You’ve worked hard to build a subscriber base. But that’s just the start! As the saying goes, “a bird in the hand is worth two in the bush”—there’s a group of people already paying you for your services, so it’s critical to focus on those customer relationships to continue to build your business.
And that’s why it’s so important to measure customer retention. Retention data gives you a window into how well your product is meeting customer needs over all the phases of the user lifecycle. What are your areas of weakness? Where do you have opportunities to improve?
In this guide, we’ll over the basics about what customer retention analysis means for SaaS companies, and how to collect and use customer data to make sure people stick around.
What is customer retention analysis (survival analysis)?
Customer retention analysis, or survival analysis (a bit dramatic, we know), is the process of examining user behavior and interactions over time, so that you can identify patterns that influence why customers churn or remain loyal.
This analysis provides valuable insights into improving customer retention and enhancing long-term engagement by identifying what keeps people coming back.
Let’s say you’re a product manager, and you see that only 40% of new customers are still actively using your product six months after signing up. You’ll want to explore what happened during that critical six-month onboarding period.
Was it a new feature launch? Did customer support interactions play a role? Retention analysis helps you break down product usage data and customer feedback to pinpoint where users drop off and what improvements could improve your customer experience in the future.
Retention analysis answers questions like these:
- Which user segments have the highest retention rates?
- What specific product features or updates contribute to improved retention?
- How do support interactions or response times affect customer loyalty?
Main objectives of customer retention analysis
Ok so what’s it all for? The answer: Customer retention analysis helps you keep your customers around for the long haul. Here are the key objectives:
- Identify key factors affecting customer churn: The goal here is to uncover why customers leave. By analyzing customer behavior and engagement, you can spot the patterns that lead to customer churn and fix them before they become a bigger issue.
- Improve overall customer retention rates: A solid analysis will reveal where your customer retention strategies are working—and where they’re not. This helps you tweak your approach to keep existing customers happy and loyal.
- Optimize product or service offerings: When you understand how customers use your product, you can tailor features or services to boost customer satisfaction and retention, ultimately leading to better long-term engagement.
- Tailor marketing and engagement strategies: Retention analysis helps you target customer segments more effectively. You’ll know which marketing campaigns resonate best with specific cohorts and can adjust your efforts accordingly.
- Understand customer lifetime value (CLV) trends: Tracking the customer retention rate over an extended period of time helps you forecast customer lifetime value (i.e., how much that customer will spend with you over their whole time using your product). This is crucial for predicting revenue and planning future marketing strategies.
- Predict future churn patterns using data analysis: With predictive analytics, you can anticipate when customers are at risk of churning and take proactive steps to retain them.
10 key metrics to know for customer retention analysis
Not all metrics are created equal—for customer retention analysis, these are the key metrics that can give you the insights you need to keep your customers engaged (plus the formulas for how they are calculated):
- Customer Churn Rate
This tells you the percentage of customers that cancel their subscriptions over a specific period of time. Formula:
Churn Rate = (Number of Churned Customers ÷ Total Number of Customers) x 100
A high churn rate could mean it's time to re-evaluate your retention strategies.
- Customer Lifetime Value (CLV)
CLV estimates how much revenue you can expect from a customer throughout their entire relationship with your business. Formula:
CLV = (Average Purchase Value x Purchase Frequency) x Customer Lifespan
Tracking CLV helps you understand the long-term value of your customer base and tailor your marketing efforts accordingly. - Customer Engagement Score
This score measures how actively customers are interacting with your product or service. It often factors in usage frequency, feature engagement, and customer feedback. Formula:
Engagement Score = (Total User Actions ÷ Total Users)
High engagement typically correlates with higher customer retention rates. - Net Promoter Score (NPS)
NPS measures customer loyalty by asking customers how likely they are to recommend your product to others. Formula:
NPS = % of Promoters - % of Detractors
You can measure NPS directly using tools like Sprig’s in-product survey templates. Learn more here.
- Monthly Recurring Revenue (MRR) Churn Rate
This metric shows how much revenue you’ve lost due to customer churn. Formula:
MRR Churn Rate = (Lost MRR ÷ Total MRR at Start of Period) x 100
Keeping a close eye on MRR churn helps you understand how churn impacts your revenue stream.
- First Contact Resolution (FCR) Rate
This measures the percentage of customer inquiries or issues resolved during the first interaction with support. Formula:
FCR Rate = (Number of Resolved Cases on First Contact ÷ Total Number of Cases) x 100
A higher FCR rate is often linked to better customer satisfaction and retention.
