Know whether your game pays back before you spend the money.
Kadar models your cohorts day by day, including the organic installs your players generate. Most studios are underestimating their own return on ad spend.
The loop
Your players are producing your next cohort.
Players retain
Retained players generate organic installs
Organic installs become players
Which means your retention is an acquisition channel, and your paid installs are worth more than your dashboard says.
What you get
Four answers, on every run.
Cohort revenue and active users by day
Every cohort tracked forward day by day, not a blended monthly average that hides the shape.
Payback day and return on spend
At day 7, 30, 90, 180 and 365, so you can see it against whatever horizon your board uses.
Which single assumption is driving your result
Sensitivity ranked, so you know whether the answer turns on retention, on price, or on spend pacing.
What would have to change to hit your target
Stated as a number on one input rather than a list of things to try harder at.
Start from your genre
No cohort data yet is not a blocker.
Kadar ships fitted defaults across five genres and three monetisation models, so a game with no live data still starts from a credible model. You replace the defaults with your own numbers as they arrive. The fitted parameters are part of what you pay for and are not published.
Honest limits
What Kadar does not do.
It is not an attribution tool. It will not tell you which campaign a player came from.
It does not connect to your ad accounts. You bring the numbers, Kadar does the modelling.
It will not tell you your game is good. It tells you what your own assumptions imply, and which one of them is doing the work.