properly.  Don’t forget that we purchased HD TVs well before our cable provider sent us any content in an HD signal. The team at SuiteCX powered by QuestionPro has been working with manufacturers to catch up to the current technology in the auto industry by identifying and prioritizing service support gaps and quickly filling them. 

According to a McKinsey report, the future of automotive looks highly tech-driven. Not surprisingly, this, along with the research and purchase moving more and more online, has dramatically changed the customer’s experience of researching, buying, owning, driving, and maintaining a car.  The team at SuiteCX by QuestionPro has been working in lockstep with automotive manufacturers for over 20 years, helping them navigate through this changing environment and ensuring they evolve to achieve competitive advantage through customer experience excellence.

Disruption in the automotive industry

As technology has taken a front seat in the new generation of vehicles, the industry has undergone a tremendous amount of change.  This has set off a chain reaction of events that significantly impact car manufacturers, dealers, and customers.

1.  60+% of vehicle features are now software driven

The shift toward software-based features means the car has essentially become a rolling software platform capable of delivering services based on data, sensors, and analytics.  While this has significantly benefited the customer in most cases, it also results in more complexity.  As a result, dealers have had to hire and manage entire departments of digital trainers and managers in order to support their customers.  In addition, due to the nature of the software, there are more recalls and unplanned events that create a need to communicate quickly to resolve problems and, in some cases, prevent dangerous situations.  Not only are there software bugs, there is also the risk of cyber-attacks.  So, trust in the manufacturer and dealer is more important than ever.

2. Disintermediation of dealers

The increase in software, which is often developed, supported, and billed by a 3rd party (think SiriusXM, OnStar, etc.), has resulted in the disintermediation of the dealers.  These 3rd parties have their own objectives, especially around service and accessories, and often the dealer is not totally in the loop.  Though the dealer would like to be the primary point of contact for the customer, often the customer goes directly to the 3rd party service provider, who may or may not be able to resolve an issue.  This can result in the customer being bounced around between the 3rd party and the dealer, and/or the dealer is never informed of the issue at all.  Because the brand is ‘responsible’, the fallout from a CX perspective can be significant.

3. Many new entities touching the customer

In this new automotive dynamic, many more people, departments, and companies are looking to engage and communicate with the customer – Dealers, Manufacturers, 3rd Party service providers, and more.  Car manufacturers typically utilize many agencies to manage the brand, design, creative, execution of campaigns, onboarding, and after-market activities.  These disparate teams are typically not coordinated in terms of outreach and communication with the customer.  Even within the dealer, different departments – Finance, Warranty, Service, etc. – are marching towards different objectives, metrics, and incentives.  Each department typically creates its own communication plan to achieve those objectives.  

So, what does all this mean to the customer?

Unfortunately, all of these changes have resulted in a fairly challenging experience for most new car customers.  First and foremost, customers have been overwhelmed by inconsistent, irrelevant, and poorly timed communication.  Inside this blizzard of communication, the customer is unable to differentiate between the communication from a branded entity (corporate or dealer) and other senders.  The customer assumes they are all integrated and coordinated. We have seen cases of a new owner being touched over 94 times in the first six weeks after driving off the lot.  And in many cases, they are more confused than ever with the proliferation of mobile apps and social media sites. 

Compounding the issue is the fact that if they unsubscribe, now there is no ability for the customer to manage or change their communications preferences.  So, even if they wanted to continue to get a select set of communications, they have no way to do that.  Once the customer has unsubscribed, they will miss important communications about issues, or worse, recalls.  And leased vehicles may not be maintained to standards, resulting in higher trade-in costs.

This brave new world has presented challenges to the car manufacturers as well.  Media channels cannot be optimized in this environment, leading to inefficient and ineffective advertising spend.  In addition, due to the heavy reliance on email, once a customer unsubscribes (or simply stops reading) the messages, the car company loses the ability to communicate upgrades and additional services.

