Human-centered design thinking has influenced much of the way that companies think about user and customer experience, and for the better. Because customer experience is becoming an important vehicle through which brand propositions are communicated today, it is worth examining if the way we design customer experiences can be improved. Particularly, is there a way to better integrate data and analytics into design thinking?
A well-designed customer experience offers many benefits, such as:
- increasing the productivity of users and service efficiency.
- Making solutions easier to use and, therefore, reducing support costs
- Increased accessibility and reducing discomfort and stress
- Signature experiences that convey and re-enforce the brand proposition
In order to achieve these results, most experience design processes begin with deep empathy, which entails physically observing, interviewing and surveying customers to uncover unmet needs and pain points.
These methods often help uncover significant opportunities to improve the customer services. Just as often, however, they take companies down unprofitable journeys and fail to identify growth opportunities.
For example, Spirit airlines probably ignores every stated customer desire except price (in most cases), yet it has a very strong business model. Can you imagine the market research that says customers don’t care about on-time arrival, service or cabin comfort and want to be nickeled and dimed for every possible amenity? An examination of behavioral data, however, would show that there is a large market of travelers who consistently shop for the cheapest flight, regardless of service, brand and reputation, and Spirit has learned to cater to this segment very well.
In my view, most experience design projects fail to bring in behavioral data and resultingly miss the bigger opportunity. I have observed many customer experience projects that try desperately to empathize with the customer, but fail to examine if this is the customer they want and what their purchase and usage behaviors truly reveal.
Sometime back, my team and I were asked to identify key factors driving retention and renewal behavior among auto and home insurance customers. Certainly, survey-based feedback was helpful and identified areas of dissatisfaction, such as complicated billing, poor claims experiences and unexplained rate increases. Individual customer interviews yielded even more interesting satisfaction drivers, such as financial trust and need for honest advice. However, looking at behavioral data, such as the types of policies purchased, tenure of the policies and household makeup actually uncovered the deepest insights. Although this is now common knowledge in the insurance industry, customers who bundle auto and home policies are much less likely to switch. Therefore, most insurance carriers try to offer an Auto-Home discount. Other behaviorally observed factors, such as the level of coverage selected and signing up for auto pay are also significant predictors of retention. Surprisingly, none of these factors bubbled up directly in customer interviews or surveys. Furthermore, factors derived from the behavioral data explained 70 to 80 percent of the attrition in any given year.
Despite this example, it would be very wrong to assume that human-centered design principles do not work or that some of the methods employed to develop user/customer empathy are bunk. However, I would say that interviews and experience audits are only one source of customer insight; mining customer behavioral data is another powerful source of customer insights. A well-thought-out experience design should have the benefit of both.