The importance of churn prediction
Jan 27, 2021 ● 7 min read
Churn prediction has become a huge part of many modern businesses because of the performance gains that it offers. Churn prediction is useful in helping companies determine metrics like customer retention and revenue generation.
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In the beginning, only telecom companies could afford to use churn prediction models and pay big data scientists to analyze their customer data. Now, customer churn rates and customer churn risk can be analyzed using churn data more easily.
One essential aspect of Churn prediction is in its use for preventing customer churn. In this blog post, the basis of customer churn will be discussed, and major aspects of churn prediction such as churn prediction models and some of its use cases.
Customer churn is used to describe the loss of customers by a company. Generally, if a customer stops using the services of a company for a long period of time (which varies depending on the products or services of the company), such a customer is considered churned. Customer churn is also called customer attrition.
Customer attrition is important to any business because a metric such as churn rate can be used for churn prediction and can be used to determine the retention rate.
Customer churn rate refers to the percentage of customers that stopped using a company’s services or products within a monitored time frame.
How Do Your Calculate Churn Rate?
Generally, churn rates are calculated by dividing the number of customers that left a company at the beginning of a chosen time frame and the number of customers that the company had at the beginning of the chosen time frame. The customer attrition rate is usually presented as a percentage.
The ideal churn rate for any company is 0%. However, depending on the customer base of the company and the type of business carried out by the company, an acceptable churn rate might range from 0% to a maximum of 5% churn rate.
Note: New customers are not considered when calculating for attrition rate.
For example, if the total number of users of a particular software is 200 at the beginning of the month, and the total number of users left is 220, but 50 new customers joined during the month while 30 customers left. The attrition rate is calculated as 30/200, which equals 15%.
Customer Churn Prediction
Customer churn prediction is regarded as one of the most popular use cases of big data by businesses. It is also called deflection probability. It involves ways in which customers that are likely to stop using certain products and services of a company are predicted based on how they use the products or services.
The method of customer churn prediction commonly used by businesses is called the binary classification task. The binary classification task takes in the input as a question in the form of whether a customer would leave the company within a specified time frame, and the output is either yes or no.
The most common use of Customer deflection probability is in SaaS companies and membership-based businesses that charge monthly, annually, or quarterly on an ongoing basis.
Usually, the first step of carrying out deflection probability analysis is setting up a customer outreach model that generates accurate data for churn prediction.
There are two steps to reducing customer attrition with targeted, proactive retention. The first step is by predicting in advance the customers that might churn with the use of attrition analysis.
The second step is carrying out market actions that have been tested or promise to have the best retention impact on the customers that might churn. While attrition deflection probability prediction and customer retention are simple in theory, accurate attrition analysis is affected by several factors.
Some of the factors limiting accurate attrition analysis include:
- Inaccurate customer data
- Weak attrition analysis model
- Type of business
- Customer data corruption
It is impossible to separate attrition analysis and machine learning. Every attrition analysis model is built based on some form of the machine learning model. In the past, only big companies were able to afford attrition analysis services. Now, almost every business has access to at least one form of churn prediction service.
Steps of churn prediction:
- Collection of historical customer data (usually stored in CSV format).
- Uploading collected customer data to a prediction service that creates a predictive model.
- Use the attrition analysis model to predict future churn rates and determine what customers are at risk of leaving.
The data collection process of attrition analysis relies on machine learning, a branch of data science that uses artificial intelligence and models to process customer data. The data collection process is an important part of attrition analysis, and churn rejection as the accuracy of the prediction relies on the accuracy of the collected data.
To properly categorize collected data, customers are represented based on information relevant to their churn. Each piece of customer information is called a feature, and the process of separating useful features from redundant ones is called feature engineering.
The four main types of features used by prediction services:
- Customer features
- Support features
- Usage features
- Contextual features
While feature categorization is generic, the usage and context of data classification are specific to the type of business or service analyzed.
The feature engineering of every business is different as it relies on the business model of the company to which the attrition analysis is to be applied.
Two major factors to consider in the data collection process of churn prediction; time frame and data extraction.
The time frame chosen for customer data analysis would depend on the type of business. For instance, a SaaS company sells yearly subscriptions and might therefore need to predict churn for six months ahead by using data from the previous year and the first six months of the current year.
Data extraction allows the historical data obtained from customers to be used to create a binary classification model for churn data analysis.
Note: it is important not to include the data of any new customer that joined the company after the beginning of the chosen time frame. This is because it is impossible to obtain substantial historical data on them.
