Collect the matching inputs.
Sort valid orders per customer and calculate each first-to-second interval. Report cohort dates, observation cutoff, repeat rate and preferably median plus distribution.
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Customer loyalty · Second-order timingTime to second purchase.
Time to second purchase.
Find the right moment to reconnect.
Time to second purchase is the elapsed time between a customer’s first and second valid orders. This calculator shows the mean among customers who have repeated; the median is often useful alongside it.
Start with your numbers.
Second-order timing calculator
01 / The definitionWhat is
What is
time to second purchase?
Time to second purchase is the elapsed time between a customer’s first and second valid orders. This calculator shows the mean among customers who have repeated; the median is often useful alongside it.
Recent customers who have not repeated are right-censored, not zero-day repeaters. Comparing only repeaters can make a weak cohort appear fast.
02 / The measurementA clear formula.
A clear formula.
A useful comparison.
Second-order timing =Total days between first and second orders ÷ Customers with a second order
Read the result in context.
Compare equally mature cohorts by first product and replenishment cycle. Use survival analysis when accounting formally for customers still waiting to repeat.
Document the comparison.
Keep the reporting dates, population, exclusions and calculation with every result. Show underlying counts as well as the average or rate, so a small sample does not look more conclusive than it is.
Illustrative example
From inputs to insight.
- Total days between first and second orders
- 9,000
- Customers with a second order
- 200
- Second-order timing
- 45 days
45 days on average among the 200 customers who repeated. Non-repeaters are not included in this mean.
Try your numbers ↑03 / Industry benchmarksContext first.
Context first.
Targets second.
Compare equally mature cohorts by first product and replenishment cycle. Use survival analysis when accounting formally for customers still waiting to repeat.
We have not verified a public industry benchmark that matches the exact definition used on this page. A precise-looking generic range would hide important differences between businesses.
Build your own benchmark
Start with a comparable baseline.
01
Match the population.
Choose the category, channel or customer cohort you can compare consistently.
02
Let the data mature.
Use complete periods and allow the purchase, attribution or returns window in your definition to close.
03
Test a specific opportunity.
Set a target from your economics and observed variation. Keep conversion and contribution in view.
04 / Clerk customer evidenceReal stories.
Real stories.
Clear measurement limits.
We have not verified a published Clerk customer case with a quantified time to second purchase result matching this page’s definition.
What would count as useful evidence?
A case reporting total days between first and second orders and customers with a second order, with the time window and comparison method. General revenue growth or a related engagement metric does not establish this result.
Customer stories describe individual implementations. They are not industry benchmarks, guarantees or standardized causal tests.
05 / Put it into practice
Find the right moment to reconnect.
Time useful reminders around observed replenishment needs and test timing with a holdout rather than treating the average as everyone’s ideal send date.
06 / Common questionsA little more
A little more
clarity.
How do I calculate time to second purchase?
Total days between first and second orders ÷ Customers with a second order. 45 days on average among the 200 customers who repeated. Non-repeaters are not included in this mean.
What should I check before comparing results?
Recent customers who have not repeated are right-censored, not zero-day repeaters. Comparing only repeaters can make a weak cohort appear fast.
What is a good time to second purchase?
Compare equally mature cohorts by first product and replenishment cycle. Use survival analysis when accounting formally for customers still waiting to repeat.
Does a higher attributed result prove incremental growth?
No. Attribution connects an interaction with an outcome under a reporting rule. To estimate what a change added, compare randomly assigned treatment and control groups using the same eligible population and a predefined measurement window.
Keep learning
Connect the numbers.
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