期刊
MARKETING SCIENCE
卷 39, 期 5, 页码 956-973出版社
INFORMS
DOI: 10.1287/mksc.2020.1229
关键词
defection; field experiments; loss function; machine learning; proactive churn management; profit lift; stochastic gradient boosting
类别
资金
- Veni Personal Grant from the Dutch National Science Foundation (NWO) [451-09-005]
- Vidi Personal Grant from the Dutch National Science Foundation (NWO) [452-12-011]
Customer defection threatens many industries, prompting companies to deploy targeted, proactive customer retention programs and offers. A conventional approach has been to target customers either based on their predicted churn probability, or their responsiveness to a retention offer. However, both approaches ignore that some customers contribute more to the profitability of retention campaigns than others. This study addresses this problem by defining a profit-based loss function to predict, for each customer, the financial impact of a retention intervention. This profit-based loss function aligns the objective of the estimation algorithm with the managerial goal of maximizing the campaign profit. It ensures (1) that customers are ranked based on the incremental impact of the intervention on churn and postcampaign cash flows, after accounting for the cost of the intervention, and (2) that the model minimizes the cost of prediction errors by penalizing customers based on their expected profit lift. Finally, it provides a method to optimize the size of the retention campaign. Two field experiments affirm that this approach leads to significantly more profitable campaigns than competing models.
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