The data is there. Nobody has time for it
The account manager knows the retailer's supplier portal has daily or weekly sales by store and stock at each depot. Once a month, before a meeting with the buyer, they download a few files, paste them into Excel and build a chart. The rest of the time the data sits unused.
When the buyer asks why sales dropped in the north, or whether the promotion cannibalised the core range, the answer is an evening of spreadsheet work, if there is an answer at all.
Why portal data goes unused
The files are large, in each retailer's own layout, and use the retailer's product codes and store numbers. Combining them with your own order and despatch data takes real work, and it has to be repeated every week to be useful. So it gets done once before a meeting, if at all.
- Each retailer's portal exports a different layout.
- Product codes differ from yours and need mapping.
- Files are too large for comfortable use in Excel.
- Downloads are manual and easy to forget.
- Nobody owns the job of turning the data into a report.
What not using it costs
Stores that have stopped selling a line go unnoticed until the range review. Depot stock runs high before a promotion or low during one, and your orders arrive without warning. Buyer meetings rely on the retailer's view of the numbers rather than yours. And your own forecast ignores the most direct signal of demand you have access to.
Access to each portal and what you can do with the data is set by each retailer's terms, which we work within.
The data pipeline we build
- Files are collected from each retailer's portal on a schedule, using its export or API where the portal and your terms allow, or from a folder your team drops them into.
- Each retailer's layout is mapped to a common format, and product codes are matched to yours using the same cross reference used for orders.
- The data is stored in a database that handles the volume, such as a cloud database on AWS or Azure.
- Sales and stock are joined to your own orders and despatches, so you can see ordering against selling through.
- Weekly reports show rate of sale by product and region, depot stock cover in weeks, promotional uplift against the base, and stores with no sales of a line they should stock.
- Reports appear in a dashboard your team can filter, and a summary is emailed to account managers before their weekly calls.
| Report | Useful for |
|---|---|
| Rate of sale by region | Buyer meetings and range reviews |
| Depot stock cover | Anticipating orders and planning |
| Promotional uplift | Judging what a promotion delivered |
| Zero-sales stores | Raising availability with the retailer |
What account managers walk in with
Weekly numbers, without an evening of spreadsheet work. The planners see depot stock heading down before orders spike. Commercial conversations start from your own analysis. And the forecast can draw on real sales rather than orders alone.
Availability issues become something you can raise with evidence. A list of stores that normally sell a line and have sold none for two weeks is a far better opening for a conversation with the retailer's category team than a sense that sales feel low.
It also changes how promotions are judged. Instead of looking only at what the retailer ordered from you, you see what actually sold through the tills, how much came from stealing sales from your core lines, and how long depot stock took to return to normal afterwards.
Is this data sitting unused at your business?
- You have retailer portal access but rarely download the data.
- Reports are built by hand before buyer meetings.
- Depot stock levels surprise your planners.
- Stores that stopped selling a line go unnoticed.
- Your forecast uses orders only.