9 Customer retention metrics & how to track them

Customer retention metrics are a baseline for all PLG companies, helping you understand how much revenue your business holds onto each month.

By building a more detailed picture with data from inside and outside your product, you can also use retention metrics to measure your product’s growth potential, spot areas for revenue generation, and limit churn.

That’s why, in this guide, we look at how to measure customer retention. This includes nine of the most important metrics and an approach for customer retention analysis. We also explore Optimizely Warehouse-Native Analytics, a product analytics platform that allows you to easily understand customer behavior across channels.

Customer retention metrics are insights that show how well you hold onto your customers over time. Unlike engagement metrics which focus on how customers interact with your products, often on a one-off basis, retention metrics specifically measure the loyalty of customer relationships in the long-term.

Customer retention metrics are figures that show your ability to maintain engagement, foster long-term relationships, and reduce churn.

Understanding this data is critical to keeping costs down as you scale. After all, it’s cheaper to retain existing customers than to acquire new ones. This is especially poignant in a PLG environment where the product itself acts as the driver for new leads and sales.

By giving your entire team the insights they need to reduce churn, you directly save money. For example, when your service team gains the actionable insights they need to improve onboarding processes you won’t need to invest in costly customer acquisition and retargeting campaigns.

Research shows that the probability of selling to a new customer is 5-20% while the probability of selling to an existing customer is over 60%. So it makes sense that by identifying areas of stickiness in your product, you can also boost revenue.

With retention metrics from inside and outside the product, marketing teams can promote upsell opportunities to existing customers more effectively.

To understand how this looks in practice: imagine that through a detailed analysis of retention metrics, you discover that a significant portion of your users are highly engaged with a specific feature. Your marketing team can then build a campaign that promotes this to similar audiences, personalizing the upsell offer based on data acquired from sales conversations and customer feedback.

Overall, with a complete hold of retention metrics, you can build a product that promotes itself — the ultimate goal of any PLG company. More often, users will become paid users, customers will become advocates, and advocates will become your most successful sales function.

Take a look at the table below to get a quick understanding of the most important user retention metrics. Alternatively, dive in and explore how to measure customer retention step-by-step in more depth.

The data point most people think of first when asked about customer retention metrics, customer retention rate refers to the percentage of customers who continue to use your product. It can be measured from the point of product creation or over a specific period of time.

When looking at the customer retention rate over a short period, you can evaluate the effectiveness of your retention strategies or identify where product issues have crept in.

Meanwhile, by conducting customer retention analysis over a longer period, you can gain a deeper understanding of overall customer loyalty and identify patterns or seasonal fluctuations in retention rates.

You can also compare your customer retention rate to industry benchmarks to understand how you stack up against your competitors.

Customer retention rate formula: (Users at end of period) - (users onboarded during the time period) / (total users at start of period) * 100 = CRR

On the opposite side of customer retention measurement, churn rate shows how many people leave the product over a given period.

A consistently high churn rate over time obviously indicates issues with your product. But, going deeper than this, analyzing patterns and trends related to specific customer segments, usage patterns, or product features allows you to develop strategies to reduce churn overall.

Optimizely Warehouse-Native Analytics’s customer analytics allow you to track specific journeys to understand drop-off points. You can also access data directly from your warehouse to understand, for example, how customer success and support touchpoints link with churn.

Customer churn rate formula: Number of customers at the start of a period / number of customers lost during a period * 100

You can also calculate churn in terms of revenue rather than customers; this allows you to quantify your losses more accurately rather than getting an inaccurate picture by weighting all users equally. Revenue churn rate takes into account issues like cancellations and plan downgrades as well as general customer churn.

Monitoring your revenue churn rate alongside behavioral analytics allows you to identify trends and patterns in customer behavior that impact revenue retention. For example, if you observe that customers who have low engagement with key features of your product are more likely to churn and result in revenue loss, you can prioritize efforts to improve the usability of those features.

Revenue churn rate formula: {[(MRR at the beginning of the month – MRR at the end of the month) – upsells]/MRR at the beginning of the month} × 100 = Revenue churn rate

Reactivation rate shows you how many of your churned or inactive customers return to your product within a specific period of time. It’s a key retention metric because it directly measures your ability to re-engage customers who have previously stopped using your product.

Tracking reactivation patterns and trends can give you valuable insights into why customers churned or became inactive in the first place. For example, you may find that customers who received personalized outreach were more likely to re-engage. This could then inform the support you provide to existing customers.

Reactivation rate formula: Reactivated customers / total churned customers * 100

Existing customer revenue growth rate looks at the year-on-year increase in your users’ spending. This allows you to understand not only whether you’re retaining users, but whether you’re engaging them over time.

Existing customer revenue growth rate is a good indication of customer loyalty; customers who consistently spend more with your business are more likely to recommend it to others. An improving number suggests that your retention and engagement strategies are effective in fostering long-term relationships with loyal customers.

Existing customer revenue growth rate formula: (Current month MRR - previous month MRR) / previous month MRR * 100 = Existing Customer Revenue Growth Rate

Customer lifetime value, an estimate of how much money the average customer will spend during their time with you, is an obvious but sometimes forgotten user retention metric. The longer your users stay, the more they’re likely to pay, and the higher their overall lifetime value is.

When you understand CLV, you can benchmark this against others in your industry, determine who your most profitable users are, and allocate your resources more effectively.

Customer lifetime value formula: (Average purchase value x average purchase frequency) x average customer lifespan = CLV

Customer satisfaction score gives you an overview of how happy your users are. You’ll gather this data in the form of a survey that focuses on a specific feature, interaction, or the product as a whole. This is usually measured out of five or with binary happy/sad faces.

You can use CSAT to evaluate the effectiveness of recent product updates, create a feedback loop with customer service, and inform marketing priorities.

Customer Satisfaction score formula: (Number of 4 and 5 responses) ÷ (number of responses) × 100 = CSAT

Net Promoter Score measures how likely it is that users will promote your product to other people.

You’ll ask a question similar to “On a scale of 0 to 10, how likely are you to recommend this product to a friend?” Then, people who answer with a 6 or above are known as promoters while those below 6 are known as detractors.

You can use your NPS to highlight issues with a particular aspect of your product or service. For example, if you ask users whether they’d recommend you shortly after onboarding and you get a high number of detractors, this could indicate issues that need to be addressed.

You can also benchmark your net promoter score against others in your industry.

Net Promoter Score formula: % promoters - % detractors = NPS

All product teams will inevitably track Daily Active Users and Monthly Active Users. However, if you’re working with any use case other than low-frequency usage apps, it’s also worth assessing DAU to MAU: the ratio of Daily Active Users to Monthly Active Users.

Also known as the stickiness rate, this metric indicates how strong user engagement is and how much value your users are deriving from your product.

To understand further which users are retained and which are churning, you can combine DAU to MAU with behavioral cohort analysis inside a product analytics platform like Optimizely Warehouse-Native Analytics. This will help you identify trends and patterns that lead to users leaving the app or becoming more engaged.

DAU to MAU rate: Daily Active Users / Monthly Active Users

Oftentimes, measuring customer retention tells you the what and how, but not the why. For a more complete understanding of your customer’s experience and the reasons behind churn or engagement, you need a product and customer analytics platform like Optimizely Warehouse-Native Analytics.

This provides you not only with direct insight into retention but with: