Churn Prediction Model: A Data-Driven Approach to Customer Retention

Nobody in business likes to lose loyal customers. In its early phases, a company’s main focus is often on acquiring new customers.
The business then grows by offering a wider variety of products to current customer base or by attempting to raise the frequency with which they transact on the current product.
Once business achieves it’s target of acquiring enough new customers, the business will eventually grow to the point where it will need to choose a little more defensive strategy and focus on retaining relationships with its current customers.
Despite giving users the finest experience possible, a small percentage of users will always be dissatisfied and choose to cease using the service.
Finding the most effective strategy to stop these departures is the company’s next challenge. Among various models, the churn model can be useful in this circumstance.

This post will demonstrate how to create a churn prediction model and enhance customer retention.

What is a Churn Model ?

Churn prediction is the process of identifying which potential customers will STOP using your product.
Churn prediction aims to comprehend more general turnover tendencies as well as to provide solutions to questions like “How many of the current customer base will return?”
Proactive actions can be taken if business knows when a customers is most likely to leave.
You must use the essential metrics unique to your organization to accurately anticipate if the consumer will return.

How to approach ?

Data Preparation and Defining churn
The first step in creating a churn prediction model is classifying consumers as churned or non-churned. Churned customers are those who end their relationship with your company.
It’s challenging and crucial to draw this distinction. You must define churn in a way that can be put into practice. Your line of business has a significant impact on how you define the end of a relationship.
Churn can be identified in subscription-based sectors like OTT When a customer cancels their subscription.
It is impossible to identify such a tipping moment in organizations like online retail, a careful analysis of consumer trends for a merchant or online company.
A very important factor is giving your definition of churn a time limit. Even while a model that predicts a customer’s turnover with accuracy tomorrow could not give you enough time to act.
As a result, you need to be clear about how long it will take to set up your retention actions. On the other hand, determining a customer’s propensity to discontinue use after a year is more difficult.
This forces you to strike a delicate balance between the amount of time you have to react and the accuracy of your predictions.
Decide on the action and time interval and then tag your customers as churn and non-churn in that time interval.

Model Building
Classification algorithms provide the basis of contemporary churn models. For this purpose, a variety of classification methods can be utilized, such as logistic regression, random forest, and XGBoost.
Depending on the amount of data and the application, select a classification model.

The train-valid-test split is a technique to evaluate the performance of your machine learning model, whether it be for classification or regression. A given dataset is divided into three subsets.
It is extremely important to test your model using out-of-time data. Out-of-time testing entails using the most recent, unseen data to validate the model and recording its performance to look for
any performance dips in the model’s output predictions.

While variable selection, try to completely comprehend the factors that are utilized to make decisions and are making sense in terms of business.
This could both reveal problems with the model or data and give the various teams very useful information.
This exercise can provide information on how important business metrics affect customer churn.

Performance Metrics
For classifiers, there are numerous performance metrics available. It is not enough to look at the churn model’s accuracy because, if the churn rate is 20% and it forecasts that all customers won’t leave, it will be 80% accurate.
This is worthless, though. Consequently, you should take into account the model’s sensitivity and precision, which gauge how many of the clients it predicted would leave really did so.
Additionally, the effectiveness of binary classifier models is evaluated using the Gini coefficient, a machine learning statistic. The Gini coefficient can be used between 0 and 1.
The model performs better the greater the Gini coefficient.

Conclusion

Once you have a model, your customer base will have varying levels of churn risk. For each part, a distinct plan of action may be used.
You additionally have the most crucial metrics from the model, which can start to automatically identify situations that tend to make customers more likely to leave and that demand for an immediate response.