Repeat purchase retention
Shows what share of first time buyers come back to buy again, by month and by the category they bought first.
Authored by Sid Chaudhary
Founder & CEO at Intempt
Agentic workflow
Coming soon1 step Blu runs end to end. It drafts a plan and nothing runs until you approve it.
- 1
Track buyers back for more
Coming soonBlu builds a step
About this recipe
When to use it
First-purchase revenue is the easy part. Repeat purchase is where DTC economics actually work. This tracks second-purchase rate per cohort along with the time-to-second-purchase distribution, so you know both how many return and how long it takes.
What it delivers
Tracks second-purchase rate per acquisition cohort along with the distribution of time-to-second-purchase, quantifying real repeat-purchase behavior rather than assumed.
How you prompt this
Describe the outcome in your own words and Blu Agent maps it to this recipe, or run it by name. It drafts a plan first and nothing runs until you approve it.
Tell Blu Agent what you want
“Shows what share of first time buyers come back to buy again, by month and by the category they bought first.”
recipe.md preview
Built from the published catalog. Blu Agent's full step prompts load in your workspace when you run it.
Inputs & outputs
What the recipe needs before it runs, and what it leaves in your workspace.
Inputs
- Nothing declared beyond an Intempt workspace. Blu asks for anything else it needs as it goes.
Outputs
How the recipe runs
Blu follows the steps in order and shows you each one before anything goes live.
- 01Track buyers back for moreCreate a Retention report called "Purchase Retention". Anchor event: Placed order (per user, scope: their first Placed order to cohort by month of first purchase) Return event: Placed order (any subsequent order) Cohort granularity: Monthly Time range: Last 12 months Breakdown: By the first-purchase product category: derived from the first order's items.product_id resolved via Products object Compare: Previous period (prior 12 months of cohorts) Chart type: Retention curve plus cohort table with M1 / M3 / M6 / M12 columns Also include a secondary view: "Time from 1st to 2nd purchase" distribution: histogram of days-between, bucketed into 0-7 / 8-30 / 31-90 / 91+ days. Annotations: - Add benchmarks: 27% of first-time DTC buyers make a 2nd purchase ever; brands at 45%+ are top-quartile. - Flag any category where M6 repeat rate is below 15% (one-time-purchase pattern). - Flag any cohort where M3 repeat rate dropped >5 percentage points vs. prior cohort (recent acquisition-quality drop). - Highlight categories with M3 repeat rate > 30% (high natural-replenishment products: candidates for subscribe-and-save). Surface the median time from 1st to 2nd purchase per category: this is the right delay for replenishment journeys. Taxonomy notes: - "first_order_created" as an event does not exist. "First order" is computed as the earliest Placed order per customer_id. - "first_purchase_category" is derived from first order's items.product_id to Products.category.
Install Intempt and run it
- 1
Sign up
Create a free Intempt workspace.
- 2
Connect your data
Add the sources this recipe reads from.
- 3
Ask Blu
Type /purchase-retention in Blu once this recipe is live there.
- 4
Review and approve
Blu builds each step and waits for your OK before anything goes live.
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