Using AI to Determine Prices and Boost Revenue in Grocery

How can one survive in the $869 billion U.S. food market, where margins are low and Walmart, Kroger and Albertsons account for a lion’s share of the industry? Amazon’s Whole Foods Market is there, too, despite the fact that it’s ranked 10th in Progressive Grocer’s annual Super 50 of the top grocers in the United States.

Grocers need to surf the waves of ever-changing customer expectations and do whatever it takes not to join the ranks of fallen retail empires. Everyone is citing customer experience as priority No. 1 for retailers. However, another major lever for retailers to pull to stay alive and even thrive is the price of an item.

Big names highly recognize importance of price, along with frictionless customer experience, a wide range of offered products and well-planned marketing activities. They fight mercilessly once something goes wrong. Do you remember how Walmart Mexico has recently penalized grocery companies providing goods to Amazon, which was selling them at a lower price? Another example is Amazon’s recent move to lower prices at Whole Foods by some 20 percent, reducing its price gap with Kroger in a bid to attract more customers.

Retail giants invest into price optimization heavily, which allows for creating the right price perception and persuade customers.

Advanced retailers recognize that traditional pricing approaches are broken. They are determined to embrace AI for pricing. But how exactly can technology make it easier for smaller operators to stand up and fight, and win?

AI to help retailers compete

Retail giants have been harnessing advanced analytical software like AI to set the right prices for years. Such technology used to be extremely expensive and available to the select few.

Today, more companies, including smaller operators, are getting the chance to grow as such software is becoming simultaneously more sophisticated and accessible to a wider number of players (ask Deloitte).

According to a series of market tests held by retail price optimization company Competera, elasticity-based machine learning algorithms can help retailers set competitive prices and raise revenue by 5 percent and beyond.

Vladimir Kuchkanov, pricing solutions architect at the company, comments on how artificial intelligence can be advantageous for smaller supermarkets competing with the likes of Walmart.

“The retail king has better chances from the beginning. Thanks to the huge amounts of products it buys from suppliers, the company can negotiate better purchase prices,” he says. “To compete with it, smaller food chains need to identify a pool of products which have to be priced lower than at Walmart — even if they are selling at a loss. This would keep attracting customers.”

However, it is just the start. “On the other hand, businesses need to indicate a group of items which can be priced higher without risking to scare off shoppers. This way retailers can compensate for low or negative profit margins they inevitably have in their fight with Walmart. But this begs two major questions: how to identify such items and how to price them. Managers cannot do that as there are too many parameters to take into account. That’s when retailers bring AI into play,” adds Vladimir.

Another challenge the technology helps retailers to tackle is the management of private label products, which are essential for creating a unique and recognizable brand and winning the hearts of customers.

According to L.E.K. Consulting, this market is projected to hit $220 billion by 2020. Also, players like Walmart and Albertsons count on such items very much: nearly 84 percent of Walmart’s customers buy the retailer’s private label products. With machine learning, retail companies can be sure that they set the right prices for every one of these items.