Opportunities & Solutions

As is often the case, with disruption comes opportunity, and in this case, there is an opportunity for automobile manufacturers and dealers to improve their bottom line, while simultaneously improving the customer experience.

1. Strategic Opportunity

By understanding the holistic customer experience, including how the customer interacts with the manufacturer/dealer, their key motivators and decision-making process, and then highlighting their moments of truth and pain points, we can suggest improvements in terms of people, process, and data sharing. This is particularly helpful in helping the manufacturers with little direct customer feedback and the dealer with the robust relationship coordinate more.  Through the use of cross-consultative workshops, we can pull disparate teams together to solve existing customer experience issues.

Through the use of structured analysis and customer journey mapping, we can present the manufacturer with a visual representation of the interaction, clearly showing them where they are pushing the customer away versus engaging them.  This allows the manufacturer to understand how and when messaging is most likely to be effective.  Sharing this information with the dealers lets them know what happens at each lifecycle step, so they can also coordinate and optimize their mind-share and spending. 

2. Revenue Opportunity

Manufacturers and dealers that are progressive, are already working together to develop a holistic view of their reputation, sales, and post-sales efforts.  This must be a collaborative and comprehensive endeavor that will improve coordination at critical customer milestones such as onboarding, first service, lease renewal, and new sales.

Once a holistic view is in hand, manufacturers and dealers can effectively use technology to surprise and delight the customer with relevant content, such as tips and techniques, as well as to notify and remind customers about sales and promotions.  The ability to text (or otherwise notify) customers about promotional information is critical to a good experience.

Beyond basic marketing promotions, manufacturers and dealers that understand the customer journey can begin to anticipate future customer needs.  For instance, if a customer has locked himself out of the car three times in the past month, that data can be leveraged to trigger communications to that customer with tips to avoid getting locked out.  And the dealer knows to give the customer a quick lesson the next time that particular customer comes in.

Another great opportunity for both revenue and relationship is to tie the owner, manufacturer, dealer and, in the case of EVs, the power company, into a holistic and relationship-oriented group, ensuring that sales, service and maintenance and charging are all coordinated and friction-free.

3. Risk Management Opportunity

For manufacturers and dealers, the ability to provide timely and relevant information to customers isn’t just important for marketing efforts, it can be critical to the safety of the customer.  The most basic are reminders for planned maintenance and service.  Unplanned events, especially those for software bugs or even more impactful cyber risk issues, can have serious consequences for customers. Events that are communicated quickly and proactively are much better received by the customer.

Knowing the customer’s primary communication channel(s) is of utmost importance, and having permission to provide notifications via text or mobile application increases the effectiveness of communication.

Closing notes

As you see – even the most staid of industries need to seriously think about Customer Experience and ensure that they have a clear view of their customers’ value, needs, and behaviors throughout their whole relationship journey.  The more proactive and collaborative the manufacturers, dealers and even their partners like utilities become, the more likely that EV and autonomous vehicle adoption will be enabled and supported.

Reference

• Don Butler, Ford Motor Company, “Digital Transformation and the Automotive Industry,” CXO Talk, https://www.cxotalk.com/connected-vehicles-automobiles-technology-platform-don-butler-ford

The post <h1>Customer delight in the automotive industry: Opportunities and Solutions</h1> <h3>Move beyond customer satisfaction</h3> appeared first on The Experience Journal by QuestionPro.

Table of Contents

• Introduction
• The Great Reshuffle’s Impact on Insights Teams
  • How High Turnover Impacts Insights Teams
• The Role of Storytelling in Insights
  • Today’s Challenge for Insights Storytelling
  • Solving the Stories Problem
• Insights Repositories: Technology for Story-based Insights
• Author

Introduction

While the full impact of The Great Reshuffle on the insights industry remains to be seen, there’s little question that our current high turnover environment presents insights teams with some unique challenges.

