Customer churn prediction is the analytical process that businesses use to estimate their unsatisfied customers through a predictive model, find their ratio, and the related issues. Some predictions also help in finding data-driven strategies to proactively address attrition.
Let’s say you have a subscription box service. With churn prediction, you can easily find out the past subscriber data such as delivery frequency, reviews, and skipped boxes to identify those who are no longer interested and ensure targeted offers or support to keep them. Every business needs to stop uncontrolled revenue loss and take action to quickly replace or retain churning customers.
What Is Customer Churn Prediction?
Customer churn prediction is one of the go-to strategies where businesses use data analysis to determine which customers are likely to stop using their product or service within a specific period. Businesses mainly find out the customers at high risk of “churning” or leaving and the related problems behind that.
They mainly follow this process when they want to retain customers and have noticeable increases in the churn rate. It is particularly important for businesses with subscription-based models, telecommunications companies, financial institutions, or any other business with a recurring customer base.
These businesses use this method regularly or at a specific period to monitor customer behavior and find the early signs of potential churn.
Why Predicting Customer Churn Matters?
As a responsible business owner, you must go through an effective churn rate prediction process, especially to maintain a healthy customer base, reduce revenue loss, and improve the customer lifetime value.
It even offers many more advantages. Have a look:
- You can reduce the churn rate directly and ensure a consistent revenue stream for your business.
- You’ll get valuable insights regarding product development, marketing, and customer service.
- It even provides you with the details about your customers’ behavior, giving you an edge over competitors. You can effectively adjust your strategies and offers to attract more supporters.
- After such improvements, you can build long-term relationships with your customers, increasing their overall value.
- Above all, you can focus on resources that are not effectively working, optimize your support, and save on your budget.
How to Predict Customer Churn: A Step-by-Step Guide
To predict customer churn, you need to analyze customer data, identify patterns, build predictive models to find churn risk, and lastly, implement retention strategies to prevent churn.
Let’s not get confused by the above preview. Here is the step-by-step guide you can go through accordingly:
Reliable Customer Segmentation & Data Collection
First off, you have to understand who your customers are about to leave and how they differ from the loyal ones. To do so, you need to segment your customers based on their different needs, behaviors, and usage patterns.
This approach is known as “one-size-fits-all,” through which you can easily define your churn customers and prevent them from churning. Here is a quick overview of your segmentation basis:
| Demographic Data | Age, gender, location, income, etc. |
| Behavioral Data | Purchase history, website activity, app usage, customer support interactions, and so on. |
| Transactional Data | Order frequency, average order value, payment methods |
| Customer Feedback Data | Surveys, reviews, and social media recommendations. |
| Product Usage Data | How often do your customers use your product or service? |
Based on the data sources, you can now easily segment the customers into different groups. This includes demographic segmentation, behavioral segmentation, RFM segmentation, cohort analysis, and so on. Make sure you have the correct data to create an effective prediction model.
Data Analysis & Preparation
When you have gathered all the relevant segments, this is the time to analyze and prepare your data. Though it seems time-consuming, it is super important for accurate churn prediction.
For this, go through your complete data and highlight any missing values. Also, you need to find out the numbers which are much higher or lower than the rest of the data. Now, clean the datasets to ensure accuracy and consistency before model training.
Fill up the missing values with mean, median, or mode. Then, standardize date formats and ensure there are no repeat records.
Analyze Trends and Identify Key Churn Factors
This time, you will have all the data right in your hands. Before you head to building a prediction model, you have to analyze your data at this stage and find the key factors for why your churn is happening. For this, you can easily analyze the churn rate by applying the formulas.
Through this, you can find and compare churn rate data from across customer segments you have made in the first step. For better findings, you can use bar charts or grouped bar charts and analyze segment-based churn values.
When you now find out the reason for each churn, this can include:
- Poor customer service
- High prices
- Better competitors offering
- Lack of engagement
- Product issues
- Billing issues
- Lack of personalization
Identify At-Risk Customers
After analyzing the data trends and reasons behind their churn, you need to define the at-risk customers in this step. For instance, you can go through a manual analysis. For example:
- If your customer’s purchase frequency has decreased by 50% in the last month, assume that these customers are at risk.
- If any of the groups have not logged into the app in the last 30 days, they are at risk.
