What is customer attrition?
Customer attrition is when customers stop purchasing or using your products or services over time.
Also known as churn, attrition can occur when:
- B2C customers no longer find value in your services or product
- Your product misaligns with customer expectations
- Your B2B product or service can't scale with your SMB or enterprise customers' business growth and evolving needs
To measure attrition, here's an easy formula you can use:
Total number of lost customers/Total number of customers = Attrition rate
You need to keep a close eye on your attrition rate to spot negative trends and intervene quickly as progressive customer loss can negatively impact your business revenue and profitability.
Also, bear in mind that, depending on your business model, retaining your existing customers may be more cost-effective than acquiring new ones.
Customer churn vs customer attrition
It's easy to confuse customer attrition with customer churn. They're very similar terms and can often be used interchangeably. But they're not the same and here's why.
Customer churn refers to the rate at which customers stop using your product or service. It's a quantitative metric expressed as a number or percentage—the "churn rate." You may often perform customer churn analysis to assess not only customer loyalty and retention, but also how frequently you're losing customers and its impact on your organization's profitability.
Customer attrition measures the qualitative aspects of churn to help you understand why customers are abandoning your product or service.
Therefore, it tells you about the customer experience. This means you can learn about the experiential, behavioral, or motivational causes of churn, such as poor product experiences, usability issues, or customer dissatisfaction.
A product experience platform like Sprig can measure customer attrition. Heatmaps, session replays, surveys, continuous feedback, and AI analysis can help explain churn by identifying friction points, such as:
- Rage clicks, which indicate frustration with unresponsive or confusing elements
- Unexpected drop-offs during checkout often point to issues with the payment process or last-minute hesitation
- Frequent form abandonment can highlight issues with form length, complexity, or privacy concerns
- Repeated navigational backtracking suggests navigation issues or difficulty finding information
While churn indicates a customer-loss problem through hard data, attrition analysis can provide you with the root causes. This means it can help you understand how to improve the customer and product experience.
Key metrics in customer attrition analysis
Here are some of the key metrics you'll use to analyze and address factors leading to customer churn.
- Customer churn rate. Divide the number of customers lost during a certain period by the total number of customers at the start of this period to know your churn rate. This metric helps to have a pulse on customer satisfaction.
- Customer lifetime value (CLV). This is the total value a customer brings to your business over the duration of their lifecycle. CLV can help prioritize your retention efforts and measure the impact of attrition of specific customers on revenue.
- Net promoter score (NPS). A key metric in customer satisfaction, NPS measures how likely your customers are to recommend your product or service to others. While customers with a low NPS could be likely to churn, this metric only provides a one-off glimpse of satisfaction. Read why NPS is a limited metric.
- Retention rate. Subtract your churn rate from 100 to know your retention score. Retention is key in customer attrition analytics because it offers a clear, actionable snapshot of customer loyalty, long-term business health, product success and the effectiveness of your retention efforts.
- Average revenue per user (ARPU). This metric measures the average revenue generated per user or account in a specific period (ie, monthly or yearly). Analyzing trends in ARPU alongside churn rates can reveal correlations. For example, a declining ARPU may indicate that customers are downgrading their plans or reducing their usage, both possible signs of churn.
- Engagement rate. This score shows how healthy your customer interaction is. A low engagement rate can be an early predictor of churn. Understanding engagement levels helps create personalized retention campaigns that focus on improving product adoption and addressing specific friction points.
While these metrics can give you an overview of customer satisfaction to confirm if you have a churn problem, only an in-depth qualitative analysis will bring you the data needed to understand the causes of churn, identify the friction points, and know how to fix them.
7 Key concerns of customer attrition analysis
When conducting a customer attrition analysis, you'll want to explore key areas that can help explain why your customers are leaving. The best way to do this is to ask yourself the following questions.
1. Which customers are leaving your product?
Different customer segments may churn for different reasons, requiring tailored retention strategies.
For example, if you're a B2B operation, an enterprise customer may abandon your product more often than your small-to-medium sized business customers. This could indicate unmet expectations that you'll want to analyze and address.
Segment-based customer attrition analysis can reveal patterns, such as shifts in product usage, demographics, or journey drop-offs.
By understanding these segment-specific trends, you can prioritize developing features that directly address each group’s unique needs. This will help you create a tailored experience for your users.
