How to build a 7-Eleven sales forecast a buyer will believe
Short answer: Build it from the bottom up: number of stores × sales per store per day × days, as three cases (low, base, high), and show the evidence behind each input: online sales, sales in other stores, comparable products. Say how much of the volume is new to the category rather than taken from products already there, and what you will do to reach the base case. A buyer has to answer for the whole category, so a forecast that shows where each number comes from beats a bigger number with no evidence.
Thai channel consultant Subpayanunt puts it plainly: buyers want data showing people actually buy, not estimates, and a business case showing how much additional sales the product brings to the category.
The formula
Monthly units = stores selling × sales per store per day × days in the month
Sales per store per day is the number that decides everything. It is explained in sales per store per day, and you can run it in the units per store calculator.
Evidence for each input
| Input | Where it comes from | Weak evidence |
|---|---|---|
| Stores | What the distributor and buyer say is realistic for a first listing | Assuming every store nationwide |
| Sales per store per day | Sales in other stores or chains, comparable products in the category, distributor experience | Your best online month divided by store count |
| Ramp-up | A slower first 4–8 weeks while awareness builds | Full sales from day one |
| Demand proof | TikTok Shop orders, repeat buyers, trial sales elsewhere | Follower counts and views |
| New to the category | Shoppers or occasions the category doesn't serve now | "Everyone will switch to us" |
Example (assumed numbers)
All figures are assumptions for illustration, not forecasts for any product:
| Case | Stores | Units per store per day | Units per month (30 days) |
|---|---|---|---|
| Low | 1,000 | 0.3 | 9,000 |
| Base | 1,000 | 0.5 | 15,000 |
| High | 1,000 | 0.8 | 24,000 |
Then check two things against the low case, not the base: can you still make money after every margin layer and fee (see how much margin you need), and can you produce and deliver the high case without running out of stock?
Mistakes that cost credibility
- Top-down maths, such as "1% of the Thai snack market". It says nothing about what one store sells.
- A single number. A range with clear assumptions looks more honest and is easier to agree on.
- No ramp-up. First weeks are almost always slower.
- Forgetting stock-outs. A forecast that assumes perfect availability will miss.
- Mixing sell-in and sell-out. Distributor orders are not store sales; see sell-in vs sell-out.
Show how you will reach the base case
A forecast reads better next to a plan: which stores or areas get content and promotion support, when, and how you'll measure it. See what to prepare for the buyer meeting and the proposal pack.
Limits
- Store counts for a first listing and the sales level needed to keep a slot are decided by the retailer and are not published.
- The example numbers are assumptions only.
FAQ
We have no sales data at all. Can we still forecast? You can, but say that the inputs come from comparable products, and expect the buyer to ask for proof. Building online sales first, for example on TikTok Shop, gives you real inputs.
Should the forecast include promotion periods? Show them separately. A buyer wants to see the level without a promotion as well as the lift with one.
Do distributors want the same forecast? They need it in sell-in terms too: how many cases they will order, when, and how much stock sits in their warehouse.
Sources
- Subpayanunt, proof of demand and the business case buyers expect (Thai): https://www.subpayanunt.com/post/how-to-enter-modern-trade-fmcg
- Subpayanunt, sell-in vs sell-out data from distributors (Thai): https://www.subpayanunt.com/post/distribution-strategy-fmcg-thailand
Related guide: How to Get a Product Into 7-Eleven Thailand
Next step: Not sure where your product should start? Send the Flowup team your product and where it sells today, on LINE or by email.
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