Retailers Scramble to Implement AI-Based Pricing Strategies

Supply shortages are easing and consumer spending is up, but inflation is showing no sign of slowing down. As we move further into 2022, it’s clear that retail is facing an uphill battle. 

Retail sales rose by 3.8% in January, but much of that increase was due to inflation. According to some estimates, as much as 60% of the projected increase in retail sales in 2022 will be a product of inflation.

When it comes to managing prices and margins, retailers are finding themselves in a position they’ve rarely seen before: Keep prices low and take a shorter margin, or pass on the higher costs to loyal customers?

Big box retailers like Target and Walmart are choosing the former, adopting pricing strategies that involve absorbing some rising costs rather than raising prices. Meanwhile, the department store Macy’s is taking a different approach. The retailer is reportedly testing to see which product categories consumers are more price-sensitive toward — where loyal customers would be willing to spend a little extra. 

A Data-Based Approach

The data-based approach to pricing utilized by Macy’s and other large retail chains relies on sophisticated algorithms and the use of artificial intelligence tools to strike the right balance and uncover hidden price ceilings for certain categories of items. Receiving incredible amounts of data continuously gives retailers the ability to quickly detect important embedded signals coming from customers and their operations, which can be acted on to deliver the right customer experience, both online and in-stores. 

But industry experts like Philip Melson are encouraging retailers to avoid taking that all-or-nothing approach. Melson says there’s a human element to pricing that retailers can’t replace strictly with AI.

“Pricing is highly quantitative in nature and, therefore, lends itself well to AI … [however] there will always be qualitative elements to ensure pricing successfully dovetails with the retailer’s broader mission and brand promise,” Melson says. 

At Fractal Analytics, an AI company that provides services in the CPG, retail, and technology sectors, Melson keeps a close eye on how companies navigate inflation-based pricing concerns. He’s now recommending that companies find ways to use AI and human intelligence together in their pricing strategies, so the two approaches will complement one another.

“AI is incredibly powerful; no one would argue that,” he says. “However, every retailer exists for its customers and should tailor the entire experience for them. That means that consideration has to be given for what their brand promise is and upholding that through the lens of pricing.”

The Human Element

Despite his role in helping global Fortune 100 companies bring analytics and AI to their decision-making processes, Melson still believes that humans should be involved in retail pricing decisions. Dramatically changing pricing strategies based on big data alone comes with a high level of risk. For example, a retailer that serves lower-income shoppers might decide to change prices dynamically in order to optimize sales and profit. However, that change would entail a high level of risk if customers on fixed budgets were to enter a store and find that they couldn’t afford the number of items they were expecting to purchase.

“If they can’t be confident they can do so, they will go elsewhere,” Melson says.

One strategy that Melson sees as less risky is using AI to scale competitive price benchmarking on a real-time basis across numerous competitors and geographies. This advanced pricing strategy isn’t new, however improvements in AI technology are leading to an expansion in the set of dynamic factors that retailers can include in their pricing decisions. For example, retailers today can take intraday demand patterns, current or forecasted weather, expected supply chain challenges, and localized inventory levels into account to balance revenues, profit, and inventory turns in a more effective and automated way.

As that same AI technology continues to evolve, Melson sees a future where the number of decisions where a person’s direct intervention is needed decreases, and model-generated pricing recommendations get pushed directly through to execution in an automated way.

“People will get more involved in the exceptions and edge cases,” he says. “Most importantly, [they’ll] spend much more time on the strategic and customer-centric aspects of pricing.”

​​Stephanie Miles is a senior editor at Street Fight.

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