Churn prediction modeling and survival analysis are powerful customer retention tools. A modern business can apply them for business strategy, profit planning, and targeted marketing.

Forecasting business revenue and expenses plays an important for in business strategy and planning. A business usually has enough information to project the costs but revenue. Future revenue depends on many internal and external factors related to customer, service, and market. Churn prediction and survival analysis help with revenue forecast through estimation of customer lifetime value (LTV) and improved customer retention.

Customer Lifetime Value (LTV)

Customer lifetime value is future business revenue generated by a customer. A customer can bring revenue in different forms; direct purchases, referrals - essential for any business, word of mouth, etc. All these actions of a customer contribute towards his value for a business. However, companies often care about the direct purchase part of LTV, as it is challenging to quantify referrals and word of mouth.

Customer Churn Prediction

Customer churn is the event of cancellation of the customer's service. Churn can be customer-initiated or service-provider initiated. In churn modeling, the focus is always on customer-initiated churn. Customer churn can happen due to several reasons such as;

• Quality of service
• Bad customer experience
• Misinformation or miscommunication by sales team
• Better pricing by the competitor
• Customer moving to different location
• Change of business

Customer churn prediction is a classification problem in machine learning, where we would train a supervised learning algorithm on historical examples of customers labeled as 'churn' and 'no-churn'. Most of the classification algorithms are very good at predicting customer churn.

Machine Learning For Churn Modeling

Machine learning-powered churn analytics can help a business to achieve higher LTV by increasing customer retention period;

• Descriptive Analytics to analyze what factors have been driving customer attrition. For instance, one of the factors could be poor customer support service if the descriptive analytics are showing that most customers called customer support 3-4 times before the customer. The business kind of wasted an opportunity to retain them.
• Predictive and Prescriptive Analytics A business can use predictive and prescriptive analytics in combination to increase the retention period of vulnerable customers. Once a business know predicted customers who are likely to churn and also the factors driving the churn, the business can launch a targeted campaign for those customers as well as fix any gaps in service quality or delivery.

Customer churn prediction quantifies the immediate risk that customer churn will take place at time t given that the customer already survived to time t. It does not tell us how long a customer will keep a subscription or contract with the business. For that problem, there is another method known as customer survival analysis, also known as customer retention rate analysis (discussed below).

Customer Survival Analysis

Customer survival analysis is essentially a customer retention rate analysis. Businesses see a lot of value in predicting the time when a customer will churn - also known as survival analysis.

Survival analysis allows a business to estimate customer loyalty, customer lifetime value (LTV), and expected revenue in the future. In contrast, churn analysis helps to predict instance business risk at time T due to potential churn (of existing customers) in the next N weeks.

How Can Datalya Help with Customer Churn & Survival Analysis?

We help financial services businesses with the development of churn prediction models and customer survival analysis. Over the last few years, we have delivered success to many clients in commercial and investment banking, insurance, brokerage, and planning businesses. If you have any questions or need help, please do not hesitate to contact us through email or phone.