Moving Beyond Next: Predictive Intelligence and the Customer Lifecycle
Alternatively, however, you could consider changing the question: Based on what this customer purchased the first time, what is the probability that this customer will become one of my best customers? Which new customers have the highest potential to become my most fervently loyal brand fanatics? By studying what is known about your best customers, and then using predictive intelligence to forecast which members of the new customer group are likely to follow the same path to brand loyalty, it provides a whole new way of thinking about which customers to invest in over the long run – not just what to do next.
Similarly, consider your active customers, who are no longer new and have purchased multiple times from you, illustrating some level of engagement with your brand. You could use predictive intelligence to ask a related question to the above: Based on this customer’s pattern of interactions with my brand, whether transactions or other measurable attributes of behavior, what is the probability that this customer will become one of my best customers? Predictive modeling can also help you from the other direction: Based on their observed pattern of interactions with the brand, which of my active customers are exhibiting a high likelihood of lapsing? These customers may need a little extra encouragement to stay connected with your brand, and predictive intelligence can help you identify who they are so that you can test various tactics to retain them.
Finally, consider your lapsed customers, who were your active customers at one point in time — but sadly, that time has long passed. Some of these customers may return someday — perhaps their needs changed temporarily, and after some time passes and they shift back to their original need state, your brand is immediately top of mind. But some of these customers have unfortunately drifted away from your brand — you’ve lost them as customers, possibly forever.
Jim Sawyer is Chief Scientist at Elicit. The company's resident savant, Jim is responsible for the artistic application of Elicit’s customer science. From evaluating the state of customer data and analytics systems to developing customized segmentation, Jim leads a team of data scientists to bring customers to life through data. He has over 20 years of experience in analytics, a Stanford B.A.S. in Mathematical and Computational Sciences and a Georgia Tech M.S. in Industrial and Systems Engineering. Elicit's Fortune 500 clients include Southwest Airlines, Fossil, GameStop, Sephora, BevMo!, HomeAway, Best Buy and Pier 1 Imports.