ASO store conversion analysis
Separate store conversion changes into product-page efficiency and traffic-source mix.
End of analysisNext steps and reference material below. Not part of the analysis result.
Next step
App store traffic
Full journey
Store conversion dropped — should I change the screenshots?
If per-source conversion held, the cause is the traffic mix, not the page.
Method and FAQOpen only when you need the methodology
See the decomposition and its limits
When to use it
When blended store conversion falls noticeably, when organic installs drop during a period of paid scaling, or when you cannot tell whether screenshots need work. Four columns are enough: date, traffic source, product page views, installs.
What it computes
It splits the period in half, computes per-source conversion and install share, and decomposes the blended change into a mix effect (share movement) and an efficiency effect (per-source conversion change). The decomposition is the same residual-free math used for campaign performance variance.
How to use the result
If the mix effect dominates, investigate why traffic composition changed — paid scaling, featuring, seasonality — rather than the page. If the efficiency effect dominates, look at icon, screenshots, and rating. Confirm either with a store experiment.
Frequently asked questions
Can I use it without impressions data?
Yes. Without an impressions column only tap-through is skipped; page-to-install conversion and the per-source decomposition still compute.
If the mix effect dominates, is there nothing to do?
No — it means the page is not the fix, not that nothing is wrong. The next question is why the share of low-converting sources grew.
Does this establish cause?
It does not. It narrows where to look in observational data, and if ads, featuring, and seasonality all moved in the same window it cannot tell you which one did it.