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383. Profit-driven churn prevention through predict-and-optimize
Invited abstract in session WA-31: Analytics for Decision Making, stream Analytics.
Wednesday, 8:30-10:00Room: 046 (building: 208)
Authors (first author is the speaker)
| 1. | Nuria Gómez-Vargas
|
| Statistics and Operations Research, Universidad de Sevilla | |
| 2. | Sebastian Maldonado
|
| Department of Management Control and Information Systems, University of Chile | |
| 3. | Carla Vairetti
|
| Universidad de los Andes |
Abstract
In this work, we introduce a novel predict-and-optimize method for profit-driven churn prevention. We frame the task of targeting customers for a retention campaign as a regret minimization problem. The main objective is to leverage individual customer lifetime values (CLVs) to ensure that only the most valuable customers are targeted. In contrast, many profit-driven strategies focus on churn probabilities while considering average CLVs. This often results in significant information loss due to data aggregation. Our proposed model aligns with the guidelines of Predict-and-Optimize (PnO) frameworks and can be efficiently solved using stochastic gradient descent methods.
Keywords
- Machine Learning
- Analytics and Data Science
Status: accepted
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