Identifying optimal product prices via Machine Learning

via MIT news:

New research describes a price-optimization method to increase online retailers’ revenue, market share, and profit.

How can online businesses leverage vast historical data, computational power, and sophisticated machine-learning techniques to quickly analyze and forecast demand, and to optimize pricing and increase revenue?

Algorithm increases revenue by 10 percent in six months

Simchi-Levi developed a machine-learning algorithm, which won the INFORMS Revenue Management and Pricing Section Practice Award, and first implemented it at online retailer Rue La La.

The initial research goal was to reduce inventory, but what the company ended up with was “a cutting-edge, demand-shaping application that has a tremendous impact on the retailer’s bottom line,” Simchi-Levi says.

Rue La La’s big challenge was pricing on items that have never been sold before and therefore required a pricing algorithm that could set higher prices for some first-time items and lower prices for others.

Within six months of implementing the algorithm, it increased Rue La La’s revenue by 10 percent.

Forecast, learn, optimize

Simchi-Levi's process involves three steps for generating better price predictions:

The first step involves matching products with similar characteristics to the products to be optimized. A relationship between demand and price is then predicted with the help of a machine-learning algorithm.

The second step requires testing a price against actual sales, and adjusting the product's pricing curve to match real-life results.

In the third and final step, a new curve is applied to help optimize pricing across many products and time periods.