It’s likely that UX, CX, and market research professionals have spent more hours in training over the past 18 months than at any time before. And while fresh faces and new perspectives can add significant value to an insights team’s production, this environment creates unique challenges for teams that are serious about crafting and weaving holistic, continuous research narratives into their deliverables – stories that empower decision-makers with not just data snapshots, but the fuller context they need to make fully-data-driven decisions.

The fact is: The only way to combat secular economic – specifically, labor-force related reshuffling – trends on an insights team is to adopt methodologies and insights repository applications centered around storing, organizing, and democratizing insights in a way that allows important research narratives to live on through successive and transient research team generations.

The Great Reshuffle’s Impact on Insights Teams

According to recent reporting by NBC News, the US tech industry has eliminated tens of thousands of jobs in 2022. Tech executives and industry leaders cite inflationary concerns, rising interest rates, and new challenges in digital advertising ROI as crimping their financial outlooks.

The insights industry, of course, either overlaps or sits inside of the tech industry (depending on how you categorize these industries). Among the firms reporting mass layoffs is Momentive – in October 2022, it said in a regulatory filing that it had laid off 11% of its staff. Figures from privately-owned companies are, of course, harder to come by. But anyone with eyes on the insights space — or first-hand experience with on-the-ground research teams — is well-aware that workforce reduction has been a major theme of the past 9-12 months on insights teams around the world.

How High Turnover Impacts Insights Teams

High turnover makes life difficult for any team or department. But on insights teams where workflows and even insights themselves are not logged in any existing repository, high-turnover environments can be especially problematic.

First, high turnover can diminish the volume and quality of an insights team’s institutional knowledge. Team members who leave their jobs take with them a deep understanding of their team’s workflow processes. Training new team members to “pick up the slack” or replacing a team with a smaller team or a new specialist can be an expensive process.

Further, institutional knowledge extends far beyond workflows. On teams without an effective system-of-record in place, departing or laid-off team members take with them hosts of unrecorded or goes-without-saying information about their brand’s product or service that cannot be easily replaced. For example, research gaps and cross-project themes that have been of particular interest to researchers may not have been recorded like the insights gleaned and analyzed on a research project. Additionally, “negative” insights – that is, what insights teams learn by what’s not in the data – often have significant influence over how a research team plans and progresses. It’s simply not realistic to expect that every valuable insight driving an insights team’s efforts is recorded, codified, and easily accessible – especially without the aid of a research repository.

Additionally, of course, high turnover creates a sense of instability and uncertainty on an insights team. This goes without saying. When coworkers are laid off, remaining team members worry about their job and work quality often declines. And without the support of an insights repository that remains part of a team no matter who leaves, remaining researchers often express frustration at the inability to discover existing insights – ones previously known to researchers who have moved on to other companies or the location of which is not pegged to the logic of any centralized repository.

The Role of Storytelling in Insights

Before exploring the ways, a high-turnover UX, CX, and market research team’s primary goal is to deliver valuable insights that can help guide decision-making within your organization. To do this effectively, it’s important that insights teams take a holistic view of the research and insights they gather, and present them in a way that is both comprehensive and easily understandable to those who will be using them to make important decisions. This means going beyond just presenting raw data and numbers, and instead weaving a compelling narrative that illustrates the key insights and their significance.

In other words, effective insights deliverables (and, for that matter, research itself) is all about storytelling.

There are a few key reasons why a story-based — or storytelling — approach is so important for UX, CX, and market research teams.

First and foremost, a holistic, story-based framework for presenting insights helps to ensure that they are easily understood and actionable. When insights are presented in a clear and compelling way, decision-makers are more likely to be able to grasp their significance and use them to inform their strategies and actions. In contrast, when insights are presented in a fragmented or disorganized manner, they can be difficult to interpret and may be overlooked or ignored.

Additionally, a story-based approach to presenting insights helps to put them into context, which can be particularly valuable for decision-makers. By providing a clear picture of the background and context surrounding the insights, you can help decision-makers understand not only what the insights are, but why they matter and how they can be used to inform strategic decisions.

