Case study 03 · Sheypoor marketplace
Save it once.
Find it sooner.
Turning an underused Save Search feature into a visible, low-friction part of browsing—then measuring each change independently.
01 · Context
A useful feature
nobody noticed.
Save Search lets people keep their criteria and receive notifications when matching listings appear. Yet awareness and usage were extremely low, weakening a natural path back into Sheypoor.
Regular marketplace users—especially people repeatedly looking for homes, vehicles, or rentals—were manually rebuilding the same searches instead.
02 · Research
Trace the whole
return loop.
The question was bigger than whether the save control was usable. I examined discovery, saving, notification delivery, and the moment users returned.
Search → Save → Notify → Return
03 · Key insights
Low adoption had
more than one cause.
Visibility was the first barrier, but the post-save experience also had to earn trust.
Discovery came too late
The option appeared only after users applied several filters, so many never encountered it.
The flow blocked intent
Restrictions before saving added friction at exactly the moment users wanted continuity.
Alerts did not always help
Notification delays, SMS issues, and irrelevant alerts reduced the value of saving.
Return behavior had a rhythm
Clear peak hours showed when people were most likely to revisit saved searches.
04 · Design decisions
Meet intent
in the moment.
The redesign treated Save Search as a journey rather than a single control.
Remove unnecessary restrictions
Let people save earlier instead of requiring multiple filters first.
Make the feature visible in results
Add a dedicated section where recurring search intent is already clear.
Prompt in context
Use a lightweight toast after users browse several listings, when saving becomes useful.
Repair the notification loop
Improve timing and relevance while reducing incorrect or excessive alerts.
Surface value
at the moment
of repeated intent.
05 · Rollout & evidence
Learn one change
at a time.
Each improvement was released separately with gaps between launches. That made it possible to isolate behavioral impact instead of guessing which variable moved adoption.
The redesign established a stronger foundation for Save Search by making it easier to discover, access, and understand inside the browsing journey.