Predictive Analytics for Customer Churn Prediction

Predictive Analytics

Detecting the possibility of cancellation or disconnection of service by a customer is known as churn prediction. Churn, or churn rate, is the quantification of the number of customers that have cancelled their subscriptions. It is calculated over a specified time period, usually on a monthly basis since most payment-oriented subscriptions have a monthly renewal scenario. 

Churn prediction is crucial for most businesses given the numerous benefits of conducting the measurement, including identifying the target audience and market compatibility. Predicting customer churn and preventing the possibility of revenue loss builds up the added potential for every business. Hence, knowledge of churn rates has a significant impact on your company's revenue generation. 

You can build a career in data science by enrolling yourself in data science training on renowned platforms such as Imarticus and learn more about prediction strategies.

What Does Churn Factor Stand for?

Churn factor displays the customer churn after considering the activity frequency of consumers. A customer with a high churn factor usually represents an already churned consumer. It can be defined by simply dividing the activity frequency of a customer by the quantitative time since the last activity of the same. 

Considering every activity of the consumer in context can ultimately assist in creating a powerful but uncomplicated prediction of customer churn. Using churn factor analysis, you can attain a better understanding of customer behaviour. 

You can retain your customers for longer time frames upon gaining a clear perception of their behaviour and activity patterns, which can be attained by analysing the churn risk of your customers. Thus, calculating the churn factor can rapidly enhance the potential of your business. 

Importance of Customer Churn Prediction

The customer churn prediction process is a necessary calculation for every subscription business. It presents a fact of business life where even the slightest changes in churn rate might cause a significant impact on the business, leading to fluctuations in revenue collection. 

What is Customer Churn?

Understanding the source of consumer engagement is highly valuable in developing your retention policies. Customer churn is a major problem faced by businesses across different sectors, also known as customer attrition. Online businesses refer to a customer as churned when the latter ceases connection with the company or unsubscribes from the service. 

A service declares churned consumers when a substantial amount of time has elapsed since the last activity of the customer on the website. Customer churn also includes cases where the customer retracts their subscription. 

Benefits of Customer Churn Prediction

You can predict churn using an appropriate machine learning algorithm that calculates the churn risks of every single customer. The prevalent advantages of churn prediction are listed below:

  • It detects the accounts on the verge of getting churned and allows you to save them by adding the necessary support and suitable marketing strategies. 
  • Provides transparent insights about the user experience.
  • You get to identify the major pain factors by discovering the predominant friction points in the customer experience, such as if certain consumer accounts are unhappy with the overall features. 
  • Creates prime accounts for long-term relations, a target audience for cross-selling and upselling programmes, as well as growth opportunities in general. 
  • Churn analysis provides better outcomes in CS playbooks by optimising the tasks. 

Predictive Analysis in the Prediction of Customer Churn

What is Predictive Analysis?

The process of forecasting possible future outcomes using data analysis is known as predictive analysis. It uses machine learning, statistical computations, AI models, and data analysis to detect patterns in consumer behaviour.

Top Prediction Models in Customer Churn 

The machine learning models commonly applied in churn prediction are as follows:

  • Logistic Regression
  • Decision Trees
  • Bayes Algorithm
  • Linear Discriminant Analysis
  • Support Vector Machines

Major Steps in Predictive Analysis that Reduce Customer Churn

Customer churn analysis is actively used by IT analysts and data scientists to assist with the customer retention of a business. You can get a quick overview of the necessary tools for churn prediction from the list below:

  • Calculating the Churn Rates of Your Customers: The churn rate is a key performance indicator, or KPI, where you can calculate the KPI using the simple formula stated below:

100 x (Lost consumers ÷ Total consumers at the chosen beginning time)

It is necessary to maintain the accuracy of your churn rate since customer churn prediction portrays a direct impact on the business sales cycle.

  • Data Integration: You can generate predictions about the future of the business using predictive analytics for data analysis. 
  • Creating an Effective Churn Prediction Model: Upon training, you can use algorithms to point out inactive accounts and check for potential churner behaviours. 
  • Analyse the Churn Risk Score: Churn risk scores range from 0 to 100. Customer accounts with higher churn risk scores have greater chances of churn behaviour. The high churn risk score is 76-100, medium churn risk accounts depict a score of 51-75, and the low churn risk rate is 0-50. 
  • Consumer Segmentation: You can create well-tuned segments to form groups based on common features using machine learning algorithms. 
  • Applying a Suitable Cloud Data Platform: It provides a marvellous solution in the execution of customer churn prediction analytics. 

The Challenges in Customer Churn Prediction

The loss of consumer engagement over time, or churn rate, is one of the essential business metrics that can be tracked by the company. You can learn more about churn prediction analytics by opting for a career in data science and analytics.

Customer churn prediction includes a few obstacles, such as:

  • Changes in Stakeholders can alter past relations and consumer engagements.
  • The technique has to be accurate, which plays a critical role in the success of effective retention policies. 
  • Revenue generation might reduce due to retention-based discounts and offers. 
  • The predictive calculations need to be conducted on real-time data instead of static data to avoid the risks of inaccuracy.


Customer churn might cause a cost situation that includes losses in revenue, marketing costs, and customer replacement expenses. Reduction of the churn rates is a necessary growth factor for most online services and businesses.

You can gain a precise understanding of customer churn and other relevant topics by joining informative online tutorial programs. If you wish for a career in data science, you can opt for a data science course or a data analytics course such as Imarticus’s Postgraduate Program in Data Science and Analytics. Data analytics courses such as these can help you can learn more about predictive analytics and predictive analysis models.

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