Our client is a Fortune 100 beverage company that needed to find the optimal product recommendations for new and existing products.


Our client needed to identify gaps for category, brand, and SKU, from the regional to the outlet level.

It needed to quickly recognize and close on any growth opportunities in this sector by identifying potential sales gaps for any category, brand, or SKU, from the regional level all the way down to the outlet level.


We combined deep neural nets, collaborative filtering, and ensemble models to create an AI-driven analytics solution that helps our client make smarter product and assortment recommendations.

The AI engine processes multiple data sets; runs segmentation, recommendation, and volume prediction APIs; and then further refines the results by likelihood of purchase and other SKU and category recommendation drivers. It’s also scalable across all channels and use cases.

Now sales team members can input geographic location (nation, region, market, postal code), sub-channel, chain or franchise group, and the current product assortment into the user-friendly interface, and the solution will:

  • Uncover optimal assortment by DMA, chain, or outlet.

  • Identify chain and outlet opportunities by SKU.

  • Maximize beverage sales opportunities for existing customers.


  • This tool hasn’t just invigorated the foodservice sales division of a major company, it’s increased the technical capabilities available to decision-makers.
  • Now our client has granular insight into customer needs. Sales leaders can optimize decisions, reps can make better pitches, and everyone can make better decisions.
  • The sales team relies on the tool to tailor pitches to each account, and it has become a key part of sales leaders’ annual sales planning process.
  • 3%

    uptick in incremental revenue
  • 2%-3%

    higher incidence rates
  • 4%

    consumption value growth across categories