Customer churn isn’t just about losing customers; it’s about losing revenue, time, and trust. What if you could predict which customers are about to leave, and stop them before they do?

Churn doesn’t happen overnight, it’s a gradual process with clear signals. Yet most businesses struggle to detect the warning signs until it’s too late.

A churn prediction model reveals these signs early, helping you identify at-risk customers, understand the factors driving them away, and take strategic action to retain them.

In this article, we will guide you through the process of building a churn prediction model that can save a significant portion of your revenue.

What Is Customer Churn Prediction? 

Customer churn prediction is the process of identifying customers who are likely to stop using your product or service in the near future. 

This involves using data analytics, statistical modeling, and machine learning methods for taking timely and proactive steps such as offering incentives or personalized support, to retain customers who are at risk of churning. 

Why Is Churn Prediction Important? 

Though the primary purpose of churn prediction is customer retention, it can also help you: 

1. Minimize churn triggers for new customers 

When you have detected a potential churn in your customer base, you can assess which factors led to dissatisfaction and make amends for current as well as future customers.

For instance, if a recurring issue is poor support during the first few weeks, companies can deploy improvements such as email check-ins, feature introduction email series, onboarding checklists, or interactive dashboards. 

By addressing the churn triggers early, you not only prevent present churn but also proactively improve the experience for potential customers, reducing the chances of future churn.

2. Build a customer-centric product strategy

Churn prediction models generate real-time insights like customer sentiment, churn drivers, or engagement patterns, all of which can be used to inform your product strategy. 

By understanding why some customers leave and why others stay, you can closely align your product features and user experience with customer expectations. This can even help narrow down your ideal customer profile (ICP) as some churn may stem from product-market fit issues.

Once you identify gaps in your current product strategy, you can add missing features, build most-requested integrations, and make changes to user interface, translating into a more responsive product. 

3. Strategic resource allocation 

When you know which customers are at risk of slipping away, you can plan targeted interventions well in advance so that they have the greatest impact.

Timely and precise execution ensures that the relevant teams, marketing resources, and adequate support are deployed where they’re needed most.

For example, if a customer gives a low NPS score due to the unavailability of your support team during UK hours, the next step should be to reach out with an apology and offer a solution, such as providing 24/7 support or assigning a dedicated support rep for urgent issues.

4. Forecast business sustainability 

Predicting churn helps you evaluate the overall health of your business and understand whether your current efforts and investments like a new feature update are moving you in the right direction. 

With this predictive approach, you can make accurate financial forecasts, set realistic growth targets, adjust business strategies, and create contingency plans. By understanding how customers perceive and interact with your products or services, you can build a sustainable path forward.

5. Reduce customer acquisition costs 

Churn prediction is a cost-saving approach that tackles one of the most expensive aspects of growing businesses, customer acquisition. 

Acquiring a new customer can cost you more than retaining an existing one. In fact, the current cost of earning a new customer in the SaaS industry stands at a whopping $702. 

By maintaining and nurturing your current customer base, you can reduce these high acquisition expenses. The resources saved can then be reinvested in improving customer experiences, running niche marketing campaigns, or innovating the product. 

Plus, retained customers often become your cheerleaders, potentially bringing in new customers through referrals.

3 Most Common Reasons for Customer Churn

Below are some common causes of churn to help you take proactive measures rather than reactive ones. 

1. Changes in customer circumstance

One of the reasons for churn is a shift in the customer’s personal or business circumstances. This can include a change in budget, evolving business needs, or even internal restructuring. 

For instance, a new marketing head may decide to switch to a different email marketing software based on their previous experience. Alternatively, a company might reduce its budget for certain services due to a change in its priorities. 

To navigate this, train your reps to maintain regular check-ins with customers to understand their evolving needs and wherever possible, adapt your offerings to better meet those needs. 

2. The state of the competition

A competitive market can often feel like a battlefield. 

If your competitors offer better features, budget-friendly pricing, or 24/7 customer support, customers may be lured away.

Regularly evaluating what competitors are offering and continuously improving your value proposition, such as introducing relevant features, can help retain customers.

3. Changes to product features/performance

Any significant changes to your product, such as moving a key feature to a higher-tier plan, frequent bugs and glitches, or excess downtime, can lead to churn. 

Customers may find that the product no longer meets their needs or is less effective at solving their problems. 

