Exploratory Empathy-Driven Insights and Innovation

Ultimately we design products and brands for people, sometimes for individuals (e.g., dads, millennials, amateur athletes, STEM enthusiasts), and other times for customers inside an organization (e.g., systems engineers, procurement managers, heads of surgery, chief data officers). But at the end of the day, brands serve people. Rich, qualitative, and often observational research methods offer a window into people’s lives as they experience the customer journey and confront various situations in the course of moving through their world. These empathy-driven insights can prove useful in uncovering white spaces against which to innovate new product and brand ideas.

For example, a cleaning products company spent several weeks talking to women in their homes to understand the context and experience of bathroom cleaning. These women cleaned and spontaneously narrated the experience while the company’s insights team watched and generated interpretive hypotheses that resulted in follow-up questions. By the end of the exercise, the consumers and insights team had collectively reached some conclusions about the top friction points that needed a solution. Specifically, the most effective bathroom-cleaning products came at the cost of fumes so intense that women threw windows open and banished kids to the other side of the house to keep them away from the smell. On top of the negative fume experience, the toilet brush and all its germs completely creeped them out. Even wearing gloves, many shuddered and made faces as they pulled the brush from its stand.

As a result of these journey-driven lessons, cleaning products from the KaBOOM brand now tout a lack of “obnoxious” fumes alongside the claim of potent efficacy. To address the gross toilet brush experience, KaBOOM developed a foaming toilet bowl cleaner that no longer requires a brush. The foam rises up the side of the bowl and cleans as the bubbles go down. The color of the water in the bowl changes once the cleaning process finished. Flushing the residue completes the process. No brush needed (phew!).

Clorox addressed consumers’ aversion to the toilet brush a different way. By creating a toilet wand that has disposable one-use scrubbers, it eliminated the need for storing the dreaded brush between cleanings. Note that “the brush grosses me out” insight certainly doesn’t propose a clear solution, but it does give a highly resonant problem for product designers to solve and for brand marketers to use in crafting a compelling brand promise. Both Clorox and KaBOOM (owned by Church and Dwight) leveraged the same empathy-driven customer experience to drive innovation, albeit arriving at different leap-forward solutions rather than merely trying to develop a less revulsion- inducing brush.

TeamSnap, a mobile and web-based software platform, used a similar journey- driven inquiry into youth sports organizations to develop solutions for managing all their administrative functions (e.g., web site creation, registration, scheduling, team rostering, communication, and so on). A deep dive revealed that these primarily volunteer-run groups had to find workarounds to enter player information from their own registration database into a separate web-based system in order to register kids with the relevant sanctioning association for their sport (e.g., USA Hockey, Little League International). For health insurance and liability reasons, as well as to qualify for certain tournaments and games, registration with these sanctioning organizations was mandatory. As a result, volunteers retyped information or did monstrous cut-and-paste jobs into both systems instead of spending more time on things that had a bigger positive impact on player experience and performance: coaching, conditioning, mentoring, and one-on-one communication with players and parents. From a sports organization perspective, the often-clunky dual registration represented a major friction point, although not all the organizations overtly complained about it.

After learning about these experiences, TeamSnap built application programming interfaces (APIs) with USA Hockey, Hockey Canada, and US Lacrosse. And, it is building more than 20 additional APIs or other semistructured formats (e.g., .CSV files) to enable a sports organization to register its players and collect payment, then use that information and money to seamlessly register with the relevant sanctioning body. This provides another example of how insights based on observing the customer journey identified a problem that spurred innovation and the creation of differentiating features.

Persona Development

As the previous examples illustrate, discovery-oriented customer journey insights can effectively drive innovation and entrepreneurial thinking to generate new ideas. However, once a company uses these insights to identify a white space and direct its innovation, it still has to strategically define its target audience in order to further guide product development and branding activities. Customer journeys as an insight approach have utility in this regard as well.

Traditionally, marketers have identified target audiences in terms of segments that describe the demographics of the bulk of the audience (e.g., men 30–64, live in cities of 1MM+, income $75K+, married, white-collar professionals, skilled trades workers). The demographic range captures the variance of the segment. When aligned with brand attitudes and behaviors, segment descriptions are strategically valuable in sizing and reaching an audience for a brand.

