Customer Retention- Using Churn Analysis


The business industry depends on its customers for its success and revenue, and that is why companies are now more conscious of the importance of gaining and retaining customers’ satisfaction and keeping them from leaving. Customer relationship management (CRM) helps with marketing by recognizing and selecting target consumers and creating cost-effective and
sustainable relationships with them.

CRM is the process of understanding customer behavior to support organizations in improving customer acquisition, retention, and profitability. Thus, CRM systems use business intelligence and analytical models to pinpoint the most profitable group of consumers and target them to achieve higher customer retention rates.

Those models can predict customers with a high probability to churn based on analyzing customers’ personal, demographic and behavioral data to provide personalized and customer-oriented marketing campaigns to gain customer satisfaction.


Customers become “churners” when they back out or abdicate their subscription and move their business to a competitor. That is called churning, and it is the process of customer turnover.
This is a critical concern for companies with a large and diverse customer base who can easily switch to competitors looking for discounts and better offers. Examples include credit card issuers, insurance companies, and telecommunication companies.

Accordingly, the churn management system has emerged as a crucial game-changing weapon to gain competitive advantage and a founding methodology for a range of systemic customer-focused marketing efforts. With accurate and effective churn management, a company can determine what kind of customer base, as well as which customers are most probably to discontinue and which ones are most likely to remain loyal to the said company. Part of the process is determining, understanding, and analyzing customer lifetime value.

When this kind of knowledge is available to a company, marketing managers are better equipped to make informed and strategic actions to minimize defections, win back valued defectors, and attract and retain the right kind of customers cost-effectively in the future – including those that are unlikely to churn. Data mining is an effective tool for churn analysis to perform two essential tasks:

β€’ Predict whether a particular customer will churn when it happens and the loss incurred
β€’ Understand the reason behind these customers churning and train the model accordingly.

Machine-learning techniques have been widely used for evaluating the probability of customers churning.

A few prominent techniques used in churn analysis are:

● Regression analysis:

Regression analysis techniques investigate and
estimate the relationships among a set of features. Logistic Regression (LR) is the appropriate regression analysis model to use when the dependent variable is binary.
For customer churn, LR has been widely used to evaluate the churn probability as a function of a set of variables or customers’

● Decision Tree:

A Decision Tree (DT) is a model that generates a tree-like structure that represents a set of decisions. DT is a flexible model that supports both categorical and continuous data. Due to their flexibility, they gained popularity and became one of the most commonly used models for churn prediction.

● Bayes Algorithm:

Bayes algorithm calculates the probability that an event might occur based on previous given knowledge of variables present in the equation. NaΓ―ve Bayesian (NB) is a classification technique based on Bayes’ Theorem. For customer churn problems, NB predicts the probability that a customer will stay with his service provider or switch to another one.

● Artificial neural network:

Artificial Neural Networks (ANNs) are machine-learning techniques inspired by the human brain’s biological neural network.
ANNs are adaptive, can learn by example, and are fault tolerant. An ANN comprises a set of connected nodes (neurons) organized in layers. Neurons in each layer use supervised learning techniques. In the case of customer churn analysis, this method has proven to perform better than LR.

The main objective of CRM is to retain existing customers because it is at least 5 to 20 times more cost-effective than acquiring new ones depending on business domains. Customer retention includes all actions taken by organizations to guarantee customer loyalty and
reduce customer churn. Churn prediction includes using data mining and predictive analytical models to predict the customers with a high likelihood to churn/defect.

Thus, these models analyze personal and behavioral customer data for tailored and customer-centric retention marketing campaigns. Customer retention and churn prediction have been increasingly investigated in many business domains, including, but not limited to, telecommunication banking, retail, and cloud services subscriptions.

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