Another key reason why it’s important for UX, CX, and market research teams to be storytellers is that these kinds of stories build trust and credibility. When you present insights in a clear, holistic, and story-based manner, you are demonstrating a commitment to providing high-quality, reliable information that decision-makers can trust. This can help to establish your insights team as a valuable resource and partner within your organization and can help build trust and credibility with external stakeholders – clients, partners, etc.

Today’s Challenge for Insights Storytelling

In the context of high-turnover, and having established the unique importance of storytelling on insights teams, the challenge for insights teams today is clear:

When researchers come and go in rapid succession, how can an insights team manager maintain continuous stories across this revolving door of personnel? Stories that, ultimately, are required if decision-makers expect to make data-driven decisions in the context of the larger trends and narratives that give insights deliverables any meaning whatsoever?

Insights stories are ultimately about trends over time. Stories involve a succession of events – developments in the industry, advancements in technology, changes in consumer preferences, and how an insights team’s work has tracked these changes against their brand’s performance. No matter how many new faces might be on an insights team, a brand’s customers are part of a long and extended story.

Without a clear understanding of what kind of research has already been done, these stories are not forthcoming. And when researchers come and go at high frequency, connecting these dots becomes an almost impossible task. A study that may have added considerable value to an existing insights narrative – for example, one that confirms the direction of consumer sentiments regarding a particular product line – might

Solving the Stories Problem

By now, any tuned-in insights team is acutely aware of the importance of an insights repository solution toward helping researchers more easily access historical projects, identify trends, and share findings in the larger context of what methods were used to discover those insights.

An insights repository is a centralized platform or database where research and insights can be stored, organized, and accessed by members of an insights — UX, CX, or market research — team. This provides a number of benefits and makes possible a host of value-adding capabilities, including:

1. Easy access to historical projects and insights. With an insights repository, researchers can quickly and easily access past projects and findings, which are valuable for identifying trends and patterns over time. This empowers researchers to build a more comprehensive (and shareable) understanding of their customers, markets, and other key stakeholders, and can inform the design and execution of future research projects to maximize their value to larger corporate initiatives.
2. Improved organization and collaboration. An insights repository also helps to improve the organization and collaboration within a UX, CX, or market research team. By providing a centralized platform for storing and accessing research and insights, team members can more easily collaborate and share their findings with one another. This ensures that all team members are working from the same set of information, and can also help to prevent duplication of effort and reduce the risk of mistakes.
3. Enhanced sharing and dissemination of findings. An insights repository can also make it easier for UX, CX, and market research teams to share their findings with other departments or teams across their organization. Because all insights are stored in one single database, it’s easy to connect projects across programs or research initiatives that help to color in details about the same larger trend.

It should be obvious why high-turnover and in-flux teams create unique challenges for insights storytelling. We’ve covered this already. But then how can this problem be realistically solved, and in a future-proof manner that accounts for not just our present high-turnover environment, but also up-and-coming trends (like generative AI) that will undoubtedly impact the insight space?

The answer is technology. Yes, the management of insights teams will shift in response to changing labor force conditions. That goes without saying. But without a strong plan for implementing technology that, too, accounts for these developments, insights teams will forever play catch-up, always one step behind where they need to be.

Insights Repositories: Technology for Story-based Insights

When it comes to insights technology applications (rest-tech or in-tech), there have traditionally been two highest-level categories of applications:

1. Data Gathering: These applications empower insights teams to gather data from of-interest populations (customers, target market consumers, etc.). These include survey platforms, community engagement platforms, tools for recording interviews and user behavior, and other observational tools designed to equip insights researchers with firsthand knowledge of their target populations.
2. Data Analysis: These applications make it possible for insights teams to interpret and analyze the data they harvest. They include an AI-powered NLP to

For the past few decades, this has been the primary framework for thinking, at the highest level, about insights technology. But over the past few years, a new need has arisen and been met by insights-focused development teams in the form of insights repositories.