Continuous improvements, along with regular updates, training, and support for any product changes, can help mitigate this churn risk.

Basics of Churn Prediction Models 

To build a churn prediction model, it’s important to first understand what these models are, how they work, and their benefits. 

What is a churn prediction model?

A churn prediction model is a combination of data and algorithms that help you forecast which customers are most likely to end their relationship with your company.

These models use historical and behavioral data, such as purchase frequency, usage patterns, and customer interactions, to create a predictive framework that helps you identify at-risk customers.

Beyond simple yes/no predictions, these models provide granular insights into why customers might leave, when they're most likely to do so, and what specific interventions could be most effective in preventing their departure.

For instance, the model might identify that customers who reduce their feature usage by 40% in any given month are 3x more likely to churn within 60 days.

Benefits of an effective churn prediction model

Here’s how a churn prediction model can be useful:

• Proactive retention: A churn prediction model offers you the opportunity to take a proactive approach to customer retention.
  By monitoring customers who are at high risk of leaving, you can intervene with targeted strategies to address pain points that could be deal-breakers.
  Companies that offer proactive customer support see a 15-20% hike in retention. 

• Cost savings: Acquiring new customers is  5 to 25 times more. expensive than retaining existing ones. By predicting and addressing issues of existing customers, you can retain them and reduce the investment in attracting new customers.

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Plus, loyal customers can help attract new business through word-of-mouth, further lowering your acquisition costs.

• Revenue growth: Predicting and reducing churn rates means preserving recurring revenue streams and increasing customer lifetime value (LTV). Your long-term customers are also more likely to bring in new customers through referrals.
  On top of that, the insights gained from the churn prediction model can also inform product and service improvements, increasing customer stickiness in the long run. 

How to Build a Churn Prediction Model?

Now that we’ve set the stage, let’s jump into the most interesting part, building your churn prediction model. We will walk you through the five key steps of creating an effective model.  

Step 1: Use data points for predicting customer churn

The first step to building a churn prediction model is collecting relevant customer data points. The more data points you include, the better. For a reliable prediction model, you should track at least 6 months of historical data across key categories, with a minimum sample size of 1,000 customers. 

These help identify patterns and trends of when and why customers may churn, providing you the foundation to predict future customer behavior.

Here’s what you must track:

• Behavioral data: Track user activity like login frequency, transaction history, and product usage. 

• Transactional data: Review purchase behavior and order frequency to understand customer loyalty.

• Demographics: Assess customer tenure, industry, region, and company size (if B2B). 

• Support interactions: Track customer support interactions like frequency of tickets, types of issues raised, and resolution times. 

• Customer feedback: Collect and track direct customer feedback through NPS, CSAT, and in-app surveys to better understand customer sentiment.

Prioritize tracking behavioral data, transactional data, and support interactions, as they directly reveal customer engagement and satisfaction. Then, focus on collecting customer feedback and demographic information to further refine your churn predictions.

If you’re using Clearlyrated, you get an easy-to-use dashboard to receive NPS scores, compare with the industry, dig into individual responses, reviews, and testimonials, and identify areas that are impacting satisfaction. 

Step 2: Analyze trends to identify reasons behind customer churn

Once you’ve collected the data, the next step is to analyze trends and patterns to understand the root causes of why customers are leaving or staying. This will allow you to predict future churn. 

Segment the customer data based on: 

• Past churn reasons: Identify patterns in why customers have already churned. Do customers churn because of product dissatisfaction, poor customer support, pricing issues, or a lack of features?

• Product usage: Monitor how frequently customers use your product. A drop in usage, such as reduced logins, fewer transactions, a decline in purchasing frequency, or a downgrade signals that customers may not be deriving enough value from your product.

• Demographics: Customer or company (if B2B) demographics also influence churn rates. Longer-tenured customers might be less likely to churn unless there’s a major issue. Similarly, when the company size is small, there might be some churn due to budget constraints, limited resources, or changes in business priorities.

• Support interactions and CS metrics: A high volume of support requests may signal frustration. The NPS score is also a great indicator of how customers perceive your product or service. Customers who score you low on NPS surveys (e.g., 0-6) may be at risk of churning. 

