Predictive customer analytics: Your top 5 questions answered

Predictive Analytics Practice Leader, Revelwood

Over the past several months, I have had many discussions with executives regarding the benefits of predictive customer analytics. These executives lead organizations in the banking, retail, insurance and telecommunications industries. And each of them was facing a common challenge: how to get to know customers and make them smarter, more targeted offers?

Some organizations have active analytics programs, while others that don’t have them recognize the need to get one started. My story, and where the best discussions about predictive analytics begin, is about how customer analytics can transform and enhance customer experience as well as create significant business value in the process. The executives I spoke with asked some great questions; the answers to those questions can help others who are planning their predictive customer analytics journey. To illustrate, here are the top five questions gleaned from meetings with these executives. How do you build the case for the next best offer?

Building the business case for a new system or investment can be challenging, but it all comes back to measurable results. Every company that is working with customers on a day-in, day-out basis has interactions that are being—or can be—measured. Anything that can be measured can be improved; so to build the case, look at where improved customer interactions can make the biggest impact. Where and when are you touching the most customers? Are these repeatable transactions? What does your business want to sell more of? Answer these questions, and you’ll have the beginnings of a case to invest in the next best offer.

2. What portions of the analytics implementation did you control directly, and what was the span of control?

A vice president of marketing who was challenged with getting the idea of improved customer analytics to spread within the organization asked this question. This executive saw the opportunity but needed buy in and support from others in the company. Champions of predictive analytics often sit within a line of business, such as marketing, that is within the overall organization, so this scenario is common. The answer to these questions are in the answer to the previous question: have a defined business case for analytics, and take it to others in the company to solicit support. Saying “we should do analytics” won’t get the support that is needed; it pays to be specific. Approach others with a specific change and result—for example, “We want to improve customer retention by empowering sales reps to make retention offers to customers statistically at risk and who are most likely to stay when given an incentive. We can identify these customers through predictive modeling and manage the overall offer budget through an optimization process.”

3. Who comes up with the use cases?

Initially, the internal analytics champion needs to steer the conversation. With help from partners such as Revelwood and IBM, that champion needs to paint the vision of how business can be transformed using this technology—and the vision needs to be specific and detailed. As an analytics program progresses, everyone in the organization from front-line employees on up will begin thinking about how predictive analytics, optimization and statistics can help guide customer interactions toward enhanced outcomes for all. In the most effective program I’ve seen, the marketing analytics group establishes a bottom-up feedback system to collect new ideas for vetting and development.

4. Do we need a data scientist?

This question is actually the wrong question. A better question than this one is when do I need a data scientist? The tools and technology for predictive analytics and customer intelligence have progressed to the point at which companies can collect early wins and drive results by having an internal analytics champion. They also need users who understand two things: their own business and their own data. As a program moves forward, tackles more complex use cases and looks to drive value from additional predictive accuracy, the time is ripe to look into a statistical professional.

5. How much data do we need?

This question is quite common. Rare is the case in which the issue is a matter of how much data is needed to get started on an analytics project; rather, it’s about having the right data. For example, if you need to predict the purchase propensity of a given product, you don’t need years’ worth of data on all your customers. Instead, focus on a few months of purchase transactions combined with customer relationship management (CRM) and pricing data. Going wide before going deep can steer you in the right direction most of the time—at least until you need a data scientist.

Hear from the experts

IBM experts and IBM partner Revelwood will be presenting a three-part webinar series on understanding the value and opportunity that analyzing customer data and integrating predictive customer analytics into operations can unlock for organizations. Learn how to leverage predictive customer intelligence (PCI) and customer analytics for growth, reinvent customer service and satisfaction processes, understand customer lifetime value and improve segmentation tactics. Register for the September 23, 2015 webinar today. And find out more about transforming data to help improve operations with IBM solutions.