How Machine Learning Can Optimize Assortment and Retain Customers

The shifting grocery landscape is forcing retailers to quickly adjust their strategies, putting increased emphasis on fresh and prepared foods, adjusting store layouts to accommodate click-and-collect and optimizing categories to meet consumer demands. To try to stay competitive and stimulate sales, retailers tend to follow new product trends. Often, however, this leads to saturation in categories, and sometimes items are overlapping in similarities or even duplicated.

Today, a retailer’s assortment decisions should be motivated by a particular industry statistic: As many as 17% of products in a grocery category duplicate one another. Duplication is a concern not only for optimal category performance but also because, as the “paradox of choice” suggests, when overwhelmed with options, a customer will abandon a decision altogether.

However, managing a category to counterbalance the influx of innovative products and to eliminate duplication is not trivial. You might remove the wrong items—those critically important to shoppers maintaining their relationship with you. Knowing which items matter to your customers requires listening to them, and artificial intelligence (AI) can achieve this with seemingly impossible insights and recommendations.

Listen for the Voice of the Customer Before Making Category Cuts 

Category decisions must include the voice of the customer if keeping them shopping in your stores is the ultimate goal. Profitability seems like a reliable metric for making category decisions—keeping what appears to be most popular among your customer base and eliminating items that look like slow movers—but I’d caution against trusting that limited view.

At the end of the day, customers don’t care what the profitability of an item is; they care about what the value of an item is to them personally. But with manual processes, siloed teams and a massive amount of data to sift through, how can you know with certainty what they care about? AI makes this possible when paired with customer loyalty insights. AI learns which products are most important to customer retention, regardless of sales or profit performance, allowing retailers to cater to shoppers’ precise needs. AI also predicts outcomes and makes recommendations for which product to delist over another.

Machine Learning Makes Assortment Optimization Scalable

Artificial intelligence provides immediate clarity around how customers interact with products in and across grocery categories, helping you to understand exactly where a shopper sees value. The technology can go a step further, through machine-learning algorithms, to improve and automate long-term category decisions at scale.

Consider beer, for example. This category is incredibly localized; what sells in one neighborhood might not sell in another across town. The range of beer varieties is wide, and every store location will need to have a slightly different assortment to meet the desires of its customers. Whether your demographic gravitates toward craft brews or prefers large-batch, domestic beers, managing these assortment decisions manually across all stores to ensure the most effective product mix is difficult, if not impossible—that is, without AI and machine learning. And leaving these decisions to the vendors will not achieve true customer-centric assortments.   

With difficult decisions to make across the enterprise, for every category at the customer level, identifying the true value of every product goes well beyond SKU profitability. But rethinking category analysis in the context of customer journeys requires a massive amount of computational and predictive power, which AI and machine learning both deliver. With this technology, you can know exactly how a product’s exit or entrance into a category will affect the sales and loyalty for the category and the entire store.

Give customers the perception of choice as you add products to a category. Keep them returning as you enhance the shopping experience with the right mix of new and mainstay products. But as you navigate category decisions, make sure you’ve got the voice of the customer constantly in your ear. Listen to them, through the insights afforded to you by AI, and you’ll increase profitable revenue and customer loyalty.

Kevin Sterneckert is CMO of Symphony RetailAI.