By contrast, personas go deeper, depicting the prototypical customer in a vivid, in-depth, and often colorful manner. Companies develop personas as a creative expression that consolidates the segment into a single person, often including a photo or visual of the customer along with a written depiction outlining age, specific occupation, skill levels, emotional and functional goals and frustrations, and personality characteristics (e.g., introversion versus extraversion, relative optimism). The persona may also include brands that inspire the customer, their level of technology adoption and integration across devices and platforms, behaviors and habits relative to the category of interest, and behavior-change drivers. This human, journey-fueled lens adds critical empathy to bring demographic clusters to life and put a true heart, head, and face to more sterile variables. In other words, the persona is the average lived experience of a member of the target audience.

Consider the following hypothetical persona example for a typical farm cooperative member. As seen in Figure 13.1, the persona outlines the farmer’s goals and concerns, as well as his behaviors around using technology in his work as a producer (farmer). It also depicts app use and brands with which he identifies. This persona could help the co-op design its mobile app and web site to encourage greater patronage and loyalty among co-op members. Keeping the persona in mind, the development teams have a clear picture of the person for whom they are designing, which informs the information architecture, functionalities, user interface, and overall site and app design.

Overall, personas remind organizations that they design products and build brands for people, and help humanize strategic terms such as “customer” and “segments.” Personas make it easier to write messaging and design experiences. We talk to and design for people, even if we target segments.

Persona development also promotes respect for the different players or actors involved in the buying process or overall experience of consuming a given product or brand. For example, in an operating room environment, an electrosurgical and vessel-sealing generator brand must explore the journey of the full cast of characters—surgeon, circulating nurse, scrub nurse, scrub tech—who interact with the device during an operation. This type of customer journey orientation can reveal the unmet and unspoken needs and benefits sought for each actor. Creative expressions of all the players illuminate what is meaningful to them and why, and bring their collective experiences to life.

Picturing how the scrub nurse will stand at the machine next to the surgeon, while the circulating nurse moves around managing the broader surgical case, user interface–user experience designers can effectively design features such as machine button size, placement, color, types of casters and brakes, visual display size, font, location, and so on. Getting into the head of the surgeon helps designers understand what he or she is thinking about during the operation and what potential distractions the product design has to eliminate. And, creating messages that appeal to the mindset of each persona represented in the operating room journey makes brand positioning and sales messaging easier to develop and execute. Quite simply, it’s much easier to talk to customers in the language they use around your brand if you picture them as, well, people.

In sum, the customer journey lens in creating personas helps build team-level empathy for the human consumer and acts as a guide and filter for brand and product decisions. Personas take targeting to a deeper level and reduce the likelihood that managers will substitute their own personal opinion (or the opinions of friends and family) for the views of the target customer.

Measurement and Data Collection Opportunities

The journey mindset is the driving force behind the current trend of end-to-end customer feedback, which empowers brand marketers, product teams, and operations groups to continually focus on improving the entirety of customer interactions with a brand. If it is true that the totality of people’s interpretations of their touchpoints with a brand constitutes the brand’s meaning, then experience-driven insights provide the foundation for developing, managing, and maintaining the brand experience. Overall, the customer journey approach uses insights as a tracking tool to understand what is working and what could be improved on both the product side and the brand marketing plan side.

Customer experience platforms such as Medallia¹ and Net Promoter Score programs have become valuable data sources that extend across the full customer journey. They provide the analytics needed for functional groups across all customer touchpoints to find ways to optimize the brand experience while delivering on ROI.

Other quantitative data include search and social media analytics, which offer a data-driven way to target customers based on key words in searches, posts, follows, and likes. Using quantitative data to make inferences about where a person is in the customer journey allows brands to serve up appropriate content. As customers search, post, or engage in social media that indicates they are embarking on a path to purchase in some category, digital analytics allow brands to present their persuasive or informational content to influence that path. As e-commerce continues its exponential growth and each aspect of a customer’s journey becomes more measureable, brands can optimize their targeting and content based on real-time results.

Finally, brands can also use customer relationship management (CRM) platforms for journey-related insights. Consider how such data may identify unhappy customers and pinpoint where the customer experience and brand promise failed. A luxury travel company captures every customer’s individual stays in its CRM, including e-mails to the company, satisfaction ratings, and account manager notes and observations. This allows the company to not only break down individual stays but also to look for trends by property, account manager, and the like, and to develop operational, product, and communication improvements as needed.

