Tailored to Perfection: Creating a Hyper-Local Shopping Experience with AI

It’s easy to understand, emotionally, why consumers would want to shop from a store that tailors its product assortments to the neighborhood they live in. For large chain retailers, it’s a way of letting the community know that consumers are seen and that the store wants to be part of the community. Ultimately, it sounds like a no-brainer for a retailer, but managing a localized assortment can be quite complex.

Curating a localized assortment requires an abundance of data on what products are being purchased and what products are missing from the assortment, as well as how they’re tracking through the supply chain. It’s a matter of retailers and consumer goods teams being in constant communication to build a better experience for shoppers.

In a way, localization is personalization, something that the analysts at 84.51 said can impact sales. The analysts, who work with Kroger, found that nearly 60% of consumers are more likely to shop at a store that has personalized content. It’s not surprising, then, that Kroger is looking to get even more personal through its assortments. In Rodney McMullen’s opening remarks at Groceryshop 2023, the Kroger CEO said the retailer would be increasing the number of local products in stores by 10%, around 30 new local products per store.

An increase in locally sourced products is one way of creating a customized assortment by store, as retailers see significant value in developing assortments that feel central to each location and community. But can they do this efficiently? Predictive analytics can help.


Identifying the Most Effective Custom Assortment

On the heels of a retailer as large as Kroger looking to build localized assortments by adding in more local products, consumer goods partners have an opportunity to bring insightful data, powered by predictive analytics, to highlight which items will perform optimally in each of their retailer partners’ stores.

Brands using AI and predictive analytics can look at a retailer’s entire category and find products that will resonate most in each store’s assortment (including competitor products). With unbiased data, the brand comes to the retailer from a leadership position, highlighting the greatest mix of products, local or otherwise, that will grow the category first. AI modeling, in particular, can support this in three key ways: 

Forecast demand. Through predictive AI, a brand can bring to retailer partners daily, weekly and longer outlooks on the forecasted demand for products, by store and zip code. The insights look ahead at consumer demand by store and by certain consumer segments. The data-driven knowledge powers brand and retailer collaboration, helping to identify what’s available, evolve over time and build an assortment together that will be effective long-term.

Test assortments to find products that will perform best. Machine learning models locally optimize assortments by looking at products at a SKU level, how that SKU performs within a range of experimental product mixes, how the product sells during time periods and by store. Then, the data looks at how the products sell in various floor sets. Brands can come to retailers having tested multiple scenarios to see what products resonate most with local consumers. It’s virtually testing and building a product mix of national brand items, regional items and locally sourced products that have the best chance to grow categories overall.

Optimize share of shelf. AI also helps optimize how much of a product should be carried and where the products will perform best on-shelf at the store level. AI tools enable brands to visualize physical space opportunities in real time and uncover how products can impact sales and foot traffic inside a store. The insights deliver a tailored, customized and highly impactful shelf display plan for each individual store in a retailer’s network.

Getting Informed Before Getting Localized

When done right, retailers can reach higher sales, volume and profit margins, as well as build customer loyalty, by localizing assortments to each store’s shopper preferences and needs. AI-powered insights will help retailers forecast demand around what shoppers are buying by each store beyond just a zip code, and identify which items will perform best, when and at which stores. CPGs that bring these insights demonstrate category thought leadership and will set themselves apart from their competition.

CPG partners working directly with retailers, using predictive analytics, will deliver the most effective assortments possible. For retailers like Kroger, getting localized starts with the right data and insights. CPGs can elevate their positions in the eyes of a retailer like Kroger by bringing AI-powered insights that show what assortment customers want their stores to carry.

Kristine Joji serves as EVP of Strategy Consulting for Insite AI. She spent 20 years at Walmart where she was recognized as a visionary leader playing a pivotal role in optimizing Walmart’s merchandising strategies.

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