In a recent post, we discussed how advancements in artificial intelligence (AI) are driving disruption across the retail and CPG landscape, helping companies automate and optimize store visits and SKU analysis. Here, we highlight success stories and case studies showcasing how three companies significantly improved product availability, brand visibility, and customer satisfaction using AI-powered, end-to-end retail solutions.
AI helps CPG and retail businesses overcome the traditional blind spot of what actually happens in in-store aisles and F&B outlets, sharpening their competitive advantage with powerful digital shelf analytics and market intelligence.
By harnessing deep learning and computer vision, AI applications provide sales reps with real-time data about critical product details such as pricing, shelf position, and promotions. When combined with data analytics on patterns and trends, these metrics enable more effective retail execution and better alignment with pricing strategies and procurement initiatives.managers insights on planogram compliance and product placement, and helping store managers make informed decisions on store conditions. Find out how.
Take the example of Henkel Beauty Care in Germany, a global leader with over 900 SKUs across six categories. Their manual procurement process and shelf audits previously limited the merchandizing team to only 10 minutes of active selling per store visit.
With Trax Retail Execution, reps now capture shelf images via smartphones, which are analyzed in the cloud to produce actionable shelf insights in near real-time. This transition to data-driven merchandising has streamlined operations, reducing time spent on distribution checks and data collection by 50%, and gave reps 150 percent more time to unlock revenue generation opportunities.
90% of Henkel’s beauty care revenues comes from their top 10 brands. Capturing granular information at the SKU level gave them access to rich field data that allowed the team to identify which of their core products were missing on shelves, and reduce out-of-stock by 4.3 percent. The ensuing corrective action led to a revenue uplift of more than 2 percent within three and a half months.
Importantly, granular SKU-level data helped identify missing core products, reducing stockouts by 4.3%. This led to a revenue uplift of over 2% within just three and a half months — a clear demonstration of how forecasting, segmentation, and spend management align to optimize the lifecycle of products on shelves.
Coca-Cola Amatil, a major bottler in the Asia Pacific, used Trax to optimize its retail execution strategy around iced tea. Despite holding a 15% higher product penetration than its competitor, Coca-Cola realized it had fewer total facings (-4%).
Trax’s insights showed that while competitor stores stocked an average of 6.58 flavors, Coca-Cola stores averaged just 3.76. Armed with these real-time insights and benchmarks, the company strategically targeted regions where shelf share was lacking rather than rolling out promotions blindly across all stores.
This focused approach resulted in a 5% increase in market share in two weeks, generating $27,400 in incremental sales. This case exemplifies how cross-functional collaboration between procurement teams, marketing, and sales, supported by AI-driven data analytics, drives successful category management.
Coca-Cola Hellenic Russia faced challenges monitoring brand presence and compliance in on-trade channels such as HoReCa, gas stations, and fast-food outlets due to the absence of a third-party audit system.
Trax’s solution automated this process, using image recognition to assess adherence to merchandising standards at the point-of-sale. For example, it tracked “Combo” activations that combined beverages, food items, and price discounts on menus or displays.
By streamlining these audits, Coca-Cola ensured quality management of brand activations and secured optimal ROI from agreed-upon touchpoints across multiple outlet types.
Trax’s AI-driven platform provides scalability across the entire category, enabling stakeholders to monitor stock levels, product descriptions, and pricing across thousands of stores. The platform supports onboarding of new users seamlessly, providing end users with dashboards delivering actionable insights to optimize supply chain management, supplier relationship management, and strategic sourcing.
This end-to-end approach enhances procurement categories by reducing lost sales and stockouts, improving supplier performance and supplier management, and enabling proactive responses to supply chain risks.
By integrating spend analysis and source-to-pay data, the solution helps companies reduce total cost while improving sustainability and operational efficiency.
As these examples show, AI-powered retail solutions such as Trax’s data-driven merchandising platform are fast becoming the source of shelf truth. They empower CPG companies to increase share-of-shelf, optimize pricing strategies, boost revenues, and maintain compliance with merchandising standards — all while delivering superior customer engagement and satisfaction.
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