Big data and design have left their mark on various industries and business functions and now have their sights on customer experience. Data helps us uncover valuable opportunities worth solving, while design thinking guides us to a deeper understanding and more meaningful, user-centric solutions. In the year ahead, the marriage of data and design will offer powerful new ways for companies to build stronger customer relationships and drive growth.
Predictive Analytics Goes Mainstream
Large SaaS-based companies like Amazon have been reaping the benefits of big data and predictive analytics to drive business growth for years. For example, Amazon’s recommendations are powered not only by your past viewing and purchasing data but also by the data from thousands of other shoppers like you.
In 2016, we’ll see an expansion of analytics across two dimensions—across companies of all types—large and small, B2B and B2C, traditional software and SaaS—as well as beyond the traditional sales funnel and throughout the entire journey a customer has with their vendor. This includes being able to predict likelihood of churn, increase customer references, identify up/cross-sell opportunities, and ultimately maximize a customer’s total lifetime value. Most importantly, it helps ensure that customers are receiving the most value and success possible.
I’ve seen the power of predictive analytics applied to various stages of the journey a customer has with their vendor. One example from a traditional software vendor is specific to the technical support stage of a customer’s journey. In this example, a model was developed using dozens of attributes that predict both individual support requests and entire customer accounts that are likely to become critical, and used this information to alert the appropriate teams to assist.
As a result, they saw significant reductions in the number of customers reaching this critical status and an increase in overall customer satisfaction. Other experiments centered on being able to predict which additional capabilities of a product already owned by a customer might prove valuable based on the experience of similar customers. The Head of Data Science at Citrix, Mike Stringer, captures it perfectly:
“Data Science has spread down the sales funnel and into the customer success funnel. Companies failing to capitalize are at a huge disadvantage in the Customer Success arena.”
However, many companies have yet to move beyond traditional sales funnel use cases—or to work with predictive analytics at all. There are three reasons why this is about to change.
- The ongoing shift to subscription-based models. The promise of shorter sales cycles with stable recurring revenue combined with the increase in demand for pay-as-you-go OpEx purchasing are driving many traditional software companies to the cloud or to subscription-based licensing. Adobe, Autodesk and Microsoft are just a few examples. And even those who aren’t embracing these new models are feeling the consumer pressure to show that they care about the post-sale customer experience as much as the pre-sale customer experience.
- The amazing surge of big data startups from the past few years. Now hitting their stride, these companies are making data science accessible and easy for companies of all sizes, and focusing on extremely valuable use cases—including customer success. Keep an eye on GainSight, Totango, and Preact, who have all have zeroed in on this extremely fast-growing market opportunity.
- The notable increase in the number of Chief Customer Officer, VP of Customer Success, and similar roles over the past two years. While these roles may have differences in operational responsibilities, they all share a similar set of performance metrics that are grounded in customer value and success. These leaders will look to predictive analytics to be successful in their role.
This is great news for businesses of all kinds. Practical and applicable big data has arrived. As it enters the fast-growing space known as Customer Success, it will bring amazing capabilities to drive customer loyalty and business growth.
But there is one caveat: Being able to predict the future doesn’t necessarily mean that you can intervene and change it. I was once told about an interesting pattern discovered during the 30-day trial period of a SaaS product. Data scientists determined that if a customer used the product during week three of their trial period—regardless of their usage during weeks one, two or four—they were far more likely to convert into paying customers. The team celebrated these findings and began firing off email nurture campaigns that successfully increased week three usage. The impact on conversions: none.
The lesson we learned is well worth taking to heart: While data is amazing at correlating behavior and outcomes, it doesn’t uncover the underlying motivation. In this example, the teams never explored the “why” behind week three usage, and was thus unable to transform this information into true insight.
Predictive Data Meets Design
In my 2015 article in UX Magazine, Design Thinking and Lean Soup Isn’t Just for Start-Ups, I predicted that in 2015 we would see the approaches and philosophies I outlined adopted by companies of all sizes. My prediction was accurate, but to my surprise, the adoption of design thinking extended beyond large enterprise companies into sectors I hadn’t anticipated.
For example, in the financial industry, a series of design deals included Capital One’s acquisition of Adaptive Path in late 2014 followed by BBVA’s acquisition of Spring Studio in April of 2015. Industry giants also continue to invest heavily in design. IBM is on a mission, since 2012, to hire 1,000 designers, while GE opened a design studio in 2014 and is recruiting heavily in the field. Most surprisingly is the pattern of acquisition that continues in professional services firms, with Accenture’s acquisition of Fjord in 2013, and KPMG’s acquisition of Cynergy and BCG’s of Strategic & Creative in 2014. Last year, McKinsey and Wipro both jumped into the design game with acquisitions of Lunar and Designit.
Design is clearly here to stay—these professional services companies wouldn’t be investing if the demand didn’t support it. Now bring data into the picture and something even more powerful becomes possible. The best term I’ve seen applied to this is "hybrid insights,” which I first saw used in Tom and David Kelly’s book Creative Confidence. They write, “Hybrid insights allows us to embed stories in the data, bringing the data to life. It brings the ’why’ and the ’what’ together.”
In the “week three” example I shared earlier, only the “what” was provided by the data. It was not a hybrid insight. Because the “why” was never understood, they ended up following a path that had no affect on trial conversion rates. A hybrid approach blending the qualitative research and problem reframing strengths of design thinking could have sent the team down a very different and more successful path.
The blend of data and design is a match made in heaven for understanding and solving problems in a meaningful, user-centric way. Tony Costa summarized this well in a recent Forrester paper, “How CX Pros Innovate.”
“Big data won’t replace ethnography. Rather, it’ll complement it by revealing unknown insights, pushing researchers to pursue new avenues of investigation that were not viable before.”
As design thinking and now data science make their way more deeply into the fields of customer experience and customer success, companies who master these new approaches will pull away from the pack. Even companies lacking strong in-house data or design skills can benefit from this trend, as more and more customer experience-focused big data startups and professional services companies enter the field.
Image by Steve Silvas.