Offer Personalization using Temporal Convolution Network and Optimization
This addresses the challenge of reducing promotional costs while maintaining sales for online retailers, though it is incremental as it applies known methods to a specific domain.
The paper tackles the problem of optimizing personalized offers in retail by predicting item purchase probabilities using a Temporal Convolutional Network and then using these predictions to estimate offer-elasticity and optimize offers via constraint-based optimization, resulting in improved balance between transactions and profit across categories.
Lately, personalized marketing has become important for retail/e-retail firms due to significant rise in online shopping and market competition. Increase in online shopping and high market competition has led to an increase in promotional expenditure for online retailers, and hence, rolling out optimal offers has become imperative to maintain balance between number of transactions and profit. In this paper, we propose our approach to solve the offer optimization problem at the intersection of consumer, item and time in retail setting. To optimize offer, we first build a generalized non-linear model using Temporal Convolutional Network to predict the item purchase probability at consumer level for the given time period. Secondly, we establish the functional relationship between historical offer values and purchase probabilities obtained from the model, which is then used to estimate offer-elasticity of purchase probability at consumer item granularity. Finally, using estimated elasticities, we optimize offer values using constraint based optimization technique. This paper describes our detailed methodology and presents the results of modelling and optimization across categories.