The New Frontier of Price Optimization

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Until fairly recently, price optimization has been restricted primarily to certain industries that have limited inventory, such as airlines and hotels. It’s complex work, demanding the analysis of vast quantities of data and a deep understanding of competitors’ behavior. Until recently, few organizations could optimize the price of more than a handful of products at any one time.

As the author explains, this is changing.  Thanks to the growing availability of internal and external data, advances in machine learning, and increases in computing speed, price optimization can be applied more broadly.  For example, Simchi-Levi and his MIT colleagues MIT K. J. Ferreira and B. H. A. Lee, developed a way to set optimal prices for hundreds of stock units in near real-time and on an ongoing basis.

In trials of their pricing technology with three online retailers, they found that they were able to increase each retailer’s revenue, market share, and profit for selected products by double digits.  What’s more, although the examples described in this article involve online retailers, the price optimization method they developed is also appropriate for brick-and-mortar retailers; they recently implemented a similar method at a brewing company, where they optimized the company’s promotion and pricing in various retail channels with similar results.

Their system for generating better price predictions includes three steps:

  1. They match a cluster of products with similar sales characteristics to those of the product being optimized.  Then they use a machine learning technique called a regression tree, which consists of a set of if-then statements that yield a prediction. Using a company’s historical sales data, their algorithm generates as many as 20 if-then statements that can be used to predict the relationship between demand and price.  That information, in turn, can be used to generate a price.
  2. Next, they test their price against actual sales, redrawing their pricing curve to match actual results. At the end of the learning period, they know how well the product sold and can use that information to refine their demand-price curve for it.  And,
  3. Once the learning period is over, they apply the...

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