C-Store National New Products Technology

VideoMining’s Analytics Tool To Aid Strategies For Foodservice Shoppers

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VideoMining Corp.‘s, the Meal Builder, is a new interactive, cloud-based analytics tool for helping to develop strategies for growing the basket of c-store foodservice shoppers.

The tool enables users to dive deep into a rich information set, including which foodservice types are purchased on different dayparts, cross-purchase details for each food type for each daypart, geographic regions and store types, across all seasons. The tool’s intuitive interface helps users understand the key products in each foodservice category as well as explore potential meal-building opportunities through various marketing and merchandising strategies.

“Foodservice is one of the key profit centers for the modern convenience store, and innovations in this area are driving change throughout the industry,” said Dr. Rajeev Sharma, founder and CEO of the State College, Pennsylvania-based company. “This new tool will help retailers, manufacturers and vendors make fact-based decisions on how to match the needs of today’s foodservice shoppers.”

The Meal Builder is part of a suite of analytics tools and insights packages available from VideoMining’s decade-running C-Store ShopperInsights (CSI) MegaStudy program. Other tools include the Space Productivity Optimization Tool (SPOT) and the Tobacco Trip Analytics tool. The CSI MegaStudy program is powered by VideoMining’s proprietary, AI-based in-store behavior analytics platform and provides unprecedented visibility into real shopper behavior using a large dataset derived from more than 100 million c-store trips every year from a carefully selected national panel of convenience stores.

VideoMining is the developer of in-store behavior analytics for consumer packaged goods (CPG) manufacturers and retailers. VideoMining’s deep analytics platform utilizes a patented suite of advanced sensing and artificial intelligence to capture in-depth shopper behavior data and provide comprehensive solutions for optimizing retail performance. The previously unmeasured in-store behavior data is integrated with multiple other data sources such as transactions, planograms, product mapping, loyalty and promotions to create a powerful suite of insights and prescriptive analytics tools.

About the author

Kristen Brissette

Kristen lives on Cape Cod and enjoys working in the food industry. She loves attending food shows and meeting others in the field.

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