How AI, GenAI, and Unified Data Can Power Store Brand Innovation

As the Clive Humby quote goes, “data is the new oil,” something retailers know far too well with the amount of data bubbling up within their organizations. And while data has been flowing through the retailers’ pipelines for quite some time, there is rising pressure to put this data to use quickly, including optimizing private label products that continue to grow in market share.   

The challenge for retailers is pooling and managing massive amounts of data. Multiple times a day, teams across the company are pulling, organizing, and analyzing internal sales numbers, shopper loyalty program insights, supplier partner data, third-party panel results, and more. This data drives key decisions around pricing, demand, assortments, and private brand strategies. 

Unfortunately, this data often resides in manual spreadsheets, and gathering actionable insights can take an exorbitant amount of time. Another issue: many business teams work in silos, sending data down different pipelines that never cross paths. Yet, private brand teams need access to the same sales data and loyalty information as merchandising and marketing teams. This is where having internal and external data running through one unified pipeline, all sourced and funneling to one location, can arm private label teams with new ways to grow their portfolios.

Add AI and GenAI to the mix, and retailers can further innovate and grow their store brand presence. 

4 Ways AI, GenAI, And Unified Data Boost Private Label 

Retailers are overwhelmed with data, but the right AI tools can help unlock the insights they need. That’s why retailers are exploring the use of GenAI to automate insights, create product copy, and develop a more personalized and localized in-store and online shopping experience. GenAI and AI, coupled with a solution that harnesses internal and external data, formats it, cleans it, and harmonizes it, can provide store brand teams with new growth opportunities. Here are four examples where AI and unified data can boost owned brands:

• Localizing store brand assortments — By store or region, retailers can use AI to identify which private label products perform well in specific stores, then localize assortments with those items to provide a customized shopping experience. GenAI can also offer language customization, delivering promotions and content in the preferred language by store.
• Improving private label discovery — Retailers want store brand items to get noticed online. AI can automatically pull key product attributes from images of store brand product packages to boost their visibility and discoverability in search rankings.
• Influencing portfolio expansion — Store brand teams can leverage AI to read through the real-time, unified data to identify products that are performing well and where the retailer doesn’t have a private label option, helping retailers determine where they can profitably expand.
• Personalizing private brand ads — GenAI can automatically create copy and images for personalized digital ads for private brand products, elevating the digital reach of store brand items.

Innovating private brands with data-driven AI

Traditionally, it’s not uncommon for a retailer’s private label division to work in a silo, even operating a separate data solution. However, unified data eliminates the silos, bringing total data visibility to all parts of a retailer organization. Then, with AI and GenAI capabilities sewn into the fabric of the data, retailers can put more power behind their private brands.

Data-driven retail analytics and AI can help store brand sales reach new heights, helping retailers gain more share of the market compared to national brands. Retailers can make strategic decisions about their private label products — from demand forecasting and assortment planning to category management and pricing — ultimately increasing private label growth.

Store brands are ripe for AI innovation. Retailers and their private label teams just need to get unified around data and AI.