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Marketing Communication Channel Preference Optimization Using a Two-Stage Statistical Modeling (303653)

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Keywords: Channel Preference Model, Optimization, Predictive Modeling, Statistical Simulations

In today’s sophisticated world where different customers behave in different unique ways and expect retailers to relate and communicate with them in their preferred way.Whether retailors want to communicate coming up new offers, rewards earned, new store opening, life stage offers such as customer’s birthday, there is a need to communicate through a channel that will be appreciated by the customers and motivate the needed action. On the other side of the story, there are significance cost differences among different communication channels. How do retailers handle this constraint optimization situation? In our paper, we discuss a novel two stage channel optimization model that predicts each customers preferred channel of optimization in the first stage and incorporates communication cost in the second stage to optimize overall marketing communication strategy. Leveraging customer transactional data, demographic data and campaign history data, our approach leverages machine learning techniques and statistical simulations to arrive at optimal communication strategies that help us to engage with different customer groups using their most preferred way under cost constrain.