Step 3: Create your predictive model

When you've analyzed your data, the next step is to automate this analysis. You’ll need to process the existing data, set up conditions, and develop a predictive model. You can do this either manually or through machine learning algorithms that automatically detect churn patterns.

• Manual analysis: If you are a small business or don't have advanced tools, start by creating pivot tables in Excel or Google Sheets. Clean and organize your data, making sure to convert any non-numeric data into a format that can be used for analysis. Now, set up conditions like scores or rank to flag "at-risk" customers. Since this is a manual approach cross-validate for reliability.

• Machine learning models/Third-party software: You can also create your own model using machine learning language like Python, or use pre-built models like ChatGPT. If you don’t have the expertise to do this, you can simply use tools like Churnly or Gainsight. Continuously refine and retrain your model as new data becomes available.

If you're using Clearlyrated’s CX management platform, you can set up detractor alerts for customers who score you below 6 on NPS surveys so that the relevant team members are immediately notified to take corrective actions.

Step 4: Identify customers with high churn risk 

Once your model is in place, it’s time to apply it to identify the customers who are at high risk of churning. This will allow you to take immediate action. 

Use your predictive model to rank/score customers based on their churn probability. The higher the score, the higher the risk. For customers at churn risk, also identify the reasons behind their potential departures for course correction. 

Then, categorize these at-risk customers into three groups –high-risk, moderate-risk, and low-risk customers. This allows you to prioritize your retention efforts.

Aim to intervene within 1-2 weeks of identifying high-risk customers to prevent churn. Moderate-risk customers should be targeted within 2-4 weeks, but ensure constant engagement. Low-risk customers can be monitored, with interventions within 4-6 weeks or when there’s a notable change in their behavior. 

Step 5: Implement retention strategies to prevent churn

Even the best churn prediction model won’t save customers if you don’t take action. Once you’ve identified customers at risk, the final step is to deploy relevant retention strategies.

If a customer gives you a low NPS score (6 or below), reach out within 24-48 hours to address their concerns. Personalize the outreach, apologize for the poor experience, and offer solutions to resolve their issues. Follow up after 1-2 weeks to ensure satisfaction and track if their NPS score improves. Add the customer to an ongoing engagement plan to rebuild trust.

Similarly, if a customer has shown low product usage or stopped logging in, send targeted emails offering product tutorials, onboarding assistance, or check-ins from customer success managers to rekindle their interest.

Retention initiatives should be personalized. Keep in mind different customer segments to plan your strategies so that customers receive the right intervention based on their unique behaviors and needs.

How can Clearlyrated Help with Churn Prediction and Prevention? 

Clearlyrated is a CX management platform that gives a deep understanding of your customers. It enables your team to make proactive, measurable improvements for churn-risk customers while also identifying customers for upselling and referrals. 

With its intuitive interface, Clearlyrated makes predicting churn through direct customer feedback simple and efficient.

Its key features include: 

1. Surveys: Clearlyrated offers easy-to-setup, customizable, and industry-specific NPS surveys that accurately capture client sentiment and service quality. From creating surveys to organizing contact lists and pre-survey communication, the tool has everything you need to collect actionable customer feedback.

2. Workflow automation: Set up workflows to receive real-time alerts for potential issues and detractors. Automatically close the loop when the team has addressed the customer concerns. 

3. Feedback evaluation: Easily track your company’s NPS score and compare it with industry benchmarks. Analyze individual responses, reviews, and testimonials to identify key factors impacting customer satisfaction and adjust strategies accordingly. 

4. Real-time issue tracking: Manage, track, and address customer feedback in real-time from a single dashboard. Quickly respond to pressing client feedback and ensure timely follow-ups.

5. Detractor alerts: Get instant notifications for negative feedback, so you can act quickly. Clearlyrated’s automated alerts keep the right team members in the loop while providing best practices and guidance to help you turn detractors into loyal customers.

Stay One Step Ahead With a Churn Prediction Model

Building a churn prediction model is no longer an option, but a necessity for businesses wanting to grow sustainably.

No need to worry if you don’t have advanced prediction tools, a Google/Excel sheet is a great starting point. 

Direct feedback reveals exactly what your customers are dissatisfied with, making it a key component in predicting churn. Use survey tools like Clearlyrated, which make the collection, tracking, and redressal of customer feedback easy and efficient.

Take the first step towards preventing churn and book a demo with Clearlyrated.