Time to first payment

Time to first payment is the average elapsed time between a customer entering the funnel and completing their first paid transaction. It captures the entire stretch from the moment someone signs up, starts a trial, or is acquired through to the point money actually changes hands for the first time. If 1,000 customers sign up in a month and take a combined 5,000 days to reach first payment, the average time to first payment is 5 days.

The metric matters because the speed of the first payment shapes everything after it. A customer who pays quickly has found the value, cleared any pricing or checkout friction, and is far more likely to stay. A customer who lingers unpaid is at risk of churning before they ever become revenue. Time to first payment is therefore both a conversion signal and an early predictor of customer lifetime value.

It sits close to the trial conversion rate but answers a different question. Conversion rate tells you how many trials become paying customers. Time to first payment tells you how fast they get there. Two products can convert at the same rate while one collects revenue in days and the other in weeks, and that difference compounds across cash flow, payback, and momentum.

Time to First Payment = Average (First Payment Date - Signup Date)

For each customer who has made a first payment, subtract their signup date from the date of that payment, then average across the cohort. Use the median when the distribution is skewed, which it usually is, because a few very slow payers can pull the mean well above the typical experience.

The hardest decision is how to treat customers who have signed up but not yet paid. Excluding them makes the number look faster but hides everyone who is stalling. A cleaner approach is to measure time to first payment by signup cohort and report it alongside the share of the cohort that has paid at all, so a fast average among few payers cannot masquerade as success. Always work from successful settled payments so that failed attempts and reversed transactions do not distort the timing.

1. 1

   Signup date

   The start of the interval. Pick one consistent definition, whether account creation, trial start, or first contact, and apply it to every customer so cohorts are comparable.

2. 2

   First payment date

   The date of the first successful, settled paid transaction for that customer. Exclude authorisations, pending charges, and failed payments so the metric reflects revenue that actually landed.

3. 3

   Cohort window

   The signup period you are measuring. Grouping by signup cohort lets you compare how quickly each intake of customers reaches payment without later cohorts distorting earlier ones.

4. 4

   Unpaid handling

   The rule for customers who signed up but have not paid. State whether they are excluded or counted against the cohort, and report payment share alongside the average so the two cannot diverge unnoticed.

A metric tree breaks time to first payment into the stages a customer passes through on the way to paying, and each stage has a different cause and a different owner. The gap between signup and first payment is rarely one problem. It is usually a sum of onboarding time, the moment value is felt, the decision to buy, and the mechanics of checkout, and only one of those is typically the bottleneck.

Time to first payment varies widely by business model, so the most useful benchmark is your own cohort history. Self-serve products with a card on file at signup can collect within minutes, while sales-assisted and enterprise motions may take weeks because of procurement and approval. The ranges below give a rough sense of what good looks like for each motion.

Watch the trend more than the absolute figure. A median time to first payment that is falling means the path to revenue is getting faster, which usually improves cash flow and shortens payback. A rising figure at stable acquisition volume is the warning sign, because it means friction is building somewhere between signup and checkout. Pair the interval with the share of each cohort that has paid, since a fast average among a shrinking set of payers hides a conversion problem rather than solving one.

Shortening time to first payment means removing delay from the specific stage that holds customers up, not from the journey as a whole. Measure each stage, find the one that adds the most days, fix the cause there, then re-measure to confirm the headline interval actually moved.

1. 1

   Counting authorisations as payments

   An authorisation or pending charge is not settled revenue. Measuring against it understates how long real money takes to arrive. Use only successful, settled transactions as the end of the interval.

2. 2

   Excluding unpaid signups silently

   Dropping customers who have not paid makes the average look fast while hiding everyone who stalled. Report the share of the cohort that has paid alongside the interval so the two cannot drift apart unnoticed.

3. 3

   Mixing different starting lines

   Trial start, account creation, and lead capture produce very different numbers. Mixing them in one figure makes it meaningless. Pick one consistent signup definition and apply it across every cohort.

4. 4

   Averaging across mismatched motions

   A self-serve customer and an enterprise customer pay on completely different timelines. Blending them hides the real picture. Segment by motion before reading the number or comparing it to a benchmark.