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Price Optimization for E-Commerce

Background

A leading online retailer was exploring opportunities to optimize prices to enhance profitability. Historically, they had run A/B tests on specific price attributes to set the go-forward price and would vary the price based on inventory levels. Prices were set to end in $X9.99. The company wanted to review past data to understand price elasticity and establish new tests to optimize pricing going forward.


Our Analysis

Intensity performed statistical and economic analyses evaluating historical transaction data to understand drivers of demand change and price elasticity. A demand model was built based upon the significant drivers. The project involved statistical analyses including discrete choice regression models, predictive modeling of consumer behavior, and demand simulations, among others.

Go-forward price tests were constructed to determine the optimal prices and frequency of price changes to set pricing algorithms. Additionally, prices were moved away from strict $X9.99 price rule to improve profitability.