How do we project sales?
To create our dataset, we use state-of-the-art machine-learning models which combine consumer panel data with the world’s largest truth set of online sales data.
Consumer panels help us identify traffic patterns on retail domains across the home, category, and product pages. We apply conversion rate models based on product-level sales data from more than 200K retailers to predict sales for each product. These bottom-up (from SKU-level) sales prediction models back into predictions of total retailer sales.
Methodology
We predict sales from the SKU-level upwards by estimating traffic to product and category pages for each URL and applying conversion rates as well as the retailers’ price points. These models are continuously calibrated against our set of live retailer data.
Accuracy rate
Overall accuracy rate within Grips for all countries and all categories:
| Conversion Rate | Average Order Value | Sessions | Transactions | Revenue |
| 95% | 96% | 70% | 71% | 72% |
Truth Data Source
Here, we present a detailed overview of the total coverage we possess for the selected countries. With our extensive truth data in these regions, we ensure a robust foundation for calculating accuracies and providing valuable insights to our clients.
| Country | Reported total ecommerce market [2021] | Grips tracked revenue | Grips tracked revenue on sku level |
| United States | $960,444,000,000 | 10.08% | 2.12% |
| United Kingdom | $177,465,300,000 | 17.70% | 6.18% |
| Germany | $127,500,000,000 | 28.50% | 11.29% |
| France | $92,710,000,000 | 11.06% | 3.92% |
| Netherlands | $30,600,000,000 | 30.73% | 23.94% |
| Poland | $28,900,000,000 | 33.30% | 25.72% |
| Brazil | $26,100,000,000 | 74.28% | 47.75% |
This represents the largest available, global sku-level dataset to date.