Moving Beyond Next: Predictive Intelligence and the Customer Lifecycle
Predictive intelligence can once again help you uncover deeper insights about customers in this stage of the customer lifecycle. Based on what we know about those customers who eventually did return — as well as what we know about those who did not – what is the probability that a currently lapsed customer can be reactivated? Knowing exactly who these customers are can help you develop more laser-focused reactivation tactics in the hope of re-engaging them with your brand.
Beyond the Data
“Predictive intelligence” is more than a buzzword – it encompasses a powerful set of models, methods, and tools for forecasting future customer behavior based on historical data. Sure, predictive models can be used for tactical purposes, to help determine what to do next, but they should also be used to drive insights that inform broader strategies – customer development, retention, and reactivation – based on the stages of the customer lifecycle. To get the most business value out of our predictive intelligence capabilities, we need to move beyond next.
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.