The next step involves uploading the customer data obtained by the company and creating a customer churn model for an accurate churn prediction. Data prediction services such as BigML or Google Cloud ML Engine allow historical data to be uploaded through a web interface or an API. These data prediction services help to create churn predictions by making use of concepts like logistic regression and other data analysis models, therefore, eliminating the need to deal with the complexities of churn prediction.
It is important to note that an attrition analysis is only as good as the data sets from which it is predicted. Therefore, proper care must be taken to ensure that the process of extracting the data set for predicting churn is as accurate as possible. You can use tools like BigML to check your data for potential bugs in the data extraction process and correct them before continuing to create a prediction framework.
A decision tree is a form of data representation that associates questions with feature values and a certain number of possible answers, which are represented by branches. A decision tree enhances the binary classification model by showing that the results can be based on several factors considered by the attrition analysis model. Data points based on selected features are used to create the decision tree, and the results are represented as branches.
A decision tree consists of several nodes, each of which is associated with a question on a feature value, and several possible answers represented by colored branches. The associated outputs to the decision tree are represented as leaves. The initial question is the root note, and any answer chosen from that point takes you down a branch representing that answer, which then leads to another node. The process is carried out repeatedly until a leaf is reached. The leaf is the final output of the prediction.
Churn Prediction Analysis
The model created with the use of data prediction services can then be used to make predictions on all customers and analyze those that are at risk of churning.
Why is Churn Prediction Important?
Generally, three possible ways with which modern businesses can generate more revenue are by gaining more customers, upselling existing customers, or increasing customer retention. Every revenue generation method involves an initial cost, and research has shown that it costs more to gain new customers than it costs to retain existing customers. Therefore, attrition analysis is a useful tool in determining the ultimate return on investment for a certain product or service (the ratio of the extra revenue obtained by employing one of these strategies to the cost of applying them).
Many businesses now use a subscription-based business model for their businesses. Attrition analysis provides such companies with a better understanding of their customer retention and ways of keeping their customers.
Attrition analysis reduces customer churn rates and helps a business understand the steps necessary for preventing voluminous loss of revenue due to customer churn. Attrition allows a company to determine the lifetime value prediction of a customer.
While every business is at the risk of churning customers, carrying out attrition analysis on any business reduces the churning risk significantly. Tools like Optimove provide a combination of cutting-edge attrition analysis and marketing action optimization engine to help a marketer implement the right retention techniques.
After the customers at risk of churning have been identified, the next step is to strategize the marketing actions necessary to increase the chances of the churn-probable customers remaining a customer. To compensate for the fact that every customer is different and has different preferences, a technique known as the “targeted proactive retention” is practiced.
Limitations of old methods of predicting churn
The point of predicting churn is the attempt to understand how certain customer behaviors and characteristics affect the risk and timing of customer churn. The accuracy of a predicted customer churn depends largely on the accuracy of the technique used. Old methods of attrition analysis relied on the quantification of risk-based static data and measurements e. g information showing how a customer exists at the moment but not one predicting the future. Accurate churn data would reduce the need for companies to risk customers churning.
Many of the common and cheaper churn predictions rely on older statistical and data mining techniques. Modern and more accurate attrition analysis models can predict future customer behavior using customer lifetime value (LTV) calculation for every customer. This would enable companies to adjust to the best practices that would increase customer retention.
Lifetime value decline prediction
Some prediction services can help to identify customers whose lifetime value prediction has declined substantially in recent times and are might churn in the future. This form of analysis allows companies to carry out targeted, proactive retention and churn prevention.
How do you forecast customer churns?
To forecast customer churn, you need to understand the churn rate of your company and use historical customer data obtained by your company for a particular period to calculate the churn rate and draw up an attrition analysis model.
Customer attrition can then be forecasted with the results obtained from a customer churn prediction analysis. Choosing the right attrition analysis service is as important as carrying out attrition analysis with accurate data.
Are Customers in the Lower Income Bracket more likely to churn?
While the churn rate of a company and the probability of customer attrition for a company is specific to the type of business carried out by that company, research shows that customers with lower income and lower levels of education are more likely to experience attrition.
It was suggested, however, that the lower churn rate of higher-income customers might be due to the lower price sensitivity of such customers. Therefore, the business model of a company can be adjusted appropriately with churn prediction models and marketing techniques aimed at reducing customers who might churn.
Some marketing techniques used include improved customer service, personalized products, services recommendation, and customization options.