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michael_lifecycle
Problem Solver I
Problem Solver I
September 27, 2026
Solved

Is “45 days since last purchase” enough for a win-back segment?

I’m testing a simple question around win-back segmentation:

If a product normally has a 30–45 day reorder cycle, is “45 days since last purchase” enough to decide that a customer should enter a win-back flow?

I’m finding that the same lapsed window can represent very different situations:

• genuinely overdue
• naturally low-frequency
• stockpiling after a large order
• seasonal purchasing
• recently engaged but not ready
• previous support issue
• little current buying intent

So I’m considering combining:

1. Product-specific reorder behavior
2. Purchase frequency
3. Recent clicks / site activity
4. Previous support interactions
5. Order-size anomalies

Then testing different actions by group rather than giving everyone the same discount.

Curious how others handle this in Klaviyo:

Do you use a fixed “days since last purchase” rule, or combine it with product/customer behavior?

ArpitBanjara
Principal User II
September 27, 2026

Hey ​@michael_lifecycle 

A fixed 45 days won't work for everyone, because the same gap can mean something different for each customer. Klaviyo has a better option built in called Expected Date of Next Order. It looks at each customer's own past orders instead of using one number for the whole list, and Klaviyo's own team suggests using it over Churn Risk for win back flows, since Churn Risk tends to flag brand new customers as "at risk" right after their very first order. You can read more on how it works here: Klaviyo's guide to predictive analytics.

This feature only turns on once your account has at least 500 customers who've ordered, 180 days of order history, and some repeat buyers. If your account isn't there yet, try Average Days Between Orders instead of a flat 45. It's a simple calculation based on real order history, so it works even before predictive analytics kicks in.

Clicks and site visits are easy to add in. Support tickets and unusual order sizes aren't something Klaviyo tracks by default, so if you want to use those too, your helpdesk tool would need to send that info into Klaviyo first. Until then, it's easier to just handle those cases by hand, separate from the main segment.

I hope this helps and thank you for sharing your question here in the community.

Cheers,

Arpit.

Arpit Banjara - Lead Email Marketing Specialist at Flowium

3 replies

ArpitBanjara
Principal User II
Principal User II
September 27, 2026

Hey ​@michael_lifecycle 

A fixed 45 days won't work for everyone, because the same gap can mean something different for each customer. Klaviyo has a better option built in called Expected Date of Next Order. It looks at each customer's own past orders instead of using one number for the whole list, and Klaviyo's own team suggests using it over Churn Risk for win back flows, since Churn Risk tends to flag brand new customers as "at risk" right after their very first order. You can read more on how it works here: Klaviyo's guide to predictive analytics.

This feature only turns on once your account has at least 500 customers who've ordered, 180 days of order history, and some repeat buyers. If your account isn't there yet, try Average Days Between Orders instead of a flat 45. It's a simple calculation based on real order history, so it works even before predictive analytics kicks in.

Clicks and site visits are easy to add in. Support tickets and unusual order sizes aren't something Klaviyo tracks by default, so if you want to use those too, your helpdesk tool would need to send that info into Klaviyo first. Until then, it's easier to just handle those cases by hand, separate from the main segment.

I hope this helps and thank you for sharing your question here in the community.

Cheers,

Arpit.

Arpit Banjara - Lead Email Marketing Specialist at Flowium
michael_lifecycle
Problem Solver I
Problem Solver I
September 28, 2026

Thanks Arpit — this is very helpful.

The distinction between Expected Date of Next Order and a fixed 45-day rule is especially useful.

It also highlights the part I’m more interested in testing: not simply identifying who is “lapsed”, but combining purchase timing with signals Klaviyo doesn’t fully capture — such as support issues or unusual order patterns — and then deciding whether there is actually a recoverable opportunity.

So the workflow I’m testing is becoming:

existing Klaviyo signals → additional evidence → likely cause → action → incremental verification.

I’ll keep the first test deliberately small rather than trying to replace what Klaviyo already does.
Thanks again for the practical detail.