See which customers come back and what they're worth.
Track repeat purchase rate by first-order month, narrow down to profitable segments, and see net LTV after COGS — without building a single chart.
Hover a cell to inspect it. Click a month to highlight its row. (All values are sample data)
Four things cohort analysis shows you.
Segment filters, retention by first product, real LTV and more. Here's what you see once customers are grouped by first-order month.
Find your most profitable customers in a few filters
Layer gender, acquisition channel and purchase-period filters to find the segment worth targeting. Cohort size and M+3 repeat rate update instantly.
See which first products build loyal customers
Compare M+3 repeat rate by first-order product. Products above the average are strong candidates for new-customer promotions.
Real LTV, after COGS
Connect COGS data to see Lifetime revenue and net LTV on the same chart. Cohort rankings can change once costs are applied.
Repeat rate and LTV by any dimension
Switch between store, brand, standard vs. subscription, D2C vs. external, or acquisition channel. The line chart and heatmap table update together.
How to read a cohort table.
Rows, columns, diagonals and gray cells each tell you something different. Five rules are all you need.
Compare retention across any dimension.
Switch the analysis unit — store, brand, standard vs. subscription, D2C vs. external, or acquisition channel — and compare repeat rate and LTV side by side.
Which store's first-time buyers stay longest
Revenue tells one story. Net LTV tells another.
Connect COGS data to see revenue and profit on the same chart. A segment that leads on revenue can fall behind once costs are applied.
Showing revenue only (LTR). Turn on COGS data to see net LTV.
Which first product brings buyers back?
Group customers by their first product to see which items make the best new-customer promotions, and which need a stronger follow-up.
Filter to the segment. See the cohort shift.
Layer gender, acquisition channel and purchase-period filters. Cohort size and the repeat-rate curve update instantly, so you can confirm a segment is worth targeting before you export it.
Pick a cohort. Get the list. Act on it.
Select a cohort row, choose repeat or non-repeat buyers, then export a CSV or add them to an email campaign without leaving the cohort view.
to see its customers
Frequently asked questions, answered up front.
Still have questions? Contact us anytime — we will tailor the guidance to how you plan to adopt Laplace.
Cohort analysis groups customers by a shared starting point, usually their first-purchase month, and tracks what they do afterward. It answers questions like 'What share of January's first-time buyers ordered again three months later?' Use it to improve retention, forecast LTV and measure the long-term impact of a campaign.
Laplace calculates LTV by tracking each cohort's cumulative revenue, grouped by sign-up or first-purchase month. Based on patterns from past cohorts, the AI Agent estimates the expected LTV of new customers. Compare that estimate with customer acquisition cost (CAC) to judge how profitable each channel is.
It depends on your goal. First-purchase month works best for measuring marketing impact and repeat behavior, because it starts the clock when a customer first pays. Sign-up month suits businesses where buying takes time after sign-up, since it shows how well new members convert to first orders. Laplace supports both, so you can compare them.
Start by finding when customers left and who they were. When retention drops, Laplace's AI Agent analyzes when churn happened and which customer groups it was concentrated in, then suggests actions that fit that cohort. Common fixes include a repeat-purchase campaign within 30 days of the first order, targeted discounts for lapsed customers, and better post-purchase content.
Overall repeat rate is one number: the share of all customers who bought again. Cohort repeat rate follows customers who made their first purchase at the same time, so you can separate the effect of a specific campaign, channel or season. Your overall rate can look healthy while one cohort churns fast, so check both.