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Periodic Model Monitoring Framework for Marketing Models

About this paper

Response or propensity models are widely used by businesses to identify customers who are likely to purchase a product. To help develop these models, techniques like logistic regression and decision trees have been deployed across verticals such as banking, telecom, and retail. Advanced algorithms, such as random forest, gradient boosting techniques, and neural network models are also used for classifying outcomes, such as responder vs non-responder and good vs bad. Since propensity models are extremely important to a business, a lot of attention is paid to statistical validation of the selected model before it is deployed. Most importantly, even after the model has been validated and found to be robust, periodic monitoring is imperative to ensure that the model is performing at peak efficiency over a course of time. Ongoing monitoring is also required to determine whether changes in market conditions or business strategies demand adjustment, redevelopment, or replacement of the model.

Several metrics are used to monitor the model’s performance and its validity. This paper discusses the different ways of confirming the model’s validity. In addition, it attempts to answer the question of when to retire a model.

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