What is churn analysis?
Churn analysis is the process of identifying which customers are leaving, why they are leaving, and spotting patterns that can help prevent future churn.
In B2B SaaS, you can perform two types of churn analysis:
User churn analysis: This examines individual users who stop using the product. It’s important to note that there can be multiple individual accounts within a single customer account. Even if the company continues as a paying customer, you can have individual users churning due to product complexity, lack of value realization, etc.
Customer churn analysis (aka Logo churn): This tracks customer accounts instead of individual users. In B2B SaaS, each customer account often represents an entire business with multiple users and significant revenue. So, losing a single company can majorly impact your revenue stream and, ultimately, business growth.
How to do churn analysis
Now that you understand what churn is, let's see how you can do churn analysis effectively:
Step 1: Establish a clear definition of churn
Before you start analyzing churn data, it's important to clearly understand what kind of churn you're focusing on. A shared, clear definition of churn ensures that every department is aligned in its efforts, which further prevents any confusion and miscommunication down the road.
Start by gathering key people from teams like finance, customer success, revenue operations, and product and discuss this.
Once that's clear, determine what’s your criteria for marking a customer or user as churned. For example, you might define logo churn as: "A customer who hasn’t renewed their subscription within 30 days after it ended." This could be 60 days or 90 days, depending on your definition of churn.
Step 2: Bring all customer data into one place
Once you've clearly defined churn, the next step is to centralize all your customer data so you can actually analyze it. In most companies, valuable customer information is scattered across different systems—your CRM, billing platform, helpdesk, and product analytics tools.
For larger teams with technical resources, this typically means using a data warehouse and an Extract, Transform, Load (ETL) tool to automate and consolidate data. But if you're an SMB or early-stage company, you can use tools like Google Sheets or Airtable for this.
With your data now unified, analysts can build dashboards or tables that show key customer metrics like churn status, plan type, product usage, and support history.
Also, make this data easily accessible to relevant teams like customer success, product, and revenue operations through business intelligence tools. Doing so makes customer health visible and actionable across all departments so they can spot warning signs and intervene before it's too late.
Step 3: Segment customers into cohorts
Segmenting your customers helps you understand who is churning and how they behave before leaving so you know what patterns to watch out for. You can segment customer data in several ways depending on your business model and data maturity:
Segment by revenue tier: Groups customers based on their size or value to your business, typically into SMBs (small and medium businesses), enterprises and so on.
Segment by contract type: Customers on monthly plans often churn more easily, while annual or multi-year contracts tend to show churn risk closer to renewal time.
Segment by industry or use case: Different verticals may experience churn based on seasonality, compliance cycles, or market trends.
Segment by lifecycle stage: Where a customer is in their journey with you dramatically affects churn behavior. So you can create segments for new customers in the 0–3 month range or new customers. Those in the 3–6 month range are referred to as adopting customers. Customers in the 6+ month range might be described as established customers,
Step 4: Track customer behavior
The next task is to investigate behaviors that tend to occur before churn.
A drop in product usage is often one of the earliest warning signs. This could mean fewer logins, less frequent use of key features, or a decline in administrative activities like configuration changes. Such patterns may suggest that the customer is losing interest or not finding enough value in your product.
Another important area to examine is support interactions. Identify customers who have unresolved support tickets, have made previous complaints, or report declining satisfaction scores. They’re showing signs that they are facing issues with your product.
Similarly, billing and payment behaviors offer critical clues. Repeated failed payments, subscription downgrades, or delays in renewals are some strong indicators of churning.
You can also unlock additional and concrete insights from customer exit feedback surveys. Conduct surveys or interviews with customers who have churned to get direct insight into why they left. This adds valuable context to their pre-churn behavior and helps you to address underlying issues more effectively.
Next, validate which behavior truly predicts churn and which is just noise. Focus on those that are both statistically strong and actionable.
Start with an uplift analysis to measure how much more likely customers are to churn when a certain behavior occurs. For example, customers with 3+ open support tickets might be 3.5x more likely to churn, making it a strong predictor.
Check each predictor's consistency over time by reviewing historical data. Reliable indicators should show a stable pattern, not just a one-time spike caused by events like pricing changes.
Narrow down your list of signals to 2–3 top predictors per customer cohort. For example, SMBs might focus on "2 failed payments in 30 days" while Enterprise might track "3+ unresolved tickets with low satisfaction" or "50% drop in admin login frequency."
Step 6: Develop interventions
Once you've identified the biggest churn indicators, it's time to turn those insights into action. This step is about building structured responses that are triggered when a customer shows early signs of leaving.
