BetterBasket

TLDR; 95% of American consumers are changing their grocery shopping habits because of volatile prices, and the $800bn grocery industry must adapt. BetterBasket provides an end-to-end solution for merchandising teams at grocery stores to help with pricing and product assortment.

🍌 Problem

Grocers and food & beverage brands need data-driven recommendations more than ever because of…

• Macro impacts from price volatility,
• customer pull for more localized selection,
• and shrinking pricing zones as local supply chains get impacted.

Even for established enterprises, analyzing data across hundreds of thousands of SKUs (“apples-to-apples” comparisons), daily price movements, and multiple margin targets, while balancing sell-through, profitability, and customer satisfaction (especially at a time when many are being accused of “greedflation”) is an impossible task.

Current solutions are to:

• Make decisions without data,
• Use a labor-intensive method such as mystery shoppers, or
• Get by with tools that neither raise the level of sophistication that managers can operate at nor saves them time.

🍎 Solution

BetterBasket is an end-to-end solution for merchandising, encompassing (1) competitive data, (2) visualizations, and (3) recommendations.

🧺 How it Works

We scrape websites and flyers for in-store and online pricing and product info. Our matching algorithm then uses a combination of natural language processing and computer vision methods to create a centralized product repository across merchants, geographies, and categories that describes the items and the relationships between them.

This database allows us to give customers up-to-date and accurate competitive information on the products that they and their competitors are carrying. This includes but is not limited to historical price changes, geographical pricing (zone maps), and Venn diagrams showcasing item overlap.

Our pricing and assortment algorithms generate pricing recommendations (with the ability to automate pricing in online stores and physical stores that use digital price tags) as well as assortment recommendations (products to acquire or remove). Our algorithms use data from competitive intelligence, our customer’s unique strategies and targets, and external variables to achieve the optimal outcome based on current conditions.

🍣 Why we started BetterBasket

Vagelis and Leon worked together at Uber Eats, setting up the grocery delivery platform and notably launching our dark store footprint in Taiwan and Japan and scaling it to 20+ stores. As we built up a launch team with no retail DNA, we realized the need for merchandising analytics for pricing and procurement teams, especially with regards to competitive data.

🍴 The Ask

If you know of any grocery chains or food and beverage brands that need help with pricing or product assortment decision-making or are willing to talk about their most pressing issues, let us know! We’re at founders@betterbasket.ai and would love to learn more about their needs.