Alternatively, aggregate CRM data on where the sales funnel shows lower conversions or drop-offs may indicate where processes and messaging are failing. For example, high pre-qualification but low subsequent conversion could indicate poor messaging or inadequate product features and benefits. High initial sales or subscriptions and decreasing repeat sales or renewals could indicate product, implementation, or account servicing problems.

Qualitative Methods: Relentless Pursuit of the “Why”

On their own, customers can rarely identify, solve, or suggest specific innovations to smooth friction points in their experience with any given brand. Particularly from a technology perspective, most people are generally constrained by what they know versus what might be possible, which is why demanding more from qualitative methods adds unique value.

Methodologically, in-home and in-context interviews, video and real-time diaries, shop-along trips, and the like continue to play a role, but the need to capture the variance in journeys requires researchers to increase their expertise in observing what a customer does or doesn’t say or do—and why they do or don’t. Researchers also must be skilled in generating interpretations, hypotheses, and potential implications on the fly to test with customers. Being able to recognize when tangents are fully veering off course versus heading toward a possible problem to solve or product idea has become more important than adhering strictly to a discussion guide.

Technology has enabled a plethora of mobile and digital data collection and insight-gathering opportunities. The qualitative research company dscout leverages diaries and field studies by giving people missions to capture on their phones.² GutCheck was an early pioneer in online focus groups.³ Both Validately and UserTesting enable moderated and unmoderated online product user testing, and FocusVision developed its Revelation app for insight gathering using a custom-created social media platform specific to the client.⁴

We could devote an entire chapter to outlining the wealth of competitors and technologies in this space. However, their power stems from the interpretation, not the tool per se. Elevating methods to keep them fresh and relevant in the digital era has less to do with new platforms and allegedly new methods. It’s more about constantly pushing observations and hypothetical implications to problems to be solved, which is not a task for customers, but is an ideal task for engineers and chefs and other creators.

Two examples from entrepreneurs separated by 100 years of history drive this point home. Legendary innovators Henry Ford and Steve Jobs both famously claimed to dislike and distrust customer insight, and focus groups in particular. Ford argued that if he asked consumers what they wanted, they would have asked for a faster horse, and Jobs complained that “people don’t know what they want until you show it to them.” Brilliant men, but the quotes illustrate why building brands requires qualitative research that does more than try to come up with an answer—it has to produce insight into the true problem customers want solved or the job they want done.

Walter Isaacson’s biography of Steve Jobs recounts an executive team meeting at Apple in which everyone tossed their phones on the table and griped about them with annoyance and in highly specific detail. (Sounds a lot like one of those maligned focus groups, doesn’t it?) The iPhone ultimately was born out of an innovative design process that sought to “fix” the many things that the Apple team found frustrating about their current phones.

Using qualitative methods to get raw input on frustrations and friction points lets researchers distill the insights into problems to solve, which in turn define the innovation challenges that engineers, entrepreneurs, and designers can use to guide their innovation work. And as the iPhone proves, sometimes those innovations change the world by addressing needs no customer could have articulated directly, but rather by addressing needs derived and interpreted based on the customer experience.

More aggressively exploring the “why” behind insights also helps elevate qualitative insights and renders them more broadly actionable. All qualitative insights experts worth their salt will argue that they have always done this. However, the need to generate implications from a more varied digital world raises the stakes and the importance of translating customer experience insights into product opportunities and angles for ever more critical differentiation. This challenge is made more difficult in an environment characterized by dense information availability.

For example, consider a premium specialty cheese brand whose makers created a new hard, Italian-style cheese that had crystals dispersed throughout the product. “The crystals set them apart from other cheeses” is a fine start as an insight that might result in a differentiated brand claim. But it needs more probing and definition to find a usable insight for a brand to use in messaging or product implications. “We’re the brand with crystals” is clearly too literal and does not help build the brand. If the crystals really do differentiate, how? And why do they matter? Is it just a matter of providing a different mouth feel, or is it about a flavor burst that injects a sensorial surprise into what is otherwise a uniformly consistent flavor experience? That ability to add flavor and experiential energy to a cheese could be an emotional benefit to leverage in brand positioning. It could also suggest ideal usage occasions to promote (e.g., transitions throughout the day in which a mental and physical kick are needed). Or, maybe a distinctive, multisensory cheese is more substantive and more like a meal (rather than a topping or a snack), which could open up increased usage volume and inspire different packaging, forms, and flavors from a product development perspective. Relentlessly pursuing the “why” can transform a blunt statement (the crystals are different) into more actionable opportunities.