2. What is the customer lifetime value (CLTV) of churned customers?
Since losing one high-CLV customer can be more costly than losing several low-CLV customers, you may need a retention strategy that can prioritize some users above others.
You'll also need to weigh the cost-effectiveness of retaining existing customers versus the cost of acquiring new ones. By factoring in CLV and customer acquisition cost (CAC), you can achieve a better balance between acquisition and retention throughout your product strategies.
3. Why are they churning?
Possible reasons for churn include:
- Poor user experience
- Unmet customer needs
- Technical issues
- More competitive products or services
Knowing why customers churn can highlight where your product needs improvement. You may need to enhance features, fix bugs, streamline navigation, or improve usability.
Feedback tools like in-product surveys, net promoter score (NPS) surveys, or exit surveys are key to getting to the root causes of attrition.
4. What customer behaviors or actions predict churn?
Identifying actions or inactions that signal churn—like reduced engagement rates, decreased logins, low adoption of new features, delayed payments or downgrades—helps narrow your focus to where customers disengage.
However, gathering this data manually and tackling customer attrition analysis on your own is extremely time-consuming.
But an AI-powered tool addresses this with automation and machine learning to collect and analyze data and predict churn before it occurs.
5. When are customers most likely to churn?
Churn can also occur within specific stages in the customer journey or lifecycle, such as during onboarding or after a certain period.
If you can identify critical points where customer engagement drops, like after a free trial or product update, you're more likely to know when customers will churn and take steps to improve these specific stages.
Analysis of heatmaps and session replays via AI can help you identify friction in these crucial points more quickly, giving you the needed optimization insights to prevent churn.
6. What can you do to reduce churn?
Once you have your findings, you can take the necessary steps to prevent churn. Examples of ways you can optimize the product experience to reduce churn include:
- Enhancing the onboarding process to make it easier and more intuitive to set up accounts and understand how to use your product
- Tailoring features based on customer segments
- Personalizing existing features or offering new ones for high-value customers
- Implementing customer success strategies like training or periodic check-ins
- Creating incentives such as loyalty programs and discounts
Some of the best customer retention tools are powered by AI—it quickly analyzes your data and provides recommendations on how to improve the product or customer experience.
7. How effective are your retention efforts?
Once your churn-reducing initiatives are in place, you need to measure their impact.
You can do this through new product-experience studies, like heatmaps and session replays with in-product surveys, to gauge customer satisfaction with the changes.
This helps you determine which strategies are working and which still need improvement so you can optimize your efforts to retain customers.
Gathering customer attrition analysis data at scale
For a complete picture of what's causing your most valued customers to churn, your customer attrition analysis needs to be comprehensive.
But gathering data at scale requires extensive datasets across many customers or touchpoints. This is essential to getting accurate and reliable insights into attrition patterns.
You can achieve this more easily with AI-powered solutions like Sprig that allow you to generate data and insights from thousands of users in a fraction of the time it would take you to collect and analyze the data manually.
Here are three examples of how to use AI-driven tools for effective customer attrition analysis.
1. Identify behavioral-motivational patterns and possible churn causes with AI-powered heatmaps
Heatmaps with AI-driven analytics help you understand churn by identifying users' behavioral patterns across large-scale data sets, narrowing your focus to key areas.
Since heatmaps show where users spend the most time in your user interface, you can get a wider view of recurring patterns in customer satisfaction, support issues, or product frustrations.
AI can then summarize thousands of data points to highlight significant issues, helping you determine your key areas of focus.
Sprig heatmaps reveal where users click more often within a specific part of your site or app.With Sprig Heatmaps, you can create and tailor specific heatmap studies that trigger when users are in specific stages of the customer journey, from onboarding to adoption of new features.
You can then set a goal for each study, such as discovering friction points, and customize for customer segments.
Based on your settings, Sprig heatmaps can run continuously for ongoing data collection and conduct unlimited captures per user to collect data at scale.
Sprig's AI will then sum up the findings in real-time, providing you with insights after a certain amount of captures.
For instance, continuous heatmap studies across the different stages of your product experience might reveal that your onboarding flows and product usage are healthy. But a recently added product feature sees low user engagement.
Based on your study goal of finding friction points, AI might suggest simplifying the new feature's user flow and content that could complicate navigation.
