Churn Prediction
What is churn?
Customer churn is the tendency of customers to leave your service or stop being paying clients of a particular business. Knowing your customers’ churn rate is crucial for any company since it costs five times as much to get a new client as it does to retain an existing one. Data science and machine learning can help you predict your churn rate and create the right retention strategy that targets the customer base that is most vulnerable to churn.
Reducing churn is a goal for every company, and by leveraging artificial intelligence techniques like machine learning, companies can predict potential churners and add more value to their marketing efforts. The challenge of implementing a successful churn prediction model is to build customer loyalty and increase revenue.
In this sense, predicting and further preventing customer churn will not only save your company a lot of money and time in acquiring new clients but it can also create a new potential revenue stream.
Check out our use case on Churn Prediction for Gym Customer Retention to learn more about how data science can help you predict customer churn.
Five Steps to Build a Churn Prediction Model
Understanding the Business Case
The first step is to get to know your business, understand the need and desired outcome. You can read our use case to learn how we developed a churn prediction model for a sport center.
Collecting and Cleaning Data
It is important to understand what data will be the most useful for the creation of your churn prediction model. Today, companies collect a lot of data about their customers using CRM systems, sentiment analysis, web analytics, and so on. A big step in data preparation is converting all this raw information into structured data so that your machine learning algorithm can process it.
Extracting and Selecting Features
It is crucial to determine which variables represent behaviors associated with customer interactions with a product or service (customer demographics, behavior, and other related features such as communication preferences, past purchasing behavior, or birthdays). Then, we need to create a data set that contains only the most relevant information about the attributes affecting churn.
Building a Churn Model
There are many approaches to predict churn, for instance, binary classification, logistic regression, decision trees, random forest, and others. We will select and build the most effective predictive model, which will help you achieve the desired result in the best way.
Deployment and Monitoring
Once you have developed a model, you need to integrate it with existing software. It is also important to test and monitor the performance of the model to adjust timely features