OFFER PERSONALIZATION USING TEMPORAL CONVO-LUTION NETWORK AND OPTIMIZATION

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.

1. INTRODUCTION

In most retail settings, promotions play an important role in boosting the sales and traffic of the organisation. Promotions aim to enhance awareness when introducing new items, clear leftover inventory, bolster customer loyalty, and improve competitiveness. Also, promotions are used on a daily basis in most retail environments including online retailers, supermarkets, fashion retailers, etc. A typical retail firm sells thousands of items in a week and needs to design offer for all items for the given time period. Offer design decisions are of primary importance for most retail firms, as optimal offer roll out can significantly enhance the business' bottom line. Most retailers still employ a manual process based on intuition and past experience of the category managers to decide the depth and timing of promotions. The category manager has to manually solve the promotion optimization problem at consumer-item granularity, i.e., how to select an optimal offer for each period in a finite horizon so as to maximize the retailer's profit. It is a difficult problem to solve, given that promotion planning process typically involves a large number of decision variables, and needs to ensure that the relevant business constraints or offer rules are satisfied. The high volume of data that is now available to retailers presents an opportunity to develop machine learning based solutions that can help the category managers improve promotion decisions. In this paper, we propose deep learning with multi-obective optimization based approach to solve promotion optimization problem that can help retailers decide the promotions for multiple items while accounting for many important modelling aspects observed in retail data. The ultimate goal here is to maximize net revenue and consumer retention rate by promoting the right items at the right time using the right offer discounts at consumer-item level. Our contributions in this paper include a) Temporal Convolutional Neural Network architecture with hyperparameter configurations to predict the item purchase probability at consumer level for the given time period. b) Design and implementation of F 1 -maximization algorithm which optimises for purchase probability cut-off at consumer level. c) Methodology to estimate offer elasticity of purchase probability at consumer item granularity. d) Constraint based optimization technique to estimate optimal offers at consumer-item granularity.

