Analytics for an Online Retailer: Demand Forecasting and Price Optimization

By Kris Johnson Ferreira, Bin Hong Alex Lee, David Simchi-Levi. Manufacturing & Service Operations Management, 2016

505 citations recorded by the source corpus.

We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories. One of the retailer's main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, we use machine learning techniques to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product's demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. We develop an algorithm to efficiently solve the subsequent multiproduct price optimization that incorporates reference price effects, and we create and implement this algorithm into a pricing decision support tool for Rue La La's daily use. We conduct a field experiment and find that sales does not decrease because of implementing tool recommended price increases for medium and high price point products. Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of [2.3%, 17.8%].

Subfields: Management Information Systems, Management Science and Operations Research, Marketing

View Paper: https://www.semanticscholar.org/paper/6baf7e0f23bbf4caffddb743215e9828ed732c9a

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Citations in the corpus

Smart "Predict, then Optimize" (2017, background): “…(see Angalakudati et al. (2014), Chan et al. (2012), Deo et al. (2015), Gallien et al. (2015), Cohen et al. (2017), Besbes et al. (2015), Mehrotra et al. (2011), Chan et al. (2013), Ferreira et al. (2015) for recent examples and expositions of Simchi-Levi (2013) and den Hertog and Postek (2016)).”
Generative artificial intelligence (2023, background): “…sustainability (Piccialli et al., 2021; Schoormann et al., 2023; Strobel et al., 2023), to electronic markets, where DL can optimize pricing, serve as recommendation systems, forecast demands, and detect fake consumer reviews (Ferreira et al., 2016; M. Li et al., 2022; Zhang et al., 2023b).”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, background): “Johnson, Lee, and Simchi-Levi (2016) and Simchi-Levi and Wu (2018) analysed the application of BDA to retail.”
The Operational Value of Social Media Information (2018, methodology): “Our ﬁnding aligns with that of Ferreira et al. (2015), where the authors ﬁnd non-parametric regression technique outperforms many of the parametric demand prediction models.”
Optimization of Tree Ensembles (2017, methodology): “…of application areas; examples include chemistry (Svetnik et al. 2003), genomics (D´ıaz-Uriarte and De Andres 2006), ecology (Elith et al. 2008, Cutler et al. 2007), economics (Varian 2014, Bajari et al. 2015), marketing (Lemmens and Croux 2006) and operations management (Ferreira et al. 2015).”
The Power and Limits of Predictive Approaches to Observational-Data-Driven Optimization (2016, methodology): “gies for observational data. All of these proceeded by estimating the PRF and optimizing resulting estimated proÞt. Similarly, in their own context, each of Besbes et al. (2010), Cohen et al. (2014), Ferreira et al. (2016) Þrst estimated demand then optimized price. It may be argued that it is impor”
Market Segmentation Trees (2019, background): “Bertsimas and Kallus (2020), Cui et al. (2018), Ferreira et al. (2016), Glaeser et al. (2019), and Nambiar et al. (2019) use regression tree ensembles (e.g., bagging, boosting, or random forests) to predict demand in retail settings.”
Big Data Analytics in Operations Management (2018, background): “Ferreira et al. (2016) conduct a case study on an online retailer called Rue La La for its demand forecasting using data-driven machine learning techniques.”
Retail forecasting: Research and practice (2019, methodology): “Ferreira et al. (2015) applied this approach to an on-line fashion retailer; Schneider and Gupta (2016) applied this to on-line Amazon sales of tablet computers; Tanaka (2010) applied it to data on early sales of books and consumer electronics to forecast longer-term 6 month sales; Wright and Stern ”