An example of a Sprig heatmap study results, including an AI summary of the collected dataSource2. Track user engagement to narrow down root causes with session replays
Session recordings and replays let you see exactly how users interact with your product.
Where heatmaps show you the breadth of possible experience issues, session replays provide the depth, allowing you to zoom into the possible causes of customer loss.
However, at scale, this can create hundreds of hours of recordings to watch before you uncover the causes of customer attrition.
This is where AI-driven session captures and replays help you analyze the data and categorize the findings.
An example of Sprig Session Replays, with AI summaries to show where users encounter friction within the product experience For instance, with Sprig Replays, you can trigger recordings on a specific event or URL, like new feature adoption, to track user behavior in real time and collect extensive data across all your users.
Based on your CLTV and APRU, you can even customize recordings to trigger only on your high-value user groups.
Then, Sprig's AI Analysis can analyze and summarize the findings, saving you from examining thousands of replay hours to uncover insights.
An example of Sprig's AI summarized themes on replay findingsFor example, AI Analysis could discover that a new product feature experienced high drop-offs and show that navigational issues and a lack of clear content were likely causes.
Using Sprig's AI Recommendations, you could then generate a course of action to resolve the specific issues, which you could then roll out across other areas of your site or app.
By applying AI insights from one study, you can proactively enhance the user experience and reduce friction in similar areas, creating a more cohesive and intuitive product.
3. Gauge customer engagement and collect qualitative insights with automated in-product surveys
Once you've zoomed into your customer churn friction points, in-product surveys can help uncover the actual causes by delivering feedback directly from customers at scale.
As with heatmaps and session replays, performing survey studies at scale can be just as time and resource intensive. Instead, AI-driven survey tools do the heavy lifting for you, with analysis and summaries of large-scale survey data in real-time.
AI-powered survey tools like Sprig Surveys enable you to efficiently collect and analyze insights from thousands of users in a fraction of the time it would take you manually.
An example of an open-ended survey question with Sprig in-product surveysTrigger Sprig in-product surveys based on specific events or user attributes, allowing for targeted studies at different stages of the customer journey.
With Sprig survey templates, you can set up and run different studies quickly.
For example, you can explore usability issues for new users or gather feedback from long-term customers about feature enhancements.
You can also implement ready-to-use CSAT or NPS surveys or customize your approach based on specific needs.
Sprig automates your survey studies and feedback collection. With AI analysis, summaries, and recommendations, you can transform user feedback into quantifiable, actionable data.
For instance, you can track user engagement patterns alongside survey responses to see behavioral trends leading to customer drop-offs.
An example of a Sprig survey study that explores reasons behind a sign-up flow drop-offAI-driven tools like Sprig automate large-scale feedback collection and simplify the analysis of thousands of data points by grouping findings into themes and providing recommendations for product improvement.
See how ClassPass used Sprig surveys to improve a new filtering feature and boost customer satisfaction and retention.
How to use analysis attrition analysis to enhance product and customer experience
Now that you know how to analyze customer attrition at scale, here’s how to use customer attrition analysis to improve the customer and product experience and prevent churn.
1. Improve onboarding completion rates to retain customers
Attrition analysis might reveal high drop-off rates during the onboarding process, suggesting it's too complex for users.
By using customized heatmaps and session replays combined with event-triggered surveys, you can:
- Quantify the extent of an onboarding issue across your user base
- Zoom in on specific friction areas per customer segment.
Feedback from surveys can help identify and prioritize improvements by highlighting the most common or urgent issues among users. But without machine learning, collecting and studying the data can take weeks, leading to imprecise hypotheses.
Sprig's AI tools, Recommendations and Explorer, speed up this process.
For example, AI Explorer analyzes extensive datasets from heatmaps, replays, and survey feedback, while AI Recommendations suggests targeted solutions.
Sprig can analyze data at scale, group findings and spot trends to find the underlying cause of customer attritionImagine your company offers a wellbeing and mindfulness app with multiple subscription plans. You implement changes to the onboarding process as you scale your customer base and set up filters so customers can better navigate the increasing offerings, from sleep meditations to focus music.
However, you notice a 35% churn among new customers just two months into their plan, often before completing the onboarding process.
