Location-based offers: map discovery, filters, permission and privacy

How location-based offer discovery works

Location-based discovery answers one question: what offers can I actually reach from here? The app takes the shopper's approximate position, or a chosen city, asks the server for branches with active offers in that area, and shows them as pins on a map and as a list sorted by distance.

The unit on the map is the branch, not the business. A coffee chain with several branches appears several times, each with its own location and only the offers valid there. That is why branch data is the foundation of a local offers app: if branches are missing, misplaced or out of date, the map misleads.

In T-CODE, the offers app FLUIDS designed and built, shoppers browse nearby stores and offers on a live map, filtered by city or category, then open a store's location on the map and reach out directly. A typical discovery flow in an app like this looks like this:

1. The shopper opens the explore view and sees pins for nearby stores, or stores in the selected city.
2. They narrow results with a category, such as restaurants or cafés, or with a search for an offer or store.
3. They tap a pin to see a store card with its branch and distance.
4. They open the store to read its offers, get directions or contact it.
5. They act on an offer, which is where an account becomes necessary.

City and category filters: why the map needs both

A map alone is not enough; shoppers also need to filter by city and by category. Each filter solves a different problem.

• City works without location permission, supports people planning ahead ("what is on in Al-Khobar this weekend?"), and gives the app a sensible default when GPS is unavailable or imprecise.
• Category cuts a busy map down to what the shopper wants right now, such as restaurants, cafés and bakeries, beauty or retail.
• Search finds a specific store or offer by name when the shopper already knows what they are looking for.

Filters should combine predictably: a city plus a category shows only that category in that city, and the map and list stay in sync. Keep the category list short and owned by the platform rather than typed freely by merchants, or the same idea will appear under several spellings. For a bilingual app, store city and category names in both Arabic and English, and let shoppers search in either.

How to ask for location permission

Ask for location permission when the shopper chooses to see what is near them, not the moment the app opens. A permission prompt on first launch, before the shopper knows what the app does, is the easiest one to refuse, and once refused, the system prompt usually cannot simply be shown again; the shopper has to change it in settings.

A respectful permission flow follows a few rules:

1. Let the app work first. Show offers for a default or chosen city so the shopper sees value before any prompt.
2. Ask in context. Trigger the system prompt when the shopper taps a "near me" control or opens the map.
3. Explain the reason in plain words. The purpose string should say the location is used to show nearby offers.
4. Request only while in use. An offers app has no need for background location, and requesting it invites store review questions.
5. Accept approximate location. Recent iOS and Android versions let users share an approximate position; nearby offers usually work fine with it.
6. Handle refusal gracefully. Fall back to the city filter and offer a way to enable location later from settings.

Apple App Store and Google Play review both look at why an app requests location and whether the explanation matches its use. A clear purpose and a working fallback make that review straightforward.

Protecting location privacy

The safest location data is the data you never store. To show nearby offers, the server needs the shopper's approximate position for one query; it does not need a history of where that person has been. Design for that from the start:

Location data combined with an account is personal data. In Saudi Arabia, the Personal Data Protection Law (PDPL) governs how it is collected, used and kept, and the Saudi Data and AI Authority (SDAIA) is the body associated with it. Publish a plain privacy policy, collect only what the feature needs, and confirm your specific obligations with qualified counsel.

Privacy also affects merchants. A merchant dashboard can show views and confirmed uses per branch without revealing who was standing where. Keeping those numbers aggregate protects shoppers and keeps the merchant's tools simpler.

Guest browsing: let people look before they sign up

Guest browsing means a new user can explore the map, filters, stores and offers without creating an account. It removes a common barrier in the first minute: asking for a phone number before showing anything worth signing up for.

T-CODE follows this pattern: browsing is available to guests, and reserving an offer code requires a signed-in account. The account is requested at the moment it is genuinely needed, because a reserved code has to belong to one person for limits and expiry to work.

Three details make the switch from guest to member smooth:

• Keep context. After sign-up, return the shopper to the offer they were about to reserve, not to the home screen.
• Explain the reason. A short line such as "Sign in to reserve this code" makes the request feel fair.
• Carry preferences over. The chosen city and category should survive the sign-up step.

Guests should also stay anonymous to merchants. A store can see aggregate numbers such as views and followers, but a guest's browsing should never become a named lead. That boundary is worth stating in the privacy policy.

Keeping the map accurate and fast

A map is only as good as its branch data. Let merchants place each branch with a draggable pin rather than typing coordinates, and confirm the address on the map before saving. In T-CODE, merchants keep contact details and locations current and add branches, showing or hiding each one as it is ready; a hidden branch should vanish from discovery immediately, and so should its offers.

Speed matters as much as accuracy. Query only the visible map area, cluster pins when the shopper zooms out, and use a spatial index in the database so "nearby" stays fast as the number of branches grows. Exclude ended and sold-out offers in the query itself, not in the app, so a shopper never travels to an offer that is already gone.

If you are planning a product like this, start with how to build a coupon app, then see how codes are reserved and confirmed in coupon redemption codes. The same discovery patterns apply to service businesses, as our guide to a home services booking app shows. FLUIDS builds products like this as marketplace platforms, and our e-commerce and retail page covers the wider sector; the T-CODE case study shows the map in context, and scope, price and timeline are agreed in a written proposal.