SMU Data Science Review
Abstract
This study presents a novel approach to price optimization in order to maximize revenue for the distribution market of non-perishable products. Data analysis techniques such as association mining, statistical modeling, machine learning, and an automated machine learning platform are used to forecast the demand for products considering the impact of pricing. The techniques used allow for accurate modeling of the customer’s buying patterns including cross effects such as cannibalization and the halo effect. This study uses data from 2013 to 2019 for Super Premium Whiskey from a large distributor of alcoholic beverages. The expected demand and the ideal pricing strategy to maximize revenue for the business is analyzed. This study shows that models with cross effects can improve the forecasting capability by 39% over naïve models and by 14% over models without cross effects. Using these more accurate models, an optimal price point was calculated, which forecasted an increase in revenue by 30%. While the techniques presented in this paper have been validated for the distribution market of alcoholic beverages, they don’t rely on any domain specific knowledge from this industry, and thus can be applied to other distribution markets for non-perishable products.
Recommended Citation
Gupta, Nikhil; Moro, Massimiliano; Ayala, Kailey A.; and Sadler, Bivin
(2020)
"Price Optimization for Revenue Maximization at Scale,"
SMU Data Science Review: Vol. 3:
No.
3, Article 4.
Available at:
https://scholar.smu.edu/datasciencereview/vol3/iss3/4
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