WANTD

The gap

Before writing a line of code I looked at the leading apps in three categories — wishlist and gift apps (GoWish, GiftList, Giftful, Elfster), savings-goal apps (SavePal, Savings Goals) and gift coordination. Each solves one part well. None connects "I'm actively saving for this" with "my friends can see it and secretly coordinate gifts" and "once I have it, it moves to an archive I can recommend from".

That combination became the product's reason to exist.

What the market taught me

I turned the research into a table of success factors and decided for each one whether WANTD adopts it now, later or never:

• Universal link parsing is the feature every wishlist app is judged on — paste any shop URL, get title, image and price. → Core of the MVP.
• Secret reservations keep the surprise: hidden from the owner, visible to other gift-givers. → Phase 2, the heart of the social layer.
• Sharing via link without sign-up is the lowest barrier and the main growth driver. → Built for guests with no app and no account.
• Price-drop alerts are a proven retention hook. → Built as a background job.
• Discovery feeds and group gifting increase engagement. → Deliberately postponed.

Scope: solo MVP first, social second

The roadmap was split into phases with a clear exit for each:

1. Setup — Expo, design tokens, auth skeleton, demo mode without a backend.
2. MVP solo wishlist — items, link preview, saving progress, archive, sharing. → first TestFlight build.
3. Social — friends, other people's lists, anonymous or named reservations, double-purchase protection. → store-launch candidate.
4. Growth — price alerts, birthdays, later group gifting and discovery.

Product decisions I'm proud of

• The guest is the growth loop. A shared list opens on the web without the app or an account — "the recipient without the app is exactly the person you want to win."
• Trust is enforced, not promised. The list owner can never see reservations; this is guaranteed by database row-level security and covered by tests.
• Automation never overrides intent. Price tracking runs every six hours but never changes a user's savings goal.
• Birthdays as the reason to come back — an "upcoming" section turns a static list into a recurring habit.
• Monetisation thought through early: freemium, affiliate links from the archive, and explicitly no fee on group gifting — the market treats that as a trust killer.

Shipping it

• Link previews run in a server-side function, with an on-device fallback because some large retailers block data-centre requests.
• Saving progress shows milestones at 25/50/75/100 % and a pace estimate ("6 weeks to go").
• Launch work beyond code: Sign in with Apple, in-app account deletion, GDPR privacy policy and imprint, store listing and screenshots, a launch checklist and a device test script.
• Result: a production build on TestFlight and a live web companion — nine days after the first commit.

What I'd measure next

Analytics is planned but not yet implemented — so there are no usage numbers yet. The metrics I'd track first: share-link → guest reservation rate, items added per user in week one, and return visits around friends' birthdays.

What I learned

Writing the plan first — market, MVP, phases, decisions — made AI-assisted building fast and focused. The tools can write code quickly; deciding what not to build is still the product manager's job.