KPI Tree

Stripe Metric

Payments

Repeat Customer Rate = (Customers with 2+ Purchases / Total Unique Customers) × 100

Repeat customer rate is the percentage of customers who make more than one purchase within a defined period. It reflects customer loyalty, product satisfaction, and the effectiveness of retention and re-engagement programmes.

Full guide: definition, formula, and benchmarks
StripePayments

Repeat Customer Rate

Repeat customer rate is the percentage of customers who make more than one purchase within a defined period. It reflects customer loyalty, product satisfaction, and the effectiveness of retention and re-engagement programmes.

How to calculate repeat customer rate

Repeat Customer Rate = (Customers with 2+ Purchases / Total Unique Customers) × 100

Why repeat customer rate matters for Stripe users

Acquiring a new customer costs significantly more than retaining an existing one. A rising repeat rate signals strong product-market fit and compounding revenue potential, while a falling rate suggests satisfaction or engagement problems.

Stripe users can track repeat purchase behaviour across products, price points, and acquisition channels to understand which customer segments are most loyal and which need targeted re-engagement campaigns.

Understand and act on repeat customer rate with KPI Tree

Pull Stripe customer and payment data into your warehouse and compute repeat customer rates in KPI Tree. Place the metric in a retention tree alongside customer lifetime value and subscription churn rate.

Assign customer experience or growth ownership and set alerts when repeat rates decline for key cohorts or product lines.

Get started with your Stripe data

Query using MCP
MCP

Pull metrics from Stripe directly through the Model Context Protocol.

Data Warehouse
SnowflakeBigQueryDatabricksRedshift

Connect your existing warehouse where Stripe data already lands.

Professional Services
FivetranSnowflakedbt

Our professional services team can build you turn-key AI foundations in a matter of weeks. Data warehouse on Snowflake/BigQuery, ELT with Fivetran, all modelled in dbt with a semantic layer.

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