The New Frontier of Price Optimization | Tribune Content Agency

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Price Optimization in Practice

In reality, not every step is used in every situation. For example, Boston, Massachusetts-based Rue La La Inc. did not want us to change prices in the course of its 48-hour sales, so we skipped the learning step. And when we worked with Chicago, Illinois-based Groupon Inc., we realized that the nature of its business made a demand forecast difficult to generate, so we focused instead on learning from current sales. Here’s how my colleagues and I applied our price optimization techniques at three online retailers: Rue La La, Groupon, and B2W Digital.

Optimizing Pricing for Limited-Time Offers at Rue La La This online fashion retailer offers limited-time discounts (“flash sales”) on designer apparel and accessories. Flash sale businesses like Rue La La aim to create a feeling of urgency and scarcity of products by offering great deals but for only a limited time (often just a few days) and with limited inventory. On Rue La La’s website, the customer sees a number of “events,” each representing a collection of similar products. Each event shows a countdown timer informing the customer of the time remaining until it will no longer be available.

One of Rue La La’s main challenges was pricing items that it had never sold before. The company refers to them as “first-exposure” items, and they account for the majority of its sales. For example, in one department, about half of the first-exposure items sold out before the end of the event — suggesting that Rue La La could have raised prices on those items while still achieving high sell-through. On the other hand, many first-exposure items sell less than half of their inventory by the end of the sale period, indicating that their prices may have been too high.

In practice, Rue La La marketers set prices following the traditional cost-plus pricing method — simply adding a markup percentage to the product cost. However, the number of stock outs for some products and amount of leftover inventory for others suggested that the company was leaving money on the table. To increase revenue and market share, Rue La La needed a pricing algorithm that could set higher prices for some first-time items and lower prices for others.

Our approach was twofold and began with developing a demand prediction model for first-exposure styles. We then used this demand prediction data as input into a price optimization model to maximize revenue.2 The two biggest challenges we faced when building our demand prediction model were estimating lost sales due to stock outs and predicting demand for styles that had no historical sales data.

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The New Frontier of Price Optimization | Tribune Content Agency (September 27, 2017).