Are you often caught off guard when your customers leave you? Have you ever wondered why they prefer competitors to you? Struggling to get a fresh perspective? If you can relate to all these problems around customer retention, this blog is for you.

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While acquisition strategies are essential at the start of the business, their sustenance depends on retention strategies. It can cost up to five times as much to acquire a new customer as to keep an old one. While you can implement several customer retention strategies to improve Churn, sometimes, it is necessary to run targeted campaigns for at-risk clients. An effective strategy is to develop a churn prediction model that helps you recognize high-risk clients and take preventative action to maintain their satisfaction and engagement. But before we get to it, let’s brush up on the basics.
What is Churn?
Churn refers to the loss of customers or subscribers over a specific period. In simple terms, it measures how many customers stop doing business with you.

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- In retail, churn happens when customers stop purchasing from or interacting with your brand.
- In SaaS businesses, churn happens when customers stop renewing their subscriptions.
Identifying churn beforehand allows companies to pinpoint customers who are at risk and formulate strategies aimed at retaining them, thereby minimizing revenue loss.
Different types of Churn
Before jumping into the churn prediction model in the next section, let’s take a moment to understand different types of churn and what they mean.
Voluntary Churn:
This happens when consumers consciously choose to discontinue your product or service. They either cancel a subscription or switch to a rival product.
Involuntary Churn:
Unlike voluntary churn, this is unintentional client loss caused by outside circumstances, such as failed payments or inactive accounts.
Revenue churn:
Revenue churn occurs when your revenue takes a hit, even if your customer count is steady. It majorly occurs due to downgrades, cancellations, or lost clients.
Customer churn:
This refers to the total number of customers leaving your business during a specific period. It directly impacts your overall customer base and growth potential.
However, understanding the type of churn through mere observation is not useful for creating effective mitigation strategies. That’s where you need a churn prediction model.
What is a Churn Prediction model?
A churn prediction model is a powerful, data-driven tool that helps businesses identify which customers are likely to stop using their products or services. The model examines historical data such as customer behavior, purchase history, engagement levels, etc., and uncovers signals when someone might be at risk of leaving.
Why Churn Prediction Is Important for Businesses
How you handle your customer churn can define your success. Besides, it also impacts other aspects of your business. Let’s explore why churn prediction is so important for your business.

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- Minimizes revenue loss: By focusing on retention, you stabilize your revenue and avoid the high costs of constantly replacing lost customers.
- Enables targeted retention campaigns: Churn prediction helps you spot at-risk customers and create tailored retention strategies before it gets too late.
- Creates happy customers: Once you understand why customers leave your business, you can address and fix the root cause.
- Supports smarter marketing decisions: By identifying high-value and at-risk customers, you can allocate your marketing resources to improve your ROI and sustain your business growth.
How to Build a Churn Prediction Model
Building a churn prediction model involves a lot of steps, such as data collection, data processing, feature engineering, model selection, model training, etc. Let’s understand each step in detail.
Step 1: Data Collection
Like any data model, the accuracy of the churn prediction model depends entirely on the quality and quantity of data collected. This data can come from various sources where customer interactions are monitored, such as:
CRM Systems
Get a complete record of customer data, interactions, and behaviors, such as their basic details, demographics, company information, social media sentiments, survey responses, etc. For instance, a CRM record might show your customer’s dissatisfaction with their recent survey feedback.
Transaction Records
Gather details of purchases and interactions that can reveal trends and changes in customer behavior and spending patterns, such as billing frequency, payment frequency, downgrades, upgrades, and cancellations. It can help you find the at-risk customer. For example, a customer’s downgrade to a basic plan could signal reduced engagement.
Customer Feedback
Gauge customer satisfaction levels through direct customer feedback, such as surveys or reviews. Suppose you receive a low Net Promoter Score (NPS). It clearly indicates a risk of churn.
Engagement Metrics
You can also collect data about at-risk customers from metrics such as website visits, webinar engagement, app usage, customer service interactions, social media interactions, downloads, etc. For instance, a user who hasn’t logged into the app for weeks might be losing interest.
Step 2: Data Preprocessing
The quality of your predictions depends on how clean and organized your data is. Therefore, it’s essential to clean the data to ensure quality and consistency. Here is the systematic way to do it.
Fill in Missing Information
Just like completing a puzzle with missing pieces, if some data is missing (like a customer’s age or purchase history), fill it in using averages or similar customer data.
Remove Duplicates
Duplicates can confuse your prediction model. Ensure your data has no repeated entries, such as the same customer appearing twice.
Handle Outliers
Any unusual data can skew your results and need to be removed or adjusted. For example, it can look like someone made 1,000 daily purchases.
Assign Numbers to Categories
If your data has labels like “Male/Female” or “Basic/Premium/Pro,” turn these into numbers so the model can understand them. You can use encoding methods like one-hot encoding or label encoding.
Once your data is neat and consistent, you’ll get clearer, more actionable insights to drive your marketing decisions!
Step 3: Feature Engineering
Feature engineering is selecting, modifying, or creating new variables (features) from the raw data gathered in the previous steps.
Key features to focus on:
- Customer Demographics:
Basic details, such as age, gender, location, and income.
Example: A younger demographic may show different churn trends compared to older customers.
- Behavioral Factors:
Data about customer behaviors, such as purchase frequency, interactions with customer support, and product usage patterns.
Example: A customer frequently contacting support for the same issue may indicate dissatisfaction.
- Engagement:
Data about how often and intensively customers interact with the business (e.g., website visits, time spent on the platform, or app logins).
Example: A drop in app usage may signal a reduced likelihood of renewal.
Now, the next step is to select an appropriate model for accurate prediction.
Step 4: Model Selection
The choice of a machine learning model is crucial to predicting churn effectively. Some of the most commonly used algorithms include:
1. Logistic Regression
The logistic regression model helps you analyze the probability that a customer will stay or leave. It uses the logistic function, which ensures the answer is always a number between 0 and 1, like a percentage.
To run the logistic regression model, you need data about product usage, customer spending, and customer support interactions. Logistic regression assigns a weight to each piece of data depending on how much it influences the churn probability. Once that’s done, it calculates the likelihood of customer churn.
For instance, a customer who reduced spending and raised multiple support tickets gets flagged as 80% likely to churn.
2. Decision Trees
A decision tree helps you answer, “Will this customer leave or stay?” It breaks the customer data into small steps in the form of “if-then” questions until it reaches a final answer.
For example, it starts with the critical question, “Does the customer use the product at least once a week?” The Yes/No answer determines which branch to follow.
3. Random Forests

