With the continued shift towards e-commerce, pricing has become more complex for retailers. Pricing decisions seemingly have to be made more frequently and much quicker to remain competitive. But how can retailers remain competitive through more frequent price changes yet get their pricing right? Dynamic Pricing can help to cover this and much more, if it is informed by the right analytics.
Over time, retailers have amassed large amounts of data they can leverage to determine where and how much to adjust prices. However, this heap of data also complicates matters, as retailers now have to consider many more potential variables that can affect their pricing decisions. There is also an increased pressure to use all of the available data to frequently change prices, despite evidence that not every product should be changing prices all the time. The approach below describes how to overcome these pricing challenges with intelligent dynamic pricing.
Dynamic pricing should be highly targeted. Retailers need to fight the urge to react to price changes blindly. Although it may be advisable to change prices in response to competitor actions, applying the right analytics to inform these decision is essential.
To make this strategy successful, first you should understand the pricing landscape. To accomplish this, retailers can perform a volatility analysis to find out how often and by how much prices are changing. In addition, a lead / follow analysis, which requires competitive data, helps with determining which companies initiate price changes and how others respond.
A Classification and Regression Tree (CART) analysis can identify segments with different price sensitivity and can be used to guide more targeted pricing decisions. The ability to measure and understand the demand response to price allows retailers to raise prices without losing demand disproportionally and to lower prices just enough to stimulate demand.
Dynamic Pricing should be tailor fit. Once retailers go through the steps above, they can tailor their pricing strategy to specific segments. Typically, price sensitivity and associated customer behaviors vary greatly across a retailer’s assortment (as do other complexities in the business). Recognizing these variations, retailers should identify which segments require little more than a simple approach, and which necessitate complex pricing models in order to compete.
Dynamic pricing should learn as it goes. While the data, analytics and strategy setup is critical, nothing is set in stone. Intelligent dynamic pricing solutions should be self-learning. As retailers continue to make decisions and subsequently capture performance data, the analytic models will learn and adjust their recommendations to best meet current conditions. Not only should price recommendations get better with time, but the models should also help retailers remain competitive by continually assessing which items are the most critical to compete on.
Consistently getting pricing right in a dynamic retail landscape is challenging. Deploying the approaches identified above will allow retailers to make sense of the wealth of data available to them, delivering an intelligent pricing approach that will increase market share and drive organic revenue growth.