Start by creating workflows tied to the churn behavior you validated earlier. For example, if you know that accounts with three or more unresolved support tickets are much more likely to churn, build a system where your Customer Success team gets alerted and follows up directly with the customer.
Or, if failed payments are a strong signal, create an automated process where the billing team sends reminders, followed by a personalized email or phone call if needed.
It's also smart to test before you scale. Start with a small group of customers to see how they respond. Monitor engagement, renewals, and feedback. If something works well, expand it. If it doesn't, tweak or replace it with a different approach.
Step 7: Track results and keep improving
Once your churn response campaigns are live, you need to measure what's working and keep making improvements.
Review how each churn prevention initiative performs. For every campaign - whether it's follow-ups for billing issues or outreach for low engagement - measure key results like renewal rates, customer feedback, and response times. Identify which actions lead to actual retention and which don't move the needle.
Run quarterly reviews to refine strategies. Bring together stakeholders from customer success, product, RevOps, and support every few months to review what's working and what's not. This helps keep your churn prevention strategy fresh and aligned with customer behavior.
3 ways to tell if your churn is systematic, not situational
You've done the analysis, but not all churn is created equal. Here's how to tell if your churn problem runs deeper than isolated incidents.
Churn is consistent across multiple segments
If small, medium, and large companies are all leaving your service at the same rate despite having different needs and engagement models - that's a red flag.
If your churn were purely situational, it would typically spike in one or two segments, perhaps among SMB customers who are feeling the pinch from economic pressures. But when churn is widespread across different customer tiers, geographies, or industries, it signals a broader, more systemic issue, such as poor product-market fit, lack of differentiation, or misaligned pricing.
To tackle this, understand what is your churn rate across various customer segments - by company size, industry, or geographic region and identify common failure points across these segments to figure out the root cause of churn.
You have a high CSAT but still lose logos
Your customer satisfaction (CSAT) and Net Promoter Scores (NPS) are good, but people are still canceling. Why? This could mean regular users like your product, but the decision-makers (like managers or executives) don't see the value. If the people who control the budget don't see the benefit, no amount of satisfaction will save your business from churn.
To address it, hold Executive Business Reviews (EBRs). Use these sessions to highlight ROI, review progress toward goals, and align your product's value with their strategic priorities. Shift from reporting feature usage to showcasing business impact. Help customers see how your product contributes to their KPIs and bottom line.
Frequent customer support tickets and complaints
Your customer support team is handling a high volume of tickets, many of which are about the same issues. This is a strong indicator that there are systemic problems with your product or service that are affecting many customers.
To resolve this, analyze ticket trends. Take a deep dive into your support tickets to identify recurring issues. Are the same bugs being reported by multiple customers? Is there a feature that users consistently misunderstand? If so, you need to work on them proactively and get them fixed.
2 reasons why old retention tactics have become obsolete
If your retention tactics aren't working like they used to, it might be because of some external factors. Following are the major market changes that have reset the rules and made legacy approaches ineffective.
Budget Scrutiny
Economic pressures have led to a shift in decision-making authority, with CFOs and procurement teams now holding the reins for renewals and budgets. In the past, mid-level managers or customer champions could easily approve contract renewals. Today, finance teams demand clear, data-backed justifications for every expense. They expect quantifiable ROI, not just positive user feedback or satisfaction scores.
As a result, legacy retention efforts are failing because they rely primarily on relationships and qualitative feedback rather than measurable business outcomes. Since the person approving the renewal is not a day-to-day user of the product, without hard metrics to demonstrate real business impact, your chances of renewal decrease - regardless of how satisfied end users may be.
SaaS saturation and feature parity
The SaaS market has matured, and virtually every tool in every category now has multiple viable competitors. Feature gaps are smaller than ever, and switching vendors has become easier. Many products now allow simple data exports and quick logins to alternative platforms.
This means old retention tactics like "we have the best features" or "our solution is the most comprehensive" aren't as effective as they used to be. The competitive advantage no longer lies in having unique features but in how a product is adopted, supported, and has evolved over time.
Final thoughts
As you run churn analysis, make sure you connect the data to what's happening in your business. Don't track who's leaving, but figure out why they are leaving. Look for patterns, shifts in behavior, or gaps in the customer experience that might explain the numbers.
Back your findings with clear visuals and supporting reports to bring others on board. Whether you're making the case for product updates, new retention tactics, or a shift in messaging, strong analysis will help you make smarter business decisions and keep more customers around.
So roll up your sleeves, start mapping the signals, and get ready to reduce churn with confidence.
