Customer Retention Rate (CRR)¶

Definition¶

Customer Retention Rate (CRR) measures the percentage of customers a company retains over a given period. It reflects the ability to keep customers engaged, satisfied, and loyal to the brand, minimizing churn.

Description¶

Customer Retention Rate is the flip side of churn—and one of the most telling indicators of product-market fit, satisfaction, and long-term growth potential. It reflects the percentage of customers who stick around over a given period.

The relevance and interpretation of this metric shift depending on the model or product:

In SaaS, it’s often used to track logo retention, key to maintaining ARR.
In eCommerce or subscription models, it highlights repeat buyer behavior and helps validate loyalty initiatives.
In community-led or PLG businesses, it surfaces engagement stickiness and long-term product value.

A high CRR signals strong satisfaction, onboarding success, and product relevance, while a decline may point to activation friction, price sensitivity, or support breakdowns. Segmenting CRR by persona, plan, or acquisition source helps uncover retention levers and where your drop-offs need attention.

Customer Retention Rate informs:

Strategic decisions, like investing in loyalty programs or prioritizing onboarding redesigns
Tactical actions, such as launching targeted re-engagement campaigns
Operational improvements, including renewal workflows, customer education, or value messaging
Cross-functional alignment, by syncing product, CS, and growth on one of the most fundamental health metrics in the business

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Onboarding Quality and First 30-Day Experience: Poor onboarding leads to early churn. Great onboarding builds retention momentum.
Recurring Value and Feature Use: Customers who consistently use high-value features retain longer. Dormant users fade quickly.
Support Experience and Friction Reduction: Fast, helpful support when things go wrong can save a churn-risk customer. Frustrating support does the opposite.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If retention is sliding, identify your “retention cohort” and analyze what behaviors predict long-term use.
Add lifecycle emails at Days 7, 14, and 30, showing next steps or unlocked value for active users.
Run a test adding auto-generated weekly summaries or performance dashboards, reinforcing ongoing benefit.
Refine your onboarding checklist to get users to core value faster (TTFV < 15 minutes).
Partner with CS to prioritize intervention for accounts with 7+ days of inactivity during the first 30 days.

Required Datapoints to calculate the metric
- Number of Customers at the Start of the Period (S): The active customer count at the beginning of the defined period.
- Number of Customers at the End of the Period (E): The active customer count at the end of the period.
- Number of New Customers Acquired During the Period (N): The number of new customers added within the defined timeframe.
Example to show how the metric is derived

A subscription service calculates CRR for Q2:
- Customers at the Start of Q2 (S): 1,000
- Customers at the End of Q2 (E): 1,200
- New Customers Acquired During Q2 (N): 300
- CRR = ((1,200 − 300) / 1,000) × 100 = 90%

Formula¶

Formula

\[ \mathrm{Customer\ Retention\ Rate} = \left( \frac{E - N}{S} \right) \times 100 \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

Customers

cube(`Customers`, {
  sql: `SELECT * FROM customers`,

  measures: {
    startCount: {
      sql: `start_count`,
      type: `sum`,
      title: `Number of Customers at the Start of the Period`,
      description: `The active customer count at the beginning of the defined period.`
    },
    endCount: {
      sql: `end_count`,
      type: `sum`,
      title: `Number of Customers at the End of the Period`,
      description: `The active customer count at the end of the period.`
    },
    newCustomers: {
      sql: `new_customers`,
      type: `sum`,
      title: `Number of New Customers Acquired During the Period`,
      description: `The number of new customers added within the defined timeframe.`
    },
    retentionRate: {
      sql: `100 * (end_count - new_customers) / NULLIF(start_count, 0)`,
      type: `number`,
      title: `Customer Retention Rate`,
      description: `Measures the percentage of customers a company retains over a given period.`
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: `string`,
      primaryKey: true,
      title: `Customer ID`,
      description: `Unique identifier for each customer.`
    },
    createdAt: {
      sql: `created_at`,
      type: `time`,
      title: `Created At`,
      description: `The time when the customer record was created.`
    }
  }
})

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Onboarding Quality and First 30-Day Experience: Poor onboarding experiences can lead to early customer churn, negatively impacting the Customer Retention Rate.
- Recurring Value and Feature Use: Lack of engagement with high-value features results in dormant users who are more likely to churn, reducing the Customer Retention Rate.
- Support Experience and Friction Reduction: Frustrating support experiences increase the likelihood of customer churn, negatively affecting the Customer Retention Rate.
- Product Complexity: Complex products that are difficult to use can frustrate customers, leading to higher churn rates and a lower Customer Retention Rate.
- Price Increases: Unexpected or frequent price increases can lead to customer dissatisfaction and increased churn, negatively impacting the Customer Retention Rate.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Onboarding Quality and First 30-Day Experience: Great onboarding experiences build retention momentum, positively influencing the Customer Retention Rate.
- Recurring Value and Feature Use: Consistent use of high-value features keeps customers engaged and loyal, positively impacting the Customer Retention Rate.
- Support Experience and Friction Reduction: Fast and helpful support can save customers at risk of churning, positively affecting the Customer Retention Rate.
- Loyalty Programs: Effective loyalty programs incentivize repeat business and enhance customer retention, positively influencing the Customer Retention Rate.
- Customer Feedback and Improvement: Actively seeking and implementing customer feedback can improve satisfaction and retention, positively impacting the Customer Retention Rate.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Customer Success
Executive
Product Management (PM)
Activities

Common initiatives or actions associated with this KPI:

Product Adoption and Use
Retention Strategies
Churn Prevention Tactics

Funnel Stage & Type¶

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Retention
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Customer Engagement Score: A higher Customer Engagement Score indicates that users are actively interacting with the product, which is a strong early signal that customers are finding value and are more likely to be retained, thus positively influencing Customer Retention Rate.
- Activation Rate: A high Activation Rate shows that new users are experiencing the product's core value quickly, which typically leads to better retention over time as users are more likely to stay engaged and satisfied.
- Net Promoter Score: Net Promoter Score reflects customer loyalty and likelihood to recommend; increases in NPS often precede improvements in retention, as promoters are less likely to churn.
- Customer Health Score: A strong Customer Health Score aggregates signals from engagement, satisfaction, and support interactions, providing an early warning for potential churn and a predictive view on future retention.
- Stickiness Ratio: A high Stickiness Ratio (DAU/MAU) means users frequently return to the product, signaling habit formation and higher chances of long-term retention.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Customer Churn Rate: Customer Churn Rate is the direct inverse of Customer Retention Rate, confirming and quantifying lost customers and providing clear context for changes in retention performance.
- Contract Renewal Rate: Measures the percentage of expiring contracts that are renewed, serving as a direct confirmation of retained customers and explaining long-term retention trends.
- Net Revenue Retention: Net Revenue Retention quantifies the overall revenue impact of retained, expanded, and churned customers, offering a broader business perspective on changes in retention.
- Average Customer Lifespan: Indicates how long an average customer stays with the company, providing a quantitative measure that amplifies the impact of retention trends over time.
- Customer Downgrade Rate: Tracks customers who reduce their subscription or usage, which often precedes churn; a rising downgrade rate can explain upcoming decreases in retention and help quantify retention risk.