Time to second purchase
Measure the time from a customer's first order to their second order. Use the median and conversion percentages to guide when to send replenishment reminders.
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
Measure the gap to order two
Coming soonBlu builds a step
About this recipe
When to use it
Send a replenishment nudge too early and it's noise. Too late and they've already bought elsewhere or forgotten. This histograms the real days between first and second order across your base, so the trigger delay is based on actual behavior, not a guess.
What it delivers
Builds a histogram of days elapsed between a customer's first and second order_created events, revealing the real distribution to time replenishment journey triggers against.
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
“Measure the time from a customer's first order to their second order. Use the median and conversion percentages to guide when to send replenishment reminders.”
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.
- 01Measure the gap to order twoCreate an Insights report called "Time from 1st to 2nd Purchase". Series A: Distribution histogram of days between each user's 1st Placed order and 2nd Placed order (only users who have at least 2 orders) Series B: Cumulative percentage: what % of repeat-buyers make their 2nd purchase within X days Buckets for Series A: 0-3 days / 4-7 days / 8-14 days / 15-30 days / 31-60 days / 61-90 days / 91-180 days / 181-365 days / 365+ days Time range: Users whose 1st order was 6+ months ago (so we have a full follow-up window for measuring 2nd-purchase timing) Breakdown: By first-order product category (derived from first Placed order.items to Products.category): top 6 categories Compare: Previous period (cohorts whose 1st order was 12-18 months ago: answers: is repeat-velocity getting faster or slower?) Chart type: Histogram chart with the cumulative % overlay as a secondary line Annotations: - Highlight the median time-to-second-purchase per category: this is the right delay to set on replenishment journey triggers. - Flag any category where median time-to-second-purchase is <14 days (high-velocity replenishment: candidate for subscribe-and-save offering). - Flag any category where median time-to-second-purchase is >180 days (long-cycle category: replenishment journeys should fire at 6-9 months, not 30 days). - Highlight the share of repeat-buyers who returned within 30 days (the "early loyalty" group). This share growing over time = improving retention; shrinking = deteriorating. Use case: distinct from the existing purchase-retention recipe (which is cohort %). This is the histogram that tells you the actual time distribution: critical for journey-trigger timing. Most brands use a generic 30-day or 60-day replenishment delay; this recipe surfaces the right delay per category. Taxonomy notes: - "First order" and "second order" per user are derived from the temporal order of Placed order events for the same customer_id. - Placed order.items contains product_id which resolves to category via the Products record-object.
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 /post-purchase-second-order-velocity 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.
Related recipes
Sid ChaudharyDiscovery call qualification fieldsGenerates an AI summary for Discovery calls from meeting data using the qualification framework in the summary recipe.View recipe Sid ChaudharyPost demo nurtureKeeps a demo warm for 90 days with a playbook, a case study, an ROI calculator and a customer story, and pulls the AE in early if they bite.View recipe Sid ChaudharyReturn rate by categoryShows which product categories get returned most, and which ones have got worse since last quarter.View recipe 