Data-driven Decision Making in Online and Offline Retail

Book excerpt: .Retail operations have experienced a transformational change in the past decade with the advent and adoption of data-driven approaches to drive decision making. Granular data collection has enabled firms to make personalized decisions that improve customer experience and maintain long-term engagement. In this thesis we discuss important problems that retailers face in practice, before, while and after a product is introduced in the market. In Chapter 2, we consider the problem of estimating sales for a new product before retailers release the product to the customer. We introduce a joint clustering and regression method that jointly clusters existing products based on their features as well as their sales patterns while estimating their demand. Further, we use this information to predict demand for new products. Analytically, we show an out-of-sample prediction error bound. Numerically, we perform an extensive study on real world data sets from Johnson & Johnson and a large fashion retailer and find that the proposed method outperforms state-of-the-art prediction methods and improves the WMAPE forecasting metric between 5%-15%. Even after the product is released in the market, a customer's decision of purchasing the product depends on the right recommendation personalized for her. In Chapter 3, we consider the problem of personalized product recommendations when customer preferences are unknown and the retailer risks losing customers because of irrelevant recommendations. We present empirical evidence of customer disengagement through real-world data. We formulate this problem as a user preference learning problem. We show that customer disengagement can cause almost all state-of-the-art learning algorithms to fail in this setting. We propose modifying bandit learning strategies by constraining the action space upfront using an integer optimization model. We prove that this modification can keep significantly more customers engaged on the platform. Numerical experiments demonstrate that our algorithm can improve customer engagement with the platform by up to 80%. Another important decision a retailer needs to make for a new product, is its pricing. In Chapter 4, we consider the dynamic pricing problem of a retailer who does not have any information on the underlying demand for the product. An important feature we incorporate is the fact that the retailer also seeks to reduce the amount of price experimentation. We consider the pricing problem when demand is non-parametric and construct a pricing algorithm that uses piecewise linear approximations of the unknown demand function and establish when the proposed policy achieves a near-optimal rate of regret ( O)( [square root of] T), while making O(log log T) price changes. Our algorithm allows for a considerable reduction in price changes from the previously known O(log T) rate of price change guarantee found in the literature. Finally, once a purchase is made, a customer's decision to return to the same retailer depends on the product return polices and after-sales services of the retailer. As a result, in Chapter 5, we focus on the problem of reducing product returns. Closely working with one of India's largest online fashion retailers, we focus on identifying the effect of delivery gaps (total time that customers have to wait for the product they ordered to arrive) and customer promise dates on product returns. We perform an extensive empirical analysis and run a large scale Randomized Control Trial (RCT) to estimate these effects. Based on the insights from this empirical analysis, we then develop an integer optimization model to optimize delivery speed targets.