Price incentives for online retailers using social media

Abstract

In the era of Big Data, online retailers have access to a large amount of data about their customers. This data can include demographic information, shopping carts, transactions and browsing history. In the last decade, online retailers have been leveraging this data to build a personalized shopping experience for their customers with targeted promotions, discounts and personalized item recommendations. More recently, some online retailers started having access to social media data: more accurate demographic and interests information, friends, social interactions, posts and comments on social networks, etc. Social media data allows to understand, not only what customers buy, but also what they like, what they recommend to their friends, and more importantly what is the impact of these recommendations. This work is done in collaboration with an online marketplace in Canada with an embedded social network on its website. We study the impact of incorporating social media data on demand forecasting and we design an optimized and transparent social loyalty program to reward socially active customers and maximize the retailer's revenue. The first chapter of this thesis builds a demand estimation framework in a setting of heterogeneous customers. We want to cluster the customers into categories according to their social characteristics and jointly estimate their future consumption using a distinct logistic demand function for each category. We show that the problem of joint clustering and logistic regression can be formulated as a mixed-integer concave optimization problem that can be solved efficiently even for a large number of customers. We apply our algorithm using the actual online marketplace data and study the impact of clustering and incorporating social features on the performance of the demand forecasting model. In the second chapter of this thesis, we focus on price sensitivity estimation in the context of missing data. We want to incorporate a price component in the demand model built in the previous chapter using recorded transactions. We face the problem of missing data: for the customers who make a purchase we have access to the price they paid, but for customers who visited the website and decided not to make a purchase, we do not observe the price they were offered. The EM (Expectation Maximization) algorithm is a classical approach for estimation with missing data. We propose a non-parametric alternative to the EM algorithm, called NPM (Non-Parametric Maximization). We then show analytically the consistency of our algorithm in two particular settings. With extensive simulations, we show that NPM is a robust and flexible algorithm that converges significantly faster than EM. In the last chapter, we introduce and study a model to incorporate social influence among customers into the demand functions estimated in the previous chapters. We then use this demand model to formulate the retailer' revenue maximization problem. We provide a solution approach using dynamic programming that can deal with general demand functions. We then focus on two special structures of social influence: the nested and VIP models and compare their performance in terms of optimal prices and profit. Finally, we develop qualitative insights on the behavior of optimal price strategies under linear demand and illustrate computationally that these insights still hold for several popular non-linear demand functions.