What's the difference
Sell-in data is the stock sold to retailers or distributors by manufacturers. They’re shoes in stock rooms, racquets on racks, and hiking trousers hanging up in shops. Sell-in can also sometimes be referred to as shipment data.
Sell-out data is the stock sold by stores or by manufacturers to end consumers. It’s shoes on runners’ feet, racquets in tennis players’ hands, hiking trousers covered in mud halfway up a mountain. Sell-out data is often referred to as sell-through data, as retail audit data, or, as we most commonly call it, market intelligence.
Both sell-in and sell-out data can be analysed on a market level if multiple data providers (e.g., retailers for sell-out data or manufacturers for sell-in data) agree to share their sales figures with a trusted research agency who then aggregates the data.
However, with shifts to D2C selling, it is also important for sell-out data to collect data from manufacturers on their sales to end consumers, whether through company concept stores or company websites.
What are the challenges of collecting the data?
For both types of data, you need to establish a representative and reliable panel of data providers. It’s no good having a variable sample with some people failing to submit for each reporting period – perhaps quarterly for sell-in, and monthly for sell-out – although, sell-out could be even more timely. Data missing from providers each month masks trends and leads to bad observations and poor strategic decisions implemented because of those observations.
In industries with a relatively small number of major brands, like tennis, sell-in data is much easier to collect than it is in industries with a very large number of brands (like outdoor or cycling). In those industries, sell-in data would be almost as complex to process as sell-through data, reducing one of sell-in’s key advantages.
For sell-through data, a key challenge is establishing fast data turnarounds. The volume of raw data captured tends to be many orders of magnitude greater than with sell-in, and data is provided in different formats, through different systems and EPOS providers. For instance, our Outdoor Market Intelligence Service tracks almost 3 million unique SKUs across more than 1,000 brands and over €5 billion of sales value (as of December 2025 – these figures will inevitably grow with time). A strong, secure database, accurate categorization, and data visualisation systems are essential. Investment in a dedicated tech stack and expert team is essential.
What are the advantages of sell-in data?
Sell-in data tends to be easier to capture and track. Because in most sports sectors there are fewer manufacturers than retail stockists, or at least fewer large manufacturers. As a result, it is easier to get a robust, reliable sample of equipment sell-in. That generally makes sell-in data cheaper for clients to get their hands on. It’s a good guide to distribution levels, which often makes it helpful for identifying if there are stores or types of stores or category channels where a brand is struggling to sell but where rivals may have a good foothold. It’s also a useful tool for managing supply chain and inventory planning.
It can be a good precursor to retailer satisfaction type research, encouraging a brand to commission quantitative or qualitative surveys with key retailers to understand barriers to greater sell-in or unlock retailer interest in new product categories.
For brands who don’t sell D2C, sell-in data is effectively the company’s bottom line, but over-focusing on the sell-in data can lead to blinkered marketing and product development teams. Far from putting the customer first, it’s easy to grasp how this approach can lead to conservative design. Also, sell-in data, because it focuses on retail price, doesn’t account for the impact of pricing strategies or discounting, which is a major reason sell-out is often preferred.
What about sell-out data?
The chief advantage of sell-out data is that because it reflects true consumer demand in close to real-time, it’s a much better barometer of long-term success and health. It’s one thing to sell to retailers, but if retailers are unable to shift that stock or are forced to heavily discount it to achieve sales, it’s a safe bet that they won’t be ordering as much stock from a brand in future.
Sell-out data can also offer greater transparency that plays a role in relationship building and management. For example, brands wanting to place new stock with a retailer can prove success elsewhere or explain why consumer buying habits mean that a new line or item is likely to prove popular. Equally, when retailers want to start a conversation with brands about why they’re struggling to shift particular items, they are armed with robust evidence on an individual and market level.
Market-level sell-out data can also help (much more so than sell-in data) to avoid the bullwhip effect which occurs when retailers overreact to small, temporary, or incorrectly observed demand shifts. This overreaction travels upstream in the supply chain, leading to potential overordering, which eventually leads to discounting and potentially to increased production of units by manufacturers that they then find hard to sell into a by-then overstocked and discounting market. Aggregated data, and the ability to take both long and short term views at different times by accessing a sell-out dashboard can mitigate this risk.
More regular data collection and therefore reporting can be a key advantage of sell-out data, enabling faster decision making to respond to challenges or opportunities in the market.
Often but not always, sell-in data is collected less regularly, e.g., on a quarterly, half-yearly, or annual basis. Sell-through data is often, though not always, collected monthly, and some industries may collect more regularly still.
For multiple reasons, forecasting is ultimately easier with sell-out data. Because it’s more aligned with end users, marketing teams often prefer to work with sell-out data to get a closer idea of what sells where and when and under what conditions. It’s easier to track the impact of demand shifting events like sales and discounts, weather, or economic conditions.
At a market level, sell-out data, by virtue of being more on-the-pulse and more connected to the end consumer, is often more valuable for advocacy work and lobbying for an industry.
What about using both together?
Sell-in and sell-out data can be used in tandem for additional benefits. For example, calculations like subtracting sell-out from sell-in data reveal the stock situation across the market. Though some services do also specifically track and monitor stock levels across metrics like days’ inventory outstanding, stock turn, churn rate, etc. This can help avoid phenomena like rationing and gaming, where perceived scarcity in the market encourages retailers to over-inflate orders to secure preferential delivery or pricing terms or to guarantee that they receive high-selling or even limited-edition items or colourways before manufacturers run out of stock.
Logistics teams can benefit from monitoring patterns in both sell-in and sell-out together to identify potential incoming orders and manage production pipelines.
What has the sports industry got?
Traditionally, the sports industry has not been as advanced as categories like FMCG in terms of data generation. Often in the past, products promising ‘market information’ have been code for a hodge podge of sell-in, import / export data, and topline sales information from small clusters of retailers or brands. An array of data capturing techniques and timelines and a lack of standardization and quality control weaken the utility of the results.
Sell-out data, or at least robust sell-out data, has traditionally been harder to come by for the sports industry. However, done right (as we like to think we do with all our market intelligence programmes), good sell out data can be an early warning system for brands and retailers, a problem-solving tool, and a powerful advocacy tool for the industry. For example, sell-out data played a role in contributing to the UK government’s designation of bike shops as essential services during the coronavirus pandemic, thus supporting a cycling boom, albeit one that has now receded.
Many markets in sport are still muddling on without accurate sales-data visibility – sell-in or sell-out. However, Sporting Insights is gradually addressing this.
