How to implement Customer Churn Prediction using Machine Learning
We will explore the churn rate in detail and do an example implementation of customer churn using a machine learning model
Published on:
January 12, 2024
Organizations perform better with customer churn prediction when implementing functional business applications of machine learning. The problem arises when there is a lot of high-quality fresh data to work with to increase profits.
The churn rate is an essential metric for customer satisfaction. A high churn rate means customers are unhappy with your product/service and leave you. Even a tiny change in churn rate compounds over time and translates to almost 12% of the churn rate yearly.
According to Forbes, acquiring a new customer is five times more expensive than retaining the existing one. The churn rate highlights how many current customers are leaving your business; lowering the churn rate significantly influences your revenue streams.
What is the Churn Rate?
The churn rate measures the number of individuals bouncing off a collective group over a specified period. The churn rate is related to the business case of customers that stop buying from your organization.
Subscription-based, membership-based, and Software as service business models are at the forefront of innovative customer retention strategies. And analyzing growth in this competitive space often involves tracking metrics like revenues, customer ratio, etc., performing customer segmentation analysis, and predicting lifetime value. The churn rate is an input of the customer lifetime value model that guides the estimation of net profit contributed to the entire future relationship with a customer; it calculates the percentage of discontinuity in subscriptions by consumers of a given service or product over a specific period.
Enhancing customer retention is a continuous process, and understanding the churn rate is the first step in the right direction. In the SaaS environment, market segmentation is evident, as there are alternatives for any SaaS product. You can learn about your Customer (KYC) and effective retention and marketing strategies for subscription-driven businesses by analyzing the churn rate.
The churn can be classified into the following:
- Customer and Revenue Churn
- Voluntary and Involuntary churn
Customer and Revenue Churn:
Customer churn is the rate at which customers cancel their subscriptions. This is also sometimes referred to as subscriber churn or logo churn. Its value is always represented in percentages. While Revenue churn, on the other hand, is the loss in your monthly recurring revenue ( MRR). Customer churn and revenue churn are both different. One might have no customer churn but still, have revenue churn if customers are downgrading subscriptions. Negative churn is an ideal situation that only applies to revenue churn. The amount of new revenue generated from your existing customers through upsells, cross-sells, or new signups is more than the revenue you lose from cancellations and downgrades.
Voluntary and Involuntary churn:
Voluntary churn happens when the customer cancels and takes the essential steps to exit the service. This could be because of dissatisfaction or not receiving values. Customer satisfaction and loyalty can be achieved, but churn will always be a part of your business.
- Poor service quality,
- Response rate,
- Dissatisfactory Customer Experience,
- Finance issues,
- Changed customer needs,
- Consumers don’t see the value,
- Long-time customers don’t feel appreciated
Achieving a 0% churn rate is impossible. However, the trick is to keep the churn rate as low as possible at all times.
What is the importance of Customer Churn Prediction?
The impact of the churn rate is straightforward. Therefore, we need strategies to minimize it. Predicting churn helps in creating proactive and targeted marketing campaigns who are about to churn. And forecasting customer churn with the help of machine learning is a powerful way to identify and predict churn by
- Identifying at-risk customers
- Identifying customer's pain points
- Identifying strategies to lower churn and increase customer retention.
What are the challenges of building an effective churn model?
Let’s look at the significant challenges that make it difficult for you to build an effective churn model.
- Lack of information or domain expertise
- Inaccurate or complex customer data
- Week analysis of attrition
- Lack of a coherent selection of a suitable churn modeling approach
- Line of business ( LOB) of services or products
- Churn event censorship
- Imbalanced data
- Concept drift is based on changes in customers' behaviors driving churn.
What are the different use cases of Churn Prediction?
Churn prediction differs for every company based on their Line of Business (LoB), data architecture, or operation workflow. The customer churns prediction model has to be aligned with the business strategy, goals, needs, and expectations.
Some of the use cases of customer churn prediction models are as follows:
- Retail market
- Subscription-based business models
- Marketing
- Human resource management
- Telecommunications
- Software as a Service Provider ( SaaS)
How to Design the Churn Prediction Workflow
The scope to build an ML-powered model to forecast customer churn involves the following process:
- Defining a problem: Understanding the insights you need from the prediction and analysis is critical. Understand the problem and collect requirements, customer pain points, and expectations.
- Establishing a data source: Specify all the data sources essential for the modeling stage. Some popular sources of churn data are CRM Systems, customer feedback, or analytic software.
- Data Preparation, analysis, and preprocessing: The Raw historical data used for solving the problem and creating predictive models needs to be transformed into a suitable format for ML algorithms.
- Testing and Modeling: It includes the development and performance testing of the churn prediction models with numerous machine learning algorithms.
- Deployment and Monitoring: This is the last phase of applying machine learning for churn rate prediction. In this stage, the most suitable model is sent to production. It can either be integrated into existing software or become the essential core of the newly built applications.
Conclusion
The churn rate is an essential indicator for subscription-based and membership-based organizations. Identifying customers who are unhappy and are most likely to churn can help managers analyze product or pricing plan weak points, operational issues, customer needs, and expectations. This way, it introduces the most proactive ways of reducing churn.
