customer retention formula might change the way we track subscription growth analytics

Been diving into retention metrics lately. Realized our current formula might be skewing our growth numbers.

Wondering if others have tweaked their retention calculations. How do you account for resubscribes vs new users in your analytics?

Good call on digging into this. We actually overhauled our retention tracking last quarter.

Now we split it into cohorts: new subs, reactivations, and continuous subscribers. Gives us a clearer picture of churn and reacquisition.

Big eye-opener was seeing how many ‘new’ subs were actually returning users. Changed our win-back strategy completely.

One tip: look at time between cancellation and resubscribe. We found a sweet spot for re-engagement emails around 45 days out.

Took some work to set up, but the insights were worth it. Helped us focus our budget where it actually drives growth.

We just count active subs every month. Pretty simple.

We track new and returning users separately. Found some interesting patterns that way. Helps us target our marketing better.

Tracking resubscribes separately is crucial. We’ve seen huge gains from this approach.

Look at resubscribe rates by cohort and time since cancellation. You’ll likely find patterns you can leverage.

Don’t just lump resubscribes in with new users. It masks churn issues and inflates growth numbers.

We now segment users into new, continuous, and reactivated. Each group needs different tactics.

Pro tip: Test different win-back offers based on why they left originally. Personalized approaches work wonders here.

I track both new and returning users separately in my analytics. It gives a clearer picture of true growth vs churn.

For resubscribes, I look at how long they were gone and why they came back. Helps fine-tune my retention efforts.

Splitting out these segments revealed some surprising patterns in our user base.