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Spotting Influential Customers for Targeted Offers: From Social to Nonsocial

Wednesday, October 3, 2018 - 9:00am - 9:45am
Keller 3-180
Georgia Perakis (Massachusetts Institute of Technology)
The growing trend in online shopping in recent years has given rise to a wealth of data that was not available before and hence providing retailers new opportunities to personalize their services to individual customers such as personalized services include targeted promotions. As a side benefit, knowledge of individual customer behavior can also help improve sales forecasting. To develop consumer targeted strategies, we first need to develop a demand forecasting model that captures trends between customers (or groups of consumers). Understanding how customers have the potential to influence each other (directly or indirectly) and create trends that lead to purchases of particular products is important when deciding on targeting promotions to the most influential customers.

Using the customers’ purchase information, we develop a personalized demand model that incorporates potential trends between groups of customers based on their transaction history. Currently, and to the best of our knowledge, there are no methods widely available in the industry that incorporate customer-to-customer trends and influences in demand modeling, especially in the absence of information beyond customer transactions. Unlike previous models, the customer demand estimation model can rely on a minimum of transaction data if, for example, social data is not available. In addition, the demand model we propose incorporates potential indirect and direct customer to customer trends in an interpretable manner. Furthermore, we test the customer demand model we propose with data from an Oracle client and the results improve the quality of prediction (WMAPE) around 3-15 percent relative to traditional demand forecasting models.

The richer personalized demand forecasting model we propose also allows us to determine how to offer targeted promotions to particular customers in order to improve profits. As a result, we also propose and examine an optimization model for targeted promotion offerings. Model we develop is scalable and hence can be solved quickly. Furthermore, when tested with data from a large fashion retailer, our targeted promotion optimization model shows profit improvements of the order of 3-10 percent relative to the client’s current practice.

Joint work with Lennart Baardman and Tamar Cohen-Hillel from MIT ORC and Setareh Borijan-Bourjeni and Kiran Panchamgam from Oracle RGBU (Retail Global Business Unit)