170%
increase in app installs
50%
decrease in cost per install
Snap's algorithm optimizes on what it sees. When Lumi fixed what it was sending, everything else followed.
80%
increase in ROAS
44%
drop in cost per purchase
Baytonia's TikTok campaigns were running on incomplete purchase data. The algorithm was learning from a partial picture of who was actually buying. Once the full signal reached the platform, targeting sharpened and cost came down.
205%
increase in purchases visible to Meta
182%
increase in ROAS
Jarir's campaigns were generating real purchases across Saudi Arabia and Kuwait that Meta couldn't fully see. The platform was allocating budget based on a partial picture of who was actually buying.
60%
decrease in cost per lead
29%
increase in leads
Snapchat was learning from half the picture. Server-side events through Journify were reaching the platform at twice the volume of pixel-only events. Once the full signal arrived, the algorithm stopped optimizing on noise and started finding real property buyers.
80%
drop in cost per purchase
15%
decrease in cost per click
Dr. Nutrition's Snapchat campaigns were generating real purchase activity the platform couldn't fully see. Cost per purchase was elevated and click quality was limited. The campaign wasn't the problem. The data feeding it was.
26%
increase in ROAS
17%
decrease in cost per purchase
Qasr Al Awani's Snapchat campaigns were running on an Event Quality Score of 4.3 out of 10. The platform's AI was learning from incomplete data. Fixing the signal changed what the algorithm could see and who it could find.
100%
increase in sign-ups
80%
drop in cost per lead
Al Hokail's Snapchat campaigns were generating real sign-up activity the platform couldn't fully see. Incomplete signals meant the algorithm was targeting on guesswork. Fixing the data layer changed what Snapchat could optimize toward.