In America's fully liberal capitalist society, there are few business strategies more bizarre than the manufacturer's suggested retail price. The strategy was invented in 1920 by P.H. Hanes, the original founder of the textile brand Hanesbrands. This enabled him to avoid distributors taking advantage of buyers in knitted underwear by advertising in publications across the United States. Even today, many U.S. store owners insist on selling at the manufacturer's suggested price, despite the desire to raise prices to offset cost pressures from inflation. However, more and more people are starting to experiment with more sophisticated pricing strategies.
An important study published by consulting firm McKinsey in 2010 showed that a 1% increase in prices could increase operating profit by an average of 8.7% if sales did not decrease. Getting the right pricing strategy isn't easy. If the price is too high, you may lose customers; if the price is too low, you will lose money. Historically, retailers' pricing has generally been a simple calculation, adding a certain profit margin to the cost, or referring to a competitor's pricing level. With the price of energy, labor and other factors soaring, they can no longer set prices on a whim.
To gain a competitive advantage, merchants are turning to price optimization systems. These systems can predict how customers will respond to different pricing levels and recommend strategies that maximize sales or profits. At its core are mathematical models that use massive amounts of transaction data to estimate the price elasticity of thousands of commodities (that is, how much demand will increase if prices fall, or vice versa). Items that are price-sensitive can be sold at a discount, and items that are not price-sensitive can be sold at a higher price. Merchants can also fine-tune their algorithms to avoid undesired outcomes, such as a price increase that exceeds double-digit percentages, or oversized packaging that increases unit costs.
Thanks to artificial intelligence (AI), these systems are getting smarter. Whereas old models could only estimate price elasticity using historical sales data, the latest batch of AI-driven models have been able to discover patterns and relationships between different items. Pricing software makers are bringing new data sources, from customer tweets to online product reviews, into models, said Doug Fuehne of Pricefx, an industry firm. Through a cloud platform developed by another service, Eversight, retailers can also estimate how a small increase or decrease in the price of an item, such as Heinz ketchup, in different stores affects sales of a particular condiment or even across categories. The platform has been adopted by large manufacturers such as Coca-Cola and Johnson & Johnson, as well as some supermarkets (such as Raley’s) and clothing sellers (JCPenney).
All of this has made the pricing system "more three-dimensional" than it used to be, according to Chad Yoes, a former executive in charge of pricing at retail giant Walmart. Retail bosses are keen to sell this pricing power to investors because they value their companies' pricing power in times of high inflation. In February, coffee chain Starbucks boasted of using AI model analysis to "continuously" adjust pricing. Food distributor US Foods also boasts about the capabilities of the pricing system, saying it uses "more than 12 different inputs" to boost sales and profits.
Price optimization may make prices more volatile. Matt Pavich of pricing software company Revionics points out: “Retailers are changing their pricing at a rate far faster than all-time highs.” This is especially true in the fast-changing world of e-commerce. But according to Mr. Yoes, even Wal-Mart has increased the frequency of price reviews on many items, from 1-2 times a year a few years ago to 2-4 times now.
Pricing systems don't necessarily drive prices up. Mr Pavich called the false impression "one of the biggest myths" about such products. A case in point is Sysco, a large food distributor that promoted the use of new pricing software last year. The company claims that the system has helped it lower prices on "key value items" -- often referred to in the industry as price-sensitive top sellers -- and raise prices on some other items. The company was able to increase sales and achieve profit growth while maintaining profit margins. This makes investors happy and customers happy.
