Trax Pioneers AI-Driven Dynamic Merchandising to Help CPG Brands and Retailers Keep Products on Shelves
New merchandising service combines computer vision and machine learning with an on-demand workforce to address in-store execution issues at scale
Trax, a leading company in computer vision solutions and analytics for retail, launched Trax Dynamic Merchandising, a first-of-its-kind enterprise-level service providing CPG brands and retailers a better way to merchandise. The service launches as brands face unprecedented pressure to keep products consistently stocked on shelves, and while they realize the profound negative effects of their inability to expediently resolve in-store execution issues.
Trax Dynamic Merchandising combines the company’s computer vision and machine learning platforms with Trax Flexforce, a 1.4 million-plus-strong, crowd-sourced workforce of trained retail representatives spread across North America. Using Trax’s proprietary artificial intelligence (AI) and computer vision technologies, Trax Dynamic Merchandising transforms real-time data of on-shelf conditions into specific execution tasks that align with a brand’s merchandising priorities. Trax’s ‘Prioritization Engines’ ensure Flexforce representatives are in the right stores when it matters most to address the most important tasks during each visit, rather than following a dated plan that was prepared days, weeks or even months prior.
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“Trax Dynamic Merchandising is a game changer for CPG brands and retailers struggling to address their unique in-store execution issues,” said Justin Behar, chief corporate development officer at Trax. “Existing solutions involve brands paying for costly dedicated retail labor or inconsistent, syndicated coverage from traditional agencies. These rigid models create significant merchandising gaps due to lack of reach and prioritization, exacerbating in-store issues, creating significant resource trade-offs and ultimately affecting their bottom line. Trax Dynamic Merchandising flips this outdated and inefficient model on its head by helping brands do the right work, at the right store, at the right time in a more sustainable and easily scalable manner.”
How Trax Dynamic Merchandising Works:
- Continuous In-Store Data Signals: Multiple sources of in-store data signals — from Internet of Things shelf cameras, Trax Flexforce visits, point of sale data and more — feed the Trax Dynamic Merchandising platform with rich data points, allowing per store, per visit optimization. The platform automatically learns from each in-store action and data point, leading to a more intelligent platform with every use.
- Trax Flexforce: Trax’s network of over 1.4 million trained, skills-based retail representatives address customers’ most urgent store needs, driving more effective sales and merchandising actions. Covering more than 99 percent of all U.S. retailers, customers have the ability to deploy Trax Flexforce to all channels, all formats, and all locations.
- AI-Driven Merchandising: Trax leverages dynamic data signals to create prescriptive execution tasks that prioritize customer merchandising needs. The platform automatically identifies the right representatives for each job based on more than a dozen factors, including execution skills, category experience, location, and workload. Photos taken in-store generate additional tasks to further drive real-time improvements, consistency and value of each store visit.
- Key Impact Reports: Digitizing every product SKU and in-store action provides total transparency to customers. Image validation of all in-store tasks guarantee customers pay only for the actual work completed. With a rich set of Trax shelf data and KPIs, customers are also able to directly measure the value of merchandising actions and make more informed decisions for overall business strategy.
Consumer goods manufacturers and retailers around the world leverage Trax’s in-store execution, store monitoring and retail analytics solutions to better 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.
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