An insights repository is a built-for-insights “second brain” platform. It empowers researchers to organize, explore, search and discover all their research data—past and present—in one organized place.

Insights repositories sit squarely between data gathering and data analysis applications, filling a gap that has only widened as the volume of data being gathered and stored by insights teams around the world has reached a critical mass — a threshold, that is, whereby a whole new set of insights can be discovered simply by parsing, indexing, and analyzing data that already exists.

In this way, insights repositories aid research in the process of continuous discovery and story-building. More often than not, stakeholders and researchers complain that they have little visibility into the kinds of research that have been conducted or historical data, and reports aren’t available to make sense of or even compare to. An insights repository solves that problem by enabling organization-wide access to insights project data, no matter the scale or types of research — qualitative, quantitative, 1-1 interviews/customer research, or even behavioral research from existing data.

Think of an insights repository as a “second brain” for insights teams, business stakeholders and clients, and decision-makers where there is a way to retrieve and summarize past studies, build upon existing insights, see the progression of cross-project knowledge graphs, and most importantly, eliminate research duplication and minimize the time and cost of future research.

An effective insights repository ensures the intelligent indexing of your existing tech stack of research, collaboration, and communication tools. The best of these applications allow for workflow management, vendor API access, real-time status and notifications, and an AI-driven tool for churning out smart insights in a scalable manner.

The insights repository consists of three fundamental levels of data:

• Insights: At a holistic level, the insights desk consists of tagged, indexed, and unified insights. This is from past and existing studies of different research types, including qualitative and quantitative studies, user research, custom studies, advanced research modeling studies, and more. All of these insights are easily searchable with the use of business taxonomy and meta-tags. These insights also monitor cost spends, ROI of studies, and other factors that provide an insight into time and resource spends.
• Observations and nugget-ed information: The secondary level of the insights hub aims to provide information at an even more granular level of studies a certain team or product conducts, insights from longitudinal tracking studies, product enhancements, marketing messaging, or a campaign that emanated from a given initiative. This level also stores presentations and outcomes, so that tribal knowledge from siloed studies is available for all to see.
• Raw research data: The final component of this repository is the actual data, including customer calls, vendor research data, questionnaires, business taxonomy tagged studies, qualitative and quantitative data, IDI’s, customer behavioral data, and more. All of this is unfiltered data that can be looked upon and leveraged as and when required.

All in all, an insights repository is a second-brain tool that empowers insights researchers with democratized and in-context research project data. And it makes possible the existence of story-based insights across time and teams. This is because, by storing not just project meta-data and raw data files, but hosting projects in context (that is, displaying how projects connect to larger themes and to other projects in the repository), stories exist by default. Research projects don’t live in silos, nor is any dataset divorced from the tools used to create and analyze them. The full lifecycle of research projects is stored in an insights repository, accessible to all insights (and other departments) team members across an organization.

In this way, insights teams can begin storing insights as stories, thereby coloring their deliverables with the context needed for anyone, anywhere to understand how a dataset or deliverable contributes to the larger story that is a brand’s historical, continuous, and ongoing relationship with customers.

The best insights are not delivered in a vacuum. They are presented in the context of where they add the most value. Likewise, insights should not be stored in a way that is meaningless to everyone except those who, in their minds, understand the significance of the intangible and unrecorded trends and narratives therein.

Insights repositories are the solution to this problem. All insights belong to a story. And while narrating every possible story is inefficient (and probably impossible), a smart built-for-insights repository can make it possible for these stories to emerge and evolve across teams and eras.

Author

The post <h1>Preserving Insights Stories: Challenges and Solutions in a High-turnover Environment</h1> <h3>How an insights repository can help presenting data in the right context</h3> appeared first on The Experience Journal by QuestionPro.