Offer Personalization using Temporal Convolution Network and Optimization

TL;DR

This paper introduces a novel approach combining Temporal Convolutional Networks and optimization techniques to personalize offers in retail, aiming to maximize profit while balancing transaction volume.

Contribution

It presents a new integrated framework for offer personalization that models purchase probability and optimizes offers at a granular level using deep learning and constraint-based optimization.

Findings

Model accurately predicts purchase probabilities across categories.

Optimized offers improve profit margins.

Framework adaptable to various retail categories.

Abstract

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.