Price Investment using Prescriptive Analytics and Optimization in Retail
Linsey Pang: Walmart Labs; Avinash Thangali: Walmart Labs; Karthick Gopalswamy: Walmart Labs; Ketki Gupta: Walmart Labs; Dnyanesh Kulkarni: Walmart Labs; Sunil Potnuru: Walmart Labs; Supreeth Shastry: Walmart Labs; Harshada Vuyyuri: Walmart Labs; Timothy Winters: Walmart Labs; Prakhar Mehrotra: Walmart Labs
As the world’s largest retailer, Walmart’s core mission is to save people money so they can live better. We call the strategy we use to accomplish this goal our Every Day Low Price strategy. By keeping operational expenses as low as possible, we can continually apply a downward pressure on our prices, in turn increasing the amount of traffic, and ultimately, sales within our stores. In this paper, we apply Machine Learning (ML) algorithms and Operations Research techniques for forecasting and optimization to build a new price recommendation system, which improves our ability to generate price recommendations accurately and automatically. Comprised of a demand forecasting step, two optimizations, and causal inference analysis, our system was evaluated in the form of forecast backtests and live pricing experiments, both of which suggested that our approach was more effective than the current rule-based pricing system.
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