How AI & Computer Vision Merchandising are changing the Retail Industry

Digital Transformation in Retail

In the fast-paced world of retail, ensuring shelves are perfectly stocked, organized, and compliant with planograms is critical for driving sales and maintaining customer satisfaction. Traditionally, this process relied on manual audits, where merchandisers would painstakingly inspect every shelf, record data, and identify discrepancies. While effective to some extent, this method is time-consuming, prone to human error, and difficult to scale across multiple locations. 

Enter AI and computer vision technology—a game-changer for retail operations. By combining the power of artificial intelligence with advanced image recognition capabilities, these technologies are automating shelf surveys and transforming how consumer goods companies manage their merchandising strategies. From real-time inventory monitoring to automated planogram compliance checks, AI-powered shelf surveys are not only faster but also more accurate and insightful than ever before.

In this blog post, we’ll take you through the step-by-step process of how AI and computer vision work together to streamline shelf surveys. We’ll explore how these tools detect products, analyze shelf layouts, and provide actionable insights that help retailers optimize their operations. Whether you're a retailer looking to enhance efficiency or a tech enthusiast curious about the latest innovations in retail, this is your ultimate guide to the future of shelf management.

The Role of InStore Data

In today's competitive retail landscape, CPG manufacturers and retailers are turning to a game-changing asset: in-store data. This treasure trove of information, gleaned from shelf-level analytics, display effectiveness metrics, and product placement insights, is transforming how companies approach trade spend and promotion optimization. By harnessing advanced AI-powered tools, businesses can now measure promotional ROI with unprecedented accuracy, accounting for complex factors like cannibalization and seasonality. This data-driven approach enables the creation of hyper-personalized promotions, optimized pricing strategies, and strategic product placement that capitalizes on high-traffic areas. Real-time shelf data ensures promoted products stay in stock, while comprehensive analytics inform smarter trade spend decisions across the organization. The result? More targeted, efficient promotions that not only drive sales growth but also maximize the return on every promotional dollar spent. As the retail world continues to evolve, those who embrace the power of in-store data will find themselves at the forefront of trade spend optimization, poised for sustainable long-term growth in an increasingly competitive market.

Retailers and Consumer Goods companies use various methods to conduct shelf surveys and ensure proper merchandising. Here's an overview of how AI video technology is revolutionizing the traditional practice:

AI and Computer Vision-Enhanced Shelf Survey Process

1. Image Capture

• Use cameras (mobile devices) to capture using video recording overlapping images of retail shelves to ensure full coverage.
• For large shelves, employ image stitching to combine multiple images into a single, comprehensive view.

2. Product Detection and Recognition

• Use computer vision algorithms to detect and classify products on the shelves based on their visual features (e.g., brand, SKU, UPC).
• Reference images of products are stored in the system to allow accurate identification during analysis.

3. Planogram Compliance Check

• Compare the detected product arrangement against the planogram (store layout guidelines) using AI algorithms.
• Identify discrepancies such as misplaced items, incorrect facings, or missing promotional materials.

4. Shelf Share Calculation

• Calculate the percentage of shelf space occupied by each brand or product category based on the detected data.
• Highlight deviations from contractual agreements or merchandising goals.

5. Inventory Monitoring

• Monitor stock levels in real-time by detecting out-of-stock items or low inventory using AI-powered alerts.
• Automatically notify store managers for timely restocking.

6. Pricing and Tag Verification

• Verify that pricing labels are correctly placed and match the displayed products using image recognition5.
• Detect missing or incorrect price tags for corrective action.

7. Data Analysis and Reporting

• AI aggregates insights into dashboards or reports, providing metrics such as:
• Planogram compliance rate
• Shelf share percentages
• Out-of-stock trends
• Pricing accuracy.
• Generate actionable recommendations for improving shelf management.

As we move further into 2025,  major grocery chains are embracing a wide range of technologies, from AI to self-checkout systems, reshaping the supermarket landscape and setting new standards for customer service and operational efficiency. Major retailers like Safeway and Publix are leveraging cutting-edge AI and data visualization technologies to revolutionize their operations and enhance customer experiences. Safeway, for instance, has implemented an AI-powered data visualization program that has significantly improved inventory management, reducing on-hand warehouse stock and potentially lowering inventory carrying costs. 

Benefits of Using Computer Vision and AI

Real-Time Monitoring: Enables instant detection of issues like empty shelves or misplaced items.

Scalability: Automates surveys across multiple stores without requiring manual audits35.

Efficiency Gains: Reduces time spent on shelf surveys by automating repetitive tasks like counting facings or verifying pricing4.

Data-Driven Decisions: Provides actionable insights to optimize merchandising and improve profitability.

By integrating these steps with computer vision and AI, retailers can streamline shelf survey processes, enhance compliance with merchandising standards, and improve overall operational efficiency.