- Time to First Value (TTFV)
TTFV tracks the time it takes for a new customer to achieve a meaningful outcome with your product. Formula:
TTFV = (Time of First Meaningful Action - Time of Signup)
The quicker they see value, the more likely they are to stick around. - Product Usage Rate
This metric shows how frequently customers use specific product features. Formula:
Usage Rate = (Number of Feature Interactions ÷ Total Number of Active Users)
By analyzing feature adoption, you can spot usage patterns that impact retention. - Customer Health Score
A composite metric that combines various factors (e.g., engagement, support tickets, NPS) to give an overall health score for each customer. Formula:
Health Score = Weighted Average of Multiple Customer Data Points (e.g., usage, engagement, support activity)
Customers with a low health score are more likely to churn. - Customer Retention Cost (CRC)
This measures how much you spend on customer retention efforts, such as support, loyalty programs, or customer success initiatives. Formula:
CRC = Total Retention Costs ÷ Number of Retained Customers - Retention Rate by Cohort
Using cohort analysis, you can track the retention rate of customer groups based on when they joined, their demographic data, or behavior patterns. Formula:
Cohort Retention Rate = (Number of Retained Customers in Cohort ÷ Total Number of Customers in Cohort) x 100
Tracking these metrics helps you identify where your retention efforts are paying off and where there’s room for improvement. By linking them to customer behavior and using tools like Sprig for real-time usage data and customer feedback (combining both quantitative and qualitative data), you’ll have a clear path to improving retention and driving long-term growth.
Customer retention analysis in 6 steps
Step 1: Identify the key metrics to track for retention analysis
Start by deciding which retention metrics matter most for your business. We just gave you a list of 10 common metrics that businesses track, including the churn rate, customer retention rate, CSAT, and NPS, so depending on your unique situation, you might want to focus on one metric or a collection to give you the level of understanding you’re looking for.
(Also, just so you know—Sprig’s in-product surveys or continuous feedback tools can help you gather direct customer feedback to measure these KPIs.)
The results page of a CSAT survey in Sprig — Sprig AI automatically summarizes responses to give you actionable learnings in real-time.
Step 2: Collect data on customer behavior and product usage
Using analytics tools—like session recordings and replays, or heatmaps—will give you insights into how customers interact with your product.
For example, Sprig’s session recordings can be triggered by specific user actions, making it easy to scale this approach without adding to your workload.
Results page of a session replay study in Sprig — Sprig AI instantly analyzes and groups recordings into themes to uncover patterns in your users' behavior.
Step 3: Segment customers based on their characteristics and interactions
Group your users into customer cohorts based on usage frequency, engagement levels, or demographics. This way, you can establish benchmarks and identify which customer segments are most likely to churn and which are loyal customers.
Step 4: Analyze the factors contributing to customer churn or retention
While manual analysis can be a good approach, you can use AI to scale your analysis of customer feedback and product usage data across personas and other customer cohorts, so that you can get at the overarching trends, as well as identify the specific drivers of churn. It could be anything from confusing onboarding flows, to pricing challenges, to product bugs.
Capture experience data at scale via Replays, Heatmaps, Surveys, and Feedback—Sprig AI instantly analyzes all data to uncover trends in your product experience.
Step 5: Implement strategies to address churn drivers and enhance retention
Once you know what’s causing churn, take action! Maybe you’ve done a funnel analysis, or you're sitting on a heap of historical data—there’s no point in collecting it all unless it directly informs your actions.
Even something like launching an in-app survey to show you’ve heard customers’ concerns and are looking for the best way to address it can boost loyalty and lower the risk of cancellation. Sprig’s continuous feedback tools allow you to address customer pain points in real-time, making it easier to keep active users happy and engaged.
Step 6: Continuously monitor and optimize based on data-driven insights
Retention isn’t a one-and-done effort. Use AI-driven insights and keep monitoring your customer retention metrics over time to refine your strategies and reduce churn.
With Sprig’s AI Recommendations, you’ve got a continuous flow of analysis that helps you make more informed decisions about what to do next, at any scale.
Ask anything about your product—Sprig AI analyzes all of your experience data to uncover product optimizations and generate recommendations.
Case Study: How Sprig’s in-product surveys helped ClassPass improve customer retention
So how does all this work in the real world? We’re glad you asked!
ClassPass used Sprig’s in-product surveys to gather direct feedback from its user base, identifying key gaps in the customer experience. When ClassPass first launched, their search experience wasn’t optimized for the kind of volume they were driving once they became a household name.
Enter Sprig Surveys. By launching an in-app survey targeting the search experience, ClassPass was able to capture insights from their users in the context of their product (at the exact moment they were using the search feature), giving ClassPass precisely the information they needed to identify—and resolve(!)—issues before they became major problems.
Check out the full story here.
Scale your customer retention analysis and make it more actionable with Sprig
Customer retention analysis is crucial for understanding why customers stay or churn, and which areas of your product or service need improvement. By tracking the right metrics—like churn rate, CLV, engagement scores, and NPS—you’ll gain valuable insights into your customer base and can implement targeted retention strategies to boost user retention, or even upsell your customers on new features.
But retention doesn’t stop at analysis. It’s an ongoing process that requires real-time feedback, continuous monitoring, and actionable insights to stay ahead of customer needs.
That’s where Sprig comes in. Sprig’s powerful tools—like in-product surveys, session recordings, and AI-driven analytics—give you the ability to measure customer sentiment, pinpoint friction points, and implement data-driven changes that improve the overall customer experience.
Ready to take your retention efforts to the next level? Book a demo of Sprig today and start building a more loyal customer base.