Price Management

Offline Dynamic Pricing

Once associated primarily with airlines and hotels, dynamic pricing has become more widespread across a variety of industries.  We know online retailers are increasingly experimenting with and implementing dynamic pricing strategies, but what about their brick-and-mortar counterparts?


No one can argue against the fact that Amazon has had great success (as long as we aren’t defining success solely based on profitability). They are probably the most well-known retailer to employ real-time pricing, with multiple price fluctuations per day based on their algorithms. But they are not the only ones. Frequent fluctuations in price are becoming more and more common for online retailers. They become even more frequent during the holiday shopping season when the market is especially competitive.  Part of the reason for the more common use of this pricing strategy is the growing price competition and the rise of price monitoring engines that enable retailers to react more quickly to changes in the market. Related to that is an increased investment in data capturing and analysis tools. They have proven value, and retailers have bought into their importance for staying competitive.

Whatever channel or industry you’re operating in, data is essential to optimizing business decisions. Data-driven merchandising isn’t new. Walmart has used data mining for predictive analysis for years. For example, they found that Pop-Tarts and beer were some of the top-selling items prior to a hurricane, and were able to ensure that those items were properly stocked. Another one you may have heard about is the correlation between beer and diaper sales. Moving the beer closer to the diapers in-store increased sales. Optimize your inventory and merchandising; optimize your profits. The same goes for pricing.


Speed and scalability can be more difficult offline, but many of the same tactics that online stores employ also apply. There are software services available that aggregate data such as profit margins, seasonality, customer data, historical data and predictive analytics, sales velocity, competitor data and market research, etc. With the exception of competitor data, most of this data can be captured at the point of sale.  Some competitor data can also be gathered at that point if a retailer has a price matching program. Although you need to account for differences in costs, if your inventory is also carried online, crawling the web for competitors’ prices can still be useful even if you don’t operate in that space. Some software solutions that do this can be integrated with your POS software to facilitate data convergence or repricing. It definitely takes an investment in technology, but the insights gained can have a significant impact on both your top and bottom line.

Another challenge is consumer sentiment. Whether it be privacy concerns or just a perception of fairness, consumers don’t like the idea that someone is paying less than them for the same item at the same time in the same store. What data should pricing be based on? Shopper habits, demographics, demand? Are there certain lines that shouldn’t be crossed? B&Q, a U.K. retailer began testing variable pricing earlier this year, with the idea of utilizing electronic shelf tags and mobile technology to personalize pricing. Prices may change based on time of day, a customer’s loyalty level, or demand (as a yield management tool). Will consumers appreciate the personalized approach or resent it?

What’s in Store for the Future

Big Data is still a buzzword and there’s a reason why. Retailers are aggregating data sets (loyalty programs are just one way to capture data) and using it to identify trends and opportunities and to develop algorithms to inform their pricing and merchandising decisions.

There are a number of brick-and-mortar retailers experimenting with variable or real-time pricing. Electronic shelf tags are one way retailers are automating and easing the difficulty of changing prices frequently.  These tags allow retailers to increase response time and respond to competitors’ moves faster than ever before. Obviously manually changing regular price tags is incredibly time-consuming and labor intensive.

On the other hand, some brick-and-mortar retailers are a bit more wary of dynamic pricing (due to logistical difficulty and investment required) and instead employ price matching programs to help stay competitive.  A softer approach that still takes advantage of consumer data is sending personalized coupons based on purchase history. Grocery chains such as Kroger and Stop & Shop have used this tactic. They may also try to add value in areas other than price, such as customer service, or flexible return policies. But as the market continues to change and retailers adapt, will real-time dynamic pricing eventually become the norm both online and off?

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Better decisions can only come from better data.

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