- Or, if any of them submitted more than 3 support tickets in the last week, they are likely to churn soon.
For better analysis, you can use any CRM system or marketing automation platform and implement these trials. You can go through various other approaches for this, such as creating a scoring system, looking for specific behavioral patterns, analyzing customer journeys, and so on.
Utilize Data Points for Prediction Model
To have a successful prediction, you need to ensure the best fit for the prediction model. This is right where you will put your insights into action and start actively preventing churn.
However, to create a well-proven prediction model, you need to select a machine-learning algorithm that is appropriate for your data. Some of the common algorithms include:
- Logistic Regression
- Decision Trees
- Random Forests
- Gradient Boosting Machines
- Neural Networks
You can try different models and compare their performance to ensure the best one. Even just building the model will not be enough unless you ensure proper training for it. Proper training ensures your model learns from past customer data and can define it into new cases. It is yet simple to do it—
- Use historical data where you already know your churn outcome. Label them either as “positive” or “negative.”
- Then find out the impact on churn.
- In the next step, divide some for the training stage and some for the testing sets. Typically, 70-80% of the data is used for training.
- Use the specific training features and target variables in the model.
- After training is done, use the trained model and make predictions on the testing data.
Monitor Model Performance For Consistency & Refinement
Next, you will monitor your model performance to find out how it is working. Various accuracy metrics can be used here, such as precision, recall, F1-score, AUC-ROC, and so on.
If needed, you may need to refine your data integration and optimize the model based on that. For better results, you can set alerts to notify you when model performance falls below a certain level.
How Machine Learning & AI Improve Customer Churn Prediction?
Machine Learning (ML) and Artificial Intelligence (AI) are the most effective approaches for customer churn prediction as they help analyze large volumes of customer data more precisely. You can easily identify complex data and patterns that are more likely too complicated to do yourself.
For example, a traditional approach can only focus on the number of support tickets. But with the ML model, you can go further and analyze the details of those tickets, including timing and related issues such as low product usage, unusual behavior, or low purchase history. This helps you get a more detailed view of churn risk.
How Does Churn Prediction Help In Retention?
With an effective customer churn prediction model developed, you can easily get insights into which customer groups are about to leave you soon. By this, you can take proactive measures to address their concerns and apply strategies to keep them engaged.
It even offers far greater advantages when you reach out to customers before they churn, saving you from the acquisition cost. Research says that it costs 5x more to acquire a new customer than to retain an existing one.
Plus, with the prediction model, you can easily identify the factors that contribute to churn. You can build targeted personalized retention strategies based on your business needs.
Plus, you can focus on customers who are most likely to churn, instead of ensuring all your retention efforts across the entire customer base. It saves both your time and budget.
Best of all, sometimes churn prediction models work as an early warning system for a company to manage the problem before the customer even realizes there is an issue. This helps improve customer experience.
How to Reduce Customer Churn After Prediction?
To reduce customer churn after predicting, reach out to the right at-risk customers through personalized interventions, offer improved customer service, identify their pain points, and apply result-driven strategies to increase engagement.
Personalized Onboarding
Before you do anything, ensure you have a smooth and engaging onboarding process to attract and retain them. Studies show companies with strong onboarding programs witness up to a 50% increase in customer retention.
For this, you can offer personalized content, tutorials, and resources to guide them through the process and services and address their direct pain points.
Effective Customer Support
It would be better if you could connect with each of your customers with issues before they contact customer support or leave you at all. For instance, you can offer the best support through multiple communication channels such as email, phone, and live chat. Or you can hire a specialized customer success team that will focus solely on at-risk customers.
Targeted Incentives & Offers
This is one of the best ideas to retain customers. You can retain customers up to 6x faster with various offers such as discounts, free trials, and loyalty rewards. You will customize the offering based on customer preferences and usage patterns.
Enhanced Service Value
More or less, your service and business products are all. So focus on improving your service or product based on customer feedback and market trends. Add new features and functionalities that can effectively address customer needs.
Final Thoughts
Customer churn prediction is the most important step for businesses to ensure lasting customer relationships and secure sustainable growth. With the strategies discussed in this guide, you can easily predict your churn rate no matter how large a volume of customer data you have.
Don’t miss out on the benefits of AI-powered predictive models to effectively identify and address churn risk, and improve customer satisfaction.
