Brand-new customers pose an interesting challenge to marketers in this era of big data. Marketing strategy and tactics are often driven by valuable insights gleaned from past customer data collected over time from repeat purchases and transactions. But newly acquired customers arrive without a data trail. Is it possible to predict the future behavior of new customers?
As a doctoral student in marketing, Arun Gopalakrishnan seized on this challenge to create a new computer model to provide guidance to managers who want, and need, to forecast newly acquired customers’ behaviors. Gopalakrishnan began this research as part of his doctoral studies at the Wharton School of the University of Pennsylvania and completed the research at Olin Business School where he is an assistant professor of marketing. His paper won the 2017 Olin Award for faculty research that impacts business.
Working with two professors at Wharton, Gopalakrishnan developed two cross‐cohort models (called vector changepoint models) that introduce a new framework for analyzing data that reveals insights into patterns of customer behavior over time.
Specifically, the new models reject the notion that pooling data from all previous customers to make an educated guess about the behavioral patterns of the newest customers provides an accurate forecast. In other words, the researchers found that new customers are not simply going to behave like the “average” existing customer. That assumption, according to the researchers, “ignores the potentially changing behavioral patterns” from one set of customers acquired during a certain time period to another.
The new mathematical model takes into account what it calls “regime changes” or past customer behavior changes that were influenced by new firm policy, government regulations, economic factors, competitors’ actions, or unknown drivers of change.
“Our findings suggest that simply using older cohorts [sets of customers acquired in the past] as a proxy for predicting new cohorts without understanding any potential regime changes may lead to inaccurate predictions because certain aspects of customer behavior may have changed, going from the oldest customer cohort to the newest one.”
When tested against other models, the Olin Award–winning research model/forecast tool outperforms other benchmarks. It can be applied to any industry that acquires customers who engage in repeat transactions over time. The new model also simplifies the process of mining the data.