AI Display Scoring: The FMCG Solution for Retail Execution

24/08/2026

In the Fast-Moving Consumer Goods (FMCG) sector, brand success is determined not just by product quality but by execution at the point of sale. However, businesses face a challenging reality: sales teams cannot control 100% of display compliance across thousands of retail locations nationwide. Reporting data is often subjective, lacks transparency, or is falsified, causing marketing and promotional campaigns to fall short of expectations.

To solve this problem, AI Display Scoring technology has become an inevitable trend. Instead of relying on paper reports or manually taken photos, businesses can leverage artificial intelligence to automatically analyze shelf images, providing accurate metrics on planogram compliance, share of shelf, and POSM status. Below is the operating mechanism and the practical benefits this solution brings to sales teams.

How AI Display Scoring Works

Display scoring technology operates through an intelligent image processing workflow, combining Computer Vision capabilities with business logic. The process begins when a sales representative uses a mobile app to photograph the entire shelf at a retail location. The captured image is then sent to the cloud for processing.

On the system, AI models trained on millions of data points regarding product packaging, shelf structures, and display positions perform the analysis. The system not only identifies products but also determines the precise location of each item relative to the planogram (standard display map). The result is a detailed report generated in seconds, allowing businesses to know exactly which products are missing, misplaced, or obscured.

Key Metrics in FMCG Display Scoring

To measure display effectiveness, AI Display Scoring focuses on critical quantitative metrics that assess the health of the retail point:

• Planogram Compliance: This metric measures the degree of adherence of the actual shelf to the standard planogram drawing. The system calculates the percentage of product positions that match the specified layout.
• Share of Shelf (SoS): This metric measures the proportion of shelf space your brand occupies compared to the total shelf space allocated for the product category. AI calculates this precisely based on the actual packaging area in the image.
• Out of Stock (OOS): Automatically detects empty spots on the shelf, instantly alerting teams to stockouts to prevent revenue loss.
• POSM Placement: Verifies the presence and positioning of Point of Sale Materials (Posters, Standees, Price tags) to ensure marketing messages are conveyed correctly.

Converting this data into specific scores enables businesses to easily compare performance across different retail locations, geographic regions, or individual sales representatives.

Tangible Benefits for Sales and Management Teams

Applying display scoring is not just a control tool but a powerful assistant that helps sales teams work more efficiently. For frontline staff, the application reduces the burden of manual record-keeping. Instead of counting products and writing in notebooks, they simply take a photo, and the system automatically completes the report. This allows them to dedicate more time to consulting and interacting with store owners.

For management, AI-derived data brings absolute transparency. They can monitor sales team activities in real-time, identify underperforming stores or areas with low compliance rates, and intervene promptly. Furthermore, historical data aids in trend analysis, allowing for the adjustment of display strategies to suit specific store types. In Vietnam, technology firms like AIVISION are partnering with many FMCG companies to build these AI models, ensuring high accuracy even under complex lighting conditions in traditional stores.

The Role of Agentic AI in Optimization Processes

By 2026, technology has evolved beyond mere recognition into the era of Agentic AI, where AI can autonomously execute tasks. In the context of AI Display Scoring, Agentic AI can automatically generate shelf adjustment proposals based on actual sales data and current visibility.

The system can automatically send reminders to sales representatives to improve scores at specific stores or suggest sending additional POSM to under-stocked locations. The combination of Computer Vision and Agentic AI transforms retail management into a closed-loop, automated, and continuously optimized process with minimal human intervention.

Challenges and Solutions for Implementation

Despite its many benefits, implementing display scoring faces several challenges. One of the biggest issues is the diversity of packaging and lighting conditions in traditional retail points (mom-and-pop stores). Dented packaging, poor lighting, or non-standard shooting angles can affect model accuracy.

To address this, businesses must choose technology partners capable of providing Custom AI models tailored to specific product characteristics and market conditions. Data science teams need to train models on the company's actual dataset to enhance recognition accuracy. The most effective AI Display Scoring solution is one that is flexible and can be quickly updated when new packaging is launched or display strategies change.

Preparation Steps for Implementing Display Scoring

Before deploying the solution, businesses must prepare their data and processes. Here is the necessary checklist:

• Review and digitize standard planogram files into digital formats (vector files or high-quality images).
• Collect a dataset of actual shelf photos from sample retail locations to train the AI model.
• Train the sales team on standard photography procedures: shooting angles, distance, and lighting.
• Establish target score thresholds for different store types and product categories.
• Integrate the scoring system with existing Sales Management Software (ERP) or CRM.

This thorough preparation ensures that once the solution is operational, the collected data will be accurate and immediately usable for strategic decision-making.

Frequently Asked Questions

Can AI detect product prices?

Yes, with integrated OCR (Optical Character Recognition) technology, the system can read and verify the accuracy of price tags against standard price lists, while also detecting torn or obscured tags.

What is the accuracy of AI Display Scoring?

Accuracy depends on image quality and the model training process. For models optimized for specific industries, accuracy typically exceeds 95% for basic metrics such as product recognition and Out of Stock detection.

Do small businesses need this solution?

Even small and medium-sized enterprises can benefit from strict point-of-sale control. The solution reduces field inspection costs, optimizes sales resources, and increases revenue by improving product visibility rates.

AIVISION helps enterprises turn AI into working systems. Explore our enterprise AI solutions, read more on the AIVISION blog, or talk to our team about your own use case.