New findings from Nielsen, Chicago, demonstrate that just 8% of consumers consider themselves to be firmly committed loyalists. This puts food retailers in a vulnerable position and engages them in a constant race for buyers. The race is fueled by increased price awareness of shoppers, and the abundance of options for them to choose from, as well as the dominance of grocers to sell at lower prices than most retailers.
However, smaller grocers can still compete if they use artificial intelligence (AI) in pricing.
Slashing prices is a dead-end road for smaller grocery chains
In a bid to entice customers and fight off deep discounters and online retailers, most companies sell way below price floors. Even giants cannot help it when it comes to lowering prices.
It seems that smaller grocers willing to stay afloat in this cut-throat market have no choice but to play along. Even being on the verge of bankruptcy, grocers still tap into slashing prices and eventually destroying their margins. As well, according to a study conducted by The University of North Carolina at Chapel Hill, UNC Kenan-Flagler Business School, Chapel Hill, N.C., when a Lidl store pops up in the neighborhood, local retailers drop prices by more than when Walmart opens up a new shop.
The worst thing is that most retailers rarely know which products they really should sell at a discount and which can be safely offered at a higher price and still attract shoppers. The good news is they should just follow suit of trailblazers and embrace AI and machine learning.
How can smaller chains compete with market giants price-wise?
Competing with market leaders is extremely hard and even impossible in certain cases. To be able to stay competitive, smaller grocers have to define a group of items, which are called turf or protected products that need to sell at lower prices than competitors, even if this kills their profits for these particular products. It is a necessary step to attract and retain buyers.
At the same time, it is essential for retailers to understand which products are safe to be offered at a higher price. Such items are usually exclusive to a particular retailer and will be bought anyway. Setting higher prices for these products helps retailers make up for the margins lost in their battle with bigger sellers.
This usually leads to two questions—how to define what products should be sold at a higher/ lower price and how to price them. As a rule, retail teams simply cannot answer these questions, as there are too many parameters to factor in. That’s where they usually engage in AI and machine learning.
It is true that some retailers still see AI as something extremely hard to adopt. But, not so long ago Big Data seemed the choice of tech innovators, while today everyone is using it. Same works or will work for AI soon.
Using machine learning to become the grocery king of the block
Machine learning turns “weak guys” into powerful market players.
The technology enhances retail teams in terms of computational power, precision and speed. Self-learning algorithms process massive amounts of data, factor in any number of necessary parameters and put forward price recommendations for the whole product portfolio in real time for any period of time. Among the parameters, the algorithms consider are cross-price elasticity, the retailer’s past performance and current business goals, competitive prices, customer behavior, as well as weather.
The algorithms are especially helpful when it comes to calculating the prices for own brands, or private label. By using machine learning, retail teams know exactly what prices to offer, how to influence demand reaction and reach a price optimum to hit maximum revenue per product and for the entire portfolio. Also, technology-empowered retail managers move to data-driven pricing decisions for strategic thinking.
Competition in the U.S. grocery market is getting increasingly fierce, margins are going down, online is thriving and customers are becoming less loyal every single day. Staying relevant in such an environment means using the same technology that market leaders have been successfully employing for years. Luckily, AI and machine learning are growing more accessible to more retailers.
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