The wishlist treats every save identically while the blocking uncertainty is personal

Target segment:: genuine-intent savers (80% of corpus) — especially new/low-trust shoppers (23.7%, 2.81× trust lift) and fit-uncertain shoppers (24.5%, 3.99× fit lift).
Product outcome influenced:: Evaluate→Resolve→Bag progression inside the 30-day window (the funnel stage where 57.7% of friction sits).
Root cause:: a save carries no information about the uncertainty behind it, so nothing in the product answers the specific question (“will it fit me?”, “is it genuine?”, “what if I need to return?”) that stands between that user and purchase.
Existing workarounds:: screenshotting to friends, re-searching the item outside the app, reading reviews at decision time only, offline store checks — all demand leaves the app mid-decision (5% of relevant docs).

Why users should care

The app finally answers the exact doubt each saver has instead of nudging generically. Decisions get easier without discounts or urgency tricks.

Why the business should care

Wishlist saves are already-captured high-intent demand; lifting 30-day conversion raises purchase frequency from existing users with zero discount leakage.

Honesty note: engine segments are text-inferred and review-biased toward the frustrated; this is discovery, not proof. Primary interviews (n=6) are the confirmation step, and module emphasis (trust vs fit first) may shift with their findings while the core problem statement holds.