Retail pricing strategies getting a makeover from data analytics

Business and Technology Writer

If ever there was a time when retailers needed flawless pricing strategies, it's in today's market. Pricing transparency is as at an all-time high: Consumers can find the best deals on merchandise in a matter of minutes with an online search, and retail price-comparison apps have become mainstream. As a result, retailers are turning to analytics to bring new precision to how they price goods.

Test and measure

With data analytics, retailers can identify what pricing strategies, be it markdowns or sale events, are working and which ones have lost their luster.

Susan Lee, partner with global consultancy Simon-Kucher & Partners, runs the firm's consumer goods and retail practice in North America, and she often sees retailers repeating lackluster sales events. In a recent interview with me, she explained that "it's quite common for retailers to anniversary or repeat their sales events year after year, and many of them don't invest enough people and system resources to measure the promotional impact on their financials." Shoppers quickly become accustomed to seeing merchandise on sale, and they come to expect discounts whenever they enter the store. Data analytics can help retailers get out of this sale rut.

"Strong data analysis capabilities enable a retailer to test and learn continuously, instead of repeating the same events, and [retail] buyers are equipped with insights to make smarter discounting decisions across locations, product groups and seasons," Lee explained.

So instead of placing all sweaters on sale, the more sophisticated retailers understand that different sweater segments, based on materials, price tiers or target audience, serve different roles in the store. Their purpose may be to drive traffic to the store, fuel impulse purchases or boost occasion-driven buys or gift purchases. According to Lee, "the promotion [and pricing] strategy for each product segment may be different," and data on product performance can help retailers nail down this type of strategy.

Dynamic pricing

It's also important to keep in mind that shoppers consider several factors when buying a product, including convenience, as shown by the rise of online shopping.

"In today's market, consumers do not make purchases based on price alone," Victor Rosenman, CEO of Feedvisor, which provides pricing solutions for online retailers, explained to me in an interview. "They place significant value on the overall buying experience the retailer provides, such as customer service, shipping speed, return policies and many other factors. Many consumers are ready and willing to pay premium prices in exchange for a superb buying experience but, at the same time, demand discounts from less experienced merchants that are new to the market."

Feedvisor uses machine-learning algorithms that continuously process large chunks of data, and the company determines the optimal price for a product based on the overall value of the buying experience that is offered to consumers. Rosenman explains that Feedvisor calls this process "Algo-Commerce," defined as "the discipline of using big data and machine learning algorithms to make business-critical decisions for online retailers."

How does this reflect a fundamental shift in pricing e-commerce goods? Rosenman notes that it shows the migration from classic retail pricing to market-driven pricing, similar to that of the stock market. Many retailers price in isolation, using psychological effects and techniques such as cost-plus pricing and loss-leader pricing, while others simply match the prices of large retailers. By contrast, Rosenman explained that "market-driven pricing quickly and accurately represents changes in demand, price elasticity of buyers and availability of supply. In competitive markets, a retailer needs to assess the relative value of the buyer experience it provides to consumers and respond to a change in market dynamics."

The payoff? Retailers implementing this technology have reported an increase of up to 500 percent in sales, 40 percent in profit and 90 percent time reduction on pricing decisions, Rosenman said.

Retailers that can leverage their data through advanced analytics will be able to reap similar results by pricing their merchandise more precisely and encouraging customers to come back to the store.

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