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A Random Forest is a team of decision trees working together to make a more accurate prediction. Just like a regular forest, since it comprises multiple decision trees, it is called a forest. Each tree uses slightly different data samples and other questions (features). Each tree predicts whether a customer will stay or not. Then, they combine and take a vote across all trees; the majority vote becomes the final prediction.
4. Support Vector Machines (SVM)
A Support Vector Machine is an innovative decision-making tool that creates a clear boundary between two groups.
For each feature, churn prediction creates two groups:
- Who is likely to stay?
- Who is likely to churn?
The boundary, called a hyperplane, separates the two groups based on customer data. It ensures that the boundary stays far from both groups.
For instance, a hyperplane separates customers based on whether they spend more than $100 monthly.
Step 5: Model Training
Training the model involves using historical data to teach it patterns associated with customer churn. During this phase, the model adjusts its internal parameters to minimize errors in predicting whether customers will stay or leave.
Example: If customers with low engagement and high complaint rates tend to churn, the model learns to flag similar patterns in future data.
Step 6: Model Evaluation
Once the model is trained, it’s important to evaluate its performance to ensure its accuracy and reliability. Here are some common metrics used for evaluation:
1. Accuracy: The percentage of correct predictions.
Example: If the model predicts churn correctly for 90 out of 100 customers, it has 90% accuracy.
2. Precision and Recall: These metrics benefit imbalanced datasets, where churners may be a small subset of the total customers.
3. F1 Score: A harmonic mean of precision and recall, useful when managing false positives and false negatives. It helps you decide whether the focus should be on catching more churners (recall) or avoiding incorrect flags (precision).
4. Cross-validation: Techniques to validate the model on different subsets of data to ensure it performs well on unseen datasets.
Step 7: Model Validation
Model validation ensures that your model works well with unseen data. You can use techniques like k-fold cross-validation to divide the data into k parts. You train your model using several of these parts while using the remaining parts for testing.

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For instance, in a 5-fold cross-validation, the dataset is split into five parts. Each part is used as a test set once, and the remaining four are used for training.
This process is repeated until every piece of data has been trained and tested. By doing so, your model predicts the data well and works well on entirely new data.
Step 8: Deployment and Monitoring
After successful training and validation, the model is deployed to make real-time predictions in a live environment. However, the process doesn’t end here. Continuous monitoring is necessary to ensure that the model remains accurate over time. As new data comes in, you can update and retrain the model to reflect any changes in customer behavior.
Next steps
Once your churn prediction model identifies high-risk customers, businesses can take specific actions to address and prevent churn:
1. Retention Strategies: Run targeted marketing campaigns for high-risk customers with personalized offers, incentives, or loyalty programs.
Example: Offering a 20% discount to customers who haven’t logged in for 30 days.
2. Customer Support: Provide proactive support to at-risk customers, offering solutions to their issues or concerns.Example: Reach out to customers reporting repeated issues via support tickets with tailored solutions.
3. Product/Service Improvements: Use insights from churn prediction models to improve products or services based on customer feedback and behavior patterns.
Example: Adding a new feature frequently requested by churned customers to retain current users.
End Thoughts
Continuous monitoring and periodic retraining ensure the churn prediction models remain relevant and effectively predict churn over time. The insights generated by the model enable businesses to take proactive measures to retain customers, reduce Churn, and ultimately improve profitability.
In this regard, Netcore’s automated predictive churn model can handle heavy lifting so that you can focus on developing targeted retention strategies.
Netcore’s AI-driven tools can deliver special offers, incentives, or loyalty programs tailored to high-risk customers. Further, Netcore’s customer engagement platform enables proactive support to address concerns and resolve issues effectively. Additionally, actionable insights from the churn prediction model can guide product or service enhancements, ensuring they align with customer feedback and preferences.