Computer Vision Retail Data | Share of Shelf | Laundry Detergent Category | Facings, Linear Space, Planogram Compliance, OOS Rates Across 8 Brands

This data is built entirely from computer vision, using image recognition to capture exactly what shoppers see when they walk into a store. Thousands of shelf photos are processed through high-accuracy algorithms to identify brands, measure their space, and determine whether stores are following expected layouts. The result is a granular view of retail execution that is both scalable and reliable. The Romania laundry detergent example presented here is a representative dataset, designed to show what is possible when these methods are applied. Although the data focuses on one country and one category, the same approach can be extended to any market and any product segment globally. At the core of this data are three complementary measures. The first is share of shelf by facings, which represents the percentage of total visible product units on a shelf that belong to a given brand. This metric captures the practical reality of how many packages a shopper will see at eye level and reach for. The second is share of shelf by linear space, which measures the actual horizontal length of shelf space dedicated to each brand. While facings emphasize count of products, linear share emphasizes physical space, both of which are critical for understanding visibility. The third is planogram compliance, which checks whether the shelf is set up as intended, identifying gaps, misplacements, or violations. These are complemented by out-of-stock rates, showing how often a brand’s products are missing entirely, which ties the visibility story back to availability and supply chain execution. Because all of this is powered by computer vision, the scale of coverage is enormous. Thousands of stores can be included, thousands of images processed, and every metric extracted automatically with high confidence. This creates a level of precision and breadth that traditional manual audits cannot match. The Romania dataset shows this approach in action for eight leading laundry detergent brands, but there is nothing unique about the category or the geography. The same process can be applied to snacks in Mexico, soft drinks in Nigeria, personal care products in Indonesia, or baby formula in Poland. Any category where products appear on shelves can be tracked with the same rigor and consistency. The value of this kind of data is immediately clear to brand managers. A marketing director for a detergent company can see how much shelf space their brand occupies compared to competitors, both in facings and linear share. If the share is below expectations, they can push retailers for better placement or adjust trade spending. Planogram compliance data reveals whether negotiated shelf agreements are actually being followed, providing leverage in retailer negotiations. Out-of-stock data identifies points of failure in distribution, highlighting where demand is being lost not to competitors but to empty shelves. These insights translate directly into revenue impact. Retailers themselves benefit from visibility into execution across their own stores. With computer vision data, they can measure compliance with their own merchandising standards, identify stores that need intervention, and benchmark performance between outlets. This allows chains to enforce consistency, improve shopper experience, and optimize category management. It also allows them to hold suppliers accountable while proving back to manufacturers that shelf space agreements are being respected. For distributors and logistics teams, the out-of-stock data is especially valuable. By identifying patterns in where products are missing, they can adjust delivery schedules, warehouse flows, or local stocking practices. Because this data can be collected weekly, monthly, or quarterly, teams can track whether changes in operations are making an impact, turning anecdotal feedback into quantifiable improvement. Category managers across industries can use this data to compare how visibility strategies work in different product lines. A company that sells both laundry detergents and dishwashing liquids could run the same analysis across both categories and identify whether performance issues are category-specific or systemic. By connecting the dots across multiple categories, they can improve strategy holistically rather than piecemeal. Investors and analysts gain a broader market view. Traditional retail data tells them what sold, but this computer vision data shows how products were presented to shoppers. If a brand’s sales are stagnant, the question is often whether it is due to weak consumer demand or poor visibility at the point of sale. Shelf share and compliance data provides that missing context, allowing a deeper understanding of competitive dynamics. Analysts covering multiple markets can use representative datasets like the Romania example to project how brand battles are playing out in other countries where similar forces are at work. Tourism and government bodies also find value when looking at consumer goods as part of economic monitoring. By tracking shelf share, they can see how local brands compete with multinationals, how distribution networks are functioning, and whether imported goods are overtaking domestic production. Planogram compliance can highlight the relative bargaining power of different players in the market. The representative nature of the Romania dataset is important to emphasize. This is not a one-off exercise; it is a demonstration of what computer vision can achieve in retail. The same model can be deployed in any market across LATAM, Africa, the Middle East, or Southeast Asia. It can track colas, chocolates, shampoos, baby diapers, or bottled water with equal precision. The brands and categories can be swapped, the geographies can be changed, and the frequency of collection can be tuned to fit business needs. The underlying method remains constant: capture images, process them with advanced computer vision, and extract structured insights about visibility, compliance, and availability. Over time, the value of this data compounds. Running the process quarterly allows stakeholders to see trends in how shelf space is evolving, whether compliance is improving or deteriorating, and whether out-of-stock problems are getting better or worse. Weekly or monthly runs create near-real-time monitoring of execution, enabling rapid response to issues. Because the data is standardized, it can also be compared across markets, creating benchmarks that show whether a brand is performing better in Eastern Europe than in Southeast Asia, or whether retailers in one region are more compliant than those in another. This dataset demonstrates the integration of artificial intelligence into core retail intelligence workflows. It proves that computer vision can be used not only to detect products but to provide commercial-grade insights that directly affect sales and profitability. It also shows that these insights are not limited to a single geography or product type. The Romania laundry detergent example is simply one representation of a much larger capability, one that applies to every corner of the retail world. In summary, what makes this data powerful is that it is derived directly from the shelf, seen through the eyes of shoppers, and measured with the precision of computer vision. It connects the visibility of products, the compliance of retailers, and the availability of stock into a single coherent view. It provides value to manufacturers, retailers, distributors, analysts, and policymakers. And it can be repeated anywhere, for any category, at any cadence. This representative dataset is not just a picture of Romania’s detergent market; it is proof of what is possible when computer vision is harnessed to measure and improve retail execution globally.