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Cohort Retention Calculator
Common Questions
Frequently Asked Questions
What is cohort retention analysis in ecommerce?
Cohort retention analysis groups customers by the month they first purchased and then tracks what percentage return to buy again in subsequent months. Unlike aggregate retention rates that blend all customers together, cohort analysis isolates each acquisition group so you can see whether your product experience, email flows, and post-purchase journey are genuinely improving over time - or whether growth in new customers is masking declining loyalty.
How do I calculate customer retention rate?
The basic formula is: Retention Rate = (Customers who purchased again in period N) divided by (Customers in the original cohort), expressed as a percentage. For example, if 100 customers bought in January and 22 of them purchased again in February, the P1 retention rate for that cohort is 22%. Tracking this across multiple periods and multiple cohorts gives you the cohort triangle used in this calculator.
What is a good repeat purchase rate for ecommerce?
It depends heavily on product category and repurchase cycle. For consumables (supplements, coffee, skincare), a P1 rate of 30 to 50% is achievable. For mid-cycle products like apparel or homewares, 15 to 25% is healthy. For high-consideration purchases with longer cycles (furniture, electronics), 8 to 15% P1 is reasonable. More important than the absolute number is the decay curve - how quickly retention falls from P1 to P2 to P3. A slow, gradual decay indicates genuine brand loyalty, while a steep drop signals one-time buyers.
How do I read a cohort retention triangle?
A cohort triangle is a grid where rows represent acquisition cohorts (e.g. January buyers, February buyers) and columns represent periods after acquisition (P0 = acquisition month, P1 = first month after, P2 = second month after). The P0 column is always 100% since it counts every customer at the point of acquisition. Reading across a row shows how a single cohort ages and decays. Reading down a column shows whether the same post-purchase period is improving or declining across consecutive cohorts. The heatmap colours help you spot patterns instantly - darker teal means stronger retention.
Why is customer retention more important than acquisition?
Acquiring a new customer typically costs 5 to 7 times more than retaining an existing one. Beyond the cost advantage, retained customers have higher average order values over time, generate word-of-mouth referrals, and have lower support costs because they understand your product. Most importantly, retention compounds: a customer retained at P1 has a significantly higher probability of returning at P2 and P3. A one percentage point improvement in retention can improve LTV by 10 to 25% depending on your baseline and repurchase cycle - an effect no single acquisition campaign can replicate consistently.
How does retention rate affect customer lifetime value?
Retention rate is the primary driver of LTV because it determines how many orders a customer places over their lifetime. LTV is essentially the sum of expected revenue across all future periods, discounted for the probability that the customer churns at each step. When you improve the P1 retention rate, every downstream period also benefits because each returning customer has a fresh opportunity to return again. The projected 12-month LTV in this calculator uses a decay curve based on your P1 rate to show this compounding effect - even modest retention improvements produce meaningful LTV gains when projected across 12 months.