In addition to even greater attention to the “why,” pushing for depth and dimension can offer differentiation insight to even parity-sounding concepts. Consumers and customers now demand innovation from everything from nut butters to automated conveyor systems, and qualitative methods must more meticulously and thoroughly unpack concepts that customers indicate are important to them, in order to characterize them in a way that offers differentiation potential.

For example, in-depth interviews with agricultural engineers and cotton ginners about what they wanted from a ginning equipment partner revealed that (no surprise) innovation mattered. Using an empathy lens, the interviews continued to probe on specifics, proof, and indicators of innovation, and to calibrate incremental versus full-scale invention takes on innovation. The insights gleaned allowed Lummus Ginning to craft a differentiated innovation story for itself, despite innovation claims being widespread in the ginning industry.

The Lummus version of innovation is risk averse and ROI-driven. Lummus uses a strategy of only innovating when it can make the cotton ginning process more profitable and efficient. It never indulges in product changes just for the sake of the new. Its innovation is based on a deep understanding of the trickle- down impacts on ginning of broad shifts in the cotton industry, such as trends in labor, seed oil markets, and technology developments in upstream cotton picking and stripping machinery. Lummus manages to innovate with engineering prowess and ingenuity in a category truly as old as dirt (cotton ginning has existed in some form since 12,000 BCE, give or take) to drive further profitability and improve lint quality for its cotton producer customers.

The elevation of qualitative insights in this example did not result from a new method. It came from leveraging every ounce of potency from traditional depth interviews by crafting follow-up questions, topics, and exercises to pose to customers until they produced new insights that could aid in differentiation.

Quantitative Methods: Harnessing the Power of Big Tech/Big Data

To make quantitative data work for brands in the digital environment, both technical data-science abilities and brand-application know-how are needed. Without that combination, companies run the risk of being seduced by tactical ideas that may not align with the longer-term strategic vision for the brand.

In addition, elevating quantitative insights requires ever-growing sophistication in assessing data quality, blending data, and performing analytics and visualizations, all with an eye toward finding leverageable insights that can optimize messaging and distribution channels to build brands.

The digital-era developments that have fueled quantitative insight opportunities stem from the big tech–big data combination. Big tech has provided the devices and apps that customers use to passively and actively generate prolific amounts of data. Neuroscience and cognitive psychology studies that examine attention and behavioral responses to stimuli have provided evidence of users’ immersive attention and participation in mobile software. Apps are designed to provide attentional triggers for action (e.g., sounds, notifications, etc.), and they reward and quantify the user’s continued engagement with the app, making continued engagement competitive and self-fulfilling. In fact, developers now view sleep as a primary competitor to time spent on apps. With 24 hours a day as the only true limiting factor, the world’s collective time spent on apps offers a profound opportunity for data collection relevant to brand building.

With the proliferation of the Internet, mobile devices, and apps, examples of brand-building quantitative data sources in the digital world include:

• Customer/member transactions
• Loyalty information
• Customer experience and customer satisfaction data
• Social media content and comments, likes, dislikes, and shares
• Third-party data (e.g., scanner, Rx prescribing data, government and industry reports)
• Site traffic, including time spent engaging with an app or site
• Geotagged location
• Click-through rates and sales conversions
• E-mails, videos, web site pages
• Salesforce and other CRM data

All of this big tech-enabled big data can provide descriptive data capture, and increasingly, machine-learning opportunities. Machine learning and natural language processing can develop and serve up adaptive and optimized content and experiences to customers, and increase brand adoption (B2C) and opt-in sales prospecting opportunities (B2B)—both of which are clearly a boon for building brands.

It is well beyond the scope of this chapter to assess different data science platforms or provide a guide to predictive analytics and the like. The primary takeaway of the potential of big tech/big data to elevate quantitative insights is this: with big data come big responsibilities in creating a healthy, cross-functional relationship between data/IT and marketing teams. To realize the brand-building potential of quantitative insights, the data wizards and the brand strategy stakeholders must harness their joint ownership of the customer experience to use data-driven insight to propel brands and revenue forward.