We wanted deeper insight into how price changes affected not just conversion but the quality of users from different campaigns. Moving the funnel to the web let us collect richer signals from first touch through payment.
I started tracking these: first-touch campaign, pricing-variant exposure, checkout abandonment reason, trial-to-paid conversion, and 30-day retention by cohort. Correlating those showed that one ad set drove cheap conversions but poor retention at lower prices.
What metrics do you find most useful when deciding whether a price lift is worth the potential conversion loss?
I track conversion to paid, 7 and 30 day retention, and revenue per install by campaign.
If a price change converts fewer but higher quality users the math is clear.
I also keep an eye on refund rates right after checkout since that can hide a bad choice.
Short list I use: impression to checkout, checkout to paid, trial retention at 7 and 30 days, refunds, and revenue per campaign.
You need both conversion lift and cohort quality. Web funnels make it easy to break these down by utm so you can optimize ROAS not just conversion.
I focus on trial to paid and 30 day retention by campaign.
If a campaign converts well but churns quickly I reduce spend and test different price or onboarding copy.
track paid rate
track 30 day churn
track refunds
Price sensitivity shows up across funnel stages. Track conversion at each step but weight the metrics by lifetime value.
My recommended set: first touch source, click to checkout rate, checkout conversion, refund rate, trial-to-paid conversion, and 30/90 day retention. Then compute revenue per first touch and ROAS by campaign for each price variant.
That tells you if a higher price with lower conversion still improves unit economics.
Conversion and 30 day retention are the basics.
If you can tie revenue back to the ad campaign you’ll make smarter decisions.