How do you find customers for a Salla app?
Define the merchant profile, size it, then go to the merchants — rather than waiting for them.
Write down which merchants your app is actually for, find out how many of them exist, then reach them directly. Most apps for Salla (سلة), the Saudi e-commerce platform, rely entirely on Salla App Store placement — competing for the attention of merchants who are already looking, a small fraction of the ones who have the problem the app solves.
The developers who grow fastest treat merchant discovery as outbound research: define the profile, size the segment, build the list, and approach each merchant with a reason specific to their store. As of October 2026 there were 41,038 verified Salla stores to choose from, 40,085 of them active.
Why can't the Salla API find customers for you?
Because it only knows about stores that have already installed and authorised your app. The Merchant API works on behalf of a merchant who granted your app permission — it is how you serve customers, not how you find them. The full explanation is in Salla API vs a store database.
Step 1 — Write down the merchant profile
Be concrete. “Salla merchants” is not a profile; it is the whole platform. A usable one names characteristics you can observe from outside the store:
- Category — does the app solve a problem specific to fashion, beauty or food, or is it category-neutral? Salla stores fall into 21 categories in the SallaData build.
- Payment setup — which BNPL providers, such as Tamara or Tabby, the store already offers. Existing integrations often predict whether a store will adopt another.
- Business status — a commercial registration or VAT number suggests a registered company; a freelance licence suggests an individual seller. They buy differently.
- Store age — a store opened this year has different needs from one that has been trading for years. The dataset records when each store started.
- Reachability — whether the store publishes a phone, WhatsApp, email or social profile you can use.
Some useful traits are not public in structured form — catalogue size, traffic, revenue. Check those on the storefront for your shortlist rather than expecting them in a list.
Step 2 — Size the segment before you build
Knowing how many merchants match your profile changes decisions. A segment of 200 stores can be worked by hand and probably cannot support a low-priced subscription. A segment of several thousand justifies self-serve onboarding and paid acquisition. Two worked examples from the October 2026 data:
- An app for beauty stores. Beauty & Cosmetics has 3,584 stores, and 3,166 (88%) publish a phone number. Of the 3,576 that are active and categorised, roughly 2,080 would have both a phone and an email, at the average rate of 58%.
- A payments or BNPL app. 54% of categorised active stores already offer Tamara or Tabby. Your open market is the rest — roughly 14,080 of the 30,273 categorised active stores — and the stores that do offer BNPL are a ready-made list for anything that builds on it.
The “roughly” figures apply average field shares to a category; actual filtered counts are in the files. Sizing also exposes the failure mode where an app is built for a merchant profile that barely exists — usually discovered after the engineering is done.
Step 3 — Build a reachable list
Once the profile is defined and sized, the list is a filter over the same attributes. What matters is that each merchant on it has an identifiable reason to care: the category they sell in, the payment setup they run, how long they have been trading.
Approach merchants through the business channels they publish, and make the relevance explicit — a message that names what the store actually does outperforms a generic pitch by a wide margin. Personalise, keep volumes reasonable, and honour every opt-out.
Step 4 — Feed results back into the profile
Track which segments convert. If established home-goods stores respond and brand-new fashion stores do not, that is the profile telling you where the product fits. Narrow toward it, and re-size the narrower segment before you spend on it.
Sources and method
Category totals and phone counts are from the SallaData October 2026 build. Estimates multiply the number of categorised active stores in a category by the share of categorised active stores carrying each field, so they assume a category behaves like the average. The methodology explains how stores are verified and how categories are assigned.