Trax, Storecheck Help CPGs in Mexico With Store-Level Data
Trax, which provides computer vision solutions and analytics for retail, and Storecheck, an integrated retail solution for in-store execution, have joined forces to provide CPG companies in Mexico with enhanced product and category shelf data across the traditional trade channel. Together, Trax and Storecheck aim to refine the source and quality of execution data to give brands a clearer understanding of the market to make strategic and cost-effective decisions.
This partnership comes at an opportune time for CPGs in Mexico, as the complex geography, vast quantity and dispersion of stores in the country contribute to challenges in effectively monitoring product performance on the shelf.
“Our partnership with Storecheck lays the foundation for CPGs in Mexico to truly understand how their products and categories are performing at the shelf,” said David Gottlieb, managing director, Americas at Trax, said in a recent media release. “Brands will now have access to timely, accurate data, at a level of granularity previously unavailable at this scale, in an impartial and cost-effective way.”
Trax and Storecheck will provide brands a closer look at SKU performance in traditional mom-and-pop retailers prevalent throughout the region. CPGs will be able to use this information for strategic decision-making such as: building perfect store metric programs, enhancing category understanding, auditing price and promotion compliance, and correlating sales and execution data.
“The decision to partner with Trax exemplifies the retail innovation, commitment, and quality Storecheck believes in,” said Rodrigo Sola, CEO of Storecheck. “In an effort to produce solutions and services that advance how retailers and brands can enhance the shopper experience, we are excited to work with Trax to provide timely shelf monitoring and data to CPGs.”
Trax’s in-store execution, store monitoring and retail analytics solutions aims to help retailers and manufacturers manage on-shelf availability and optimize merchandising. These solutions are powered by proprietary fine-grained image recognition and machine learning algorithms that turn photos of retail shelves into granular, actionable shelf and store-level insights.