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March 21, 2016
Dynamic Pricing: A Major Key to Retail Success
By: Sumeet Mahajan & Neil Fernandes

Retailers are busy implementing various new analytic approaches, from advanced forecasting to complex markdown techniques, as we discussed in Analytics Opportunities for Success in Retail. But one approach that has been gaining the most attention of top retailers is dynamic pricing, also known as price optimization. And it is easy to understand why. There are few, if any, data-driven approaches today that can have such a direct and significant impact on a retailer’s profitability as dynamic pricing. With that interest, and urgency, in mind, we discuss the type of executive and technical preparation required for a successful dynamic pricing initiative.


The Pricing function in retail has been disrupted by changes such as a larger than ever portfolio of products, price transparency for customers, the compulsion towards an omni-channel presence, and the evolution of new competition through e-commerce platforms. The compounded effect of all these changes have made the pursuit of improved margins and increased market-share seem more elusive than at any other time in the history of retail. Retailers cannot afford to price today with yesterday’s tools, and still expect to be in business tomorrow. Thankfully, advances in big data and in data sciences are enabling innovative ways to price, both, effectively and efficiently. Dynamic Pricing is one such innovation.

David Simchi-Levi and his team at MIT first developed a unique price optimization tool with online retailer Rue La La. This application received the 2014 INFORMS Revenue Management and Pricing Section Practice Award for a project that increased [Name Redacted] revenues by 10%.

The new approach relies on predictive as well as prescriptive analytics. There are various options depending on the sales model but typically the process is as follows. First, machine learning techniques are used to predict demand elasticity curves – curves that suggest relationship between a price and expected demand. Once the demand elasticity curve is predicted, the knowledge of this curve along with today’s conditions (events, traffic, competition, etc.), and along with today’s constraints (stock level, margin targets, etc.) is used to prescribe optimal price recommendations for each item.

On the heels of the success of this new approach to pricing at [Name Redacted], similar dynamic pricing solutions have been deployed across a range of retailers – from discount retailers in developed markets to large online retailers in developing markets. And all of them have seen a significant, and immediate, increase in revenue or profit or both.

In one such recent engagement, OPS Rules transformed the pricing function of a very large online retailer. Pricing went from being a ‘just another task’ under the commercial team, to being a true competitive differentiator. Once the new algorithms were implemented, pricing became a lot more informed, intelligent and competitive. Some of the inputs to the price recommendations included historical sales, expected traffic, competitive landscape, sales targets and available stock. As we have seen with other clients who have implemented dynamic pricing, with this retailer too we saw double digit increase in revenue, along with an increase in profit. Another interesting observation was that, almost on a daily basis, the new pricing system was selling a wider assortment of products. In other words, dynamic pricing enabled the client to sell not only deeper, but also wider.


Because of the round-the-clock prevalence of pricing within any retail organization, implementing dynamic pricing is challenging, technically as well as culturally. Technically, it involves both machine learning as well as optimization. Culturally, strong change management and business user readiness is required. In Dynamic Pricing for Online Retail, we have tried to provide more details behind the readiness required, based on our experience, and explain the approach in simple, non-technical language.


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