After running heatmaps and replays with integrated surveys during the onboarding steps, Sprig's AI Explorer analyzes the data in real-time, revealing a lengthy and complex account setup.
But what should you change?
With Sprig's AI Recommendations, you receive data-backed, actionable suggestions within seconds to improve the onboarding flow, such as:
- Reducing the onboarding steps from 10 to 3
- Adding short animated tutorials instead of lengthy instructions
- Including a help button for answers to common questions
Sprig's AI and machine learning empowers your decision-making with precision suggestions based on data, leading to improved onboarding flows and engaged customers.
Read how Invoice2Go boosted onboarding completion by 25% with Sprig.
2. Prioritize improvements to increase feature adoption based on customer needs
Attrition analysis also helps with customer needs analysis to prioritize product improvements and increase feature adoption.
You can make attrition analysis a part of your product analysis framework by collecting feedback from churned or at-risk customers to reveal patterns, like common frustrations or unmet expectations with specific features.
Let's say you run a survey within your mindfulness app questioning key aspects of the new filtering system.
You can set this up quickly with Sprig's Gauge Feature Satisfaction template. Sprig's AI Explorer can then round up the feedback from this study into themes within seconds of competing data collection.
Some common themes around low feature adoption could be:
- Navigational issues affecting predominantly new users, like "difficulty in finding a recently introduced guided meditation"
- Unintuitive design, preventing users from using the filter
Ask Sprig AI for product insights to increase feature adoption and reduce churnSprig's AI Recommendations can then suggest how to prioritize and improve this aspect. Based on the survey data, suggestions could include a new filtering flow:
- Redesigning the filter by placing it on the main menu with brighter colors
- Categorizing meditation exercises into macro areas
- Offering a search bar in each category page
- Suggesting matching meditations after users begin typing into the space bar
Using AI-powered tools helps you categorize the data, prioritize changes based on feedback, and find solutions at speed.
With machine learning, you can make the right decisions to refine features, improve usability, and increase feature adoption.
This customer-centered approach ensures you focus on making significant changes that can directly impact retention, making for more long-term user engagement.
See how Noon increased feature adoption by 46% with Sprig.
3. Optimize your customer support with rich AI-generated insights
Attrition analysis can also show a pattern of churn following customer support interactions, highlighting problems with the speed or quality of query responses.
You can quicken the discovery of more precise and actionable insights from heatmaps, replays, and surveys with AI-driven tools, like Sprig, than you could by analyzing the data on your own.
Leverage Sprig's machine learning to analyze large-scale data and get instant product improvement suggestionsYou can trigger surveys every time a user completes a customer-service interaction and exits the flow to avoid disturbing users in the moment. You could use an NPS survey.
But, to be more effective, you can pair an NPS survey with an open-ended follow-up survey to get more in-depth feedback and actionable insights.
With this, you can get real-time analysis and suggestions with segmentation analysis through AI Explorer and AI Recommendations. Based on this, you might implement solutions like:
- Investing in chatbot technology to provide quicker answers to common questions for all users
- Expanding your support team to better serve business customers, ensuring their employees don’t abandon a valuable benefit due to inadequate support
- Improving search filters to make it easier for users to find what they want, reducing customer support queries
With automated analysis, you can action customer insights faster and align your product improvements with specific customer needs more effectively, reducing the likelihood of churn.
Learn how Plann revamped their customer support thanks to Sprig survey insights.
Accelerate actionable customer attrition insights with Sprig
Now you know how to conduct customer attrition analysis at scale to prevent customer churn:
- Understand the key metrics and concerns to start your customer attrition analysis on the right foot and know the questions to ask
- Combine heatmaps, session recordings, and in-product surveys to automate data collection, saving you time from conducting in-person interviews so you can gather data at scale
- Leverage AI to analyze extensive data points and group findings into themes in seconds so you can make confident product improvement decisions.
The most important aspect is to use AI to avoid getting bogged down in qualitative data analysis. Instead, you can focus on implementing changes that directly impact customer satisfaction and reduce attrition asap.
Only Sprig combines customer attrition analysis automation with AI-powered analysis and recommendations. With Sprig, you can understand data at scale and transform customer feedback and insights into actionable, targeted improvements with speed.
Cut your customer attrition analysis time to days instead of weeks with Sprig to reduce churn, drive user retention, and boost customer engagement.