I recently presented the Predictive Customer Intelligence solution from IBM to a group of executives at the Memorial Golf Tournament in Dublin, Ohio. The audience spanned the retail, insurance and banking industries and all had one common challenge: how can we “do better” by knowing our customers and making smarter, more targeted offers? Some in the group have active analytics programs, while others recognize the need to get started.
My story – and that’s where the best predictive analytics discussions begin – was about how customer analytics can transform a customer experience for the better and create significant business value in the process. The executives in the room asked some great questions – the answers to which can certainly help others thinking about customer analytics. I’ve summarized the top five questions from the event below.
How do you build the case for 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 next best offer.
What portions of the analytics implementation did you control directly? What was the span of control?
This question was from a VP of Marketing who was challenged with getting the idea of improved customer analytics to spread within his organization. He saw the opportunity, but needed buy in and support from others in the company. Champions of predictive analytics often sit within line of business, like marketing, within the overall organization so this is a common scenario. The answer is in the question above – 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 you the support you want, so its 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 with an incentive. We can identify these customers through predictive modeling and manage the overall offer budget through an optimization process.”
Who comes up with the use cases?
Initially the internal analytics champion needs to steer the conversation. With help from partners like Revelwood and IBM, that champion must paint the vision of how business can be transformed using this technology – and the vision needs to be specific and detailed. As your analytics program progresses you’ll find that everyone in the organization from front line employees on up will begin thinking about how predictive, optimization and statistics can help guide customer interactions to a better outcome for all. In the most effective program I’ve seen, the marketing analytics group set up a bottoms-up feedback system to collect new ideas for vetting and development.
Do we need a data scientist?
This is actually the wrong question. A better question is when do I need a data scientist. The tools and technology around predictive analytics and customer intelligence have progressed to the point that a company can collect early wins and drive results by having an internal analytics champion and users who understand two things: their own business and their own data. As your program moves forward and tackles more complex use cases and looks to drive value from additional predictive accuracy, you should look into a statistical professional.
How much data do we need?
A common question. It’s rarely about how much data is needed to get started on an analytics project – rather its about having the right data. For example, if you need to predict 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 CRM and pricing data. Going wide before deep will steer you in the right direction most of the time – at least until you need a data scientist.
