Effective Customer Churn Analysis & Prediction

churn analysis

Customer churn is a measurement that shows how many clients discontinued a service, an application or stopped buying a product during a certain period of time. Churn prediction is one of the most popular applications of machine learning and data science in business. Although at first, churn analysis was essential for telecoms, now it is applicable for businesses of all sizes, including startups. Marketers with tight budgets are strongly relying on strategies build around churn and retention, considering such strategy the most cost-efficient.

Importance of churn analysis

On average 65% of company’s business comes from existing customers and acquiring the new ones is 15 times more expensive than retaining existing customers.

Churn analysis is mostly applicable in the following areas:

  • For subscription-based businesses churn is a critical metric as every customer they lose results in the loss of recurring revenue.
  • E-commerce companies are highly interested in providing their customers with timely communication without overspending on discounts and special offers for people who were not really about to churn.

Individualized customer retention solution could be a good idea. But being very time and money consuming it’s almost impossible to implement since businesses cannot spend much time on each of their multiple customers. On the other hand, knowing that a particular customer is about to churn, you could direct the retention efforts directly on her.

Churn prediction and analysis steps

The main goal of churn prediction is detecting which customers are likely to cancel a subscription to a service based on how they use it. It is done in three major steps:

  1. Data gathering and preparation
  2. Building the predictive model
  3. Testing and validation of the model on real customers

Each of these steps includes a variety of acquired techniques and smaller steps necessary for the best possible result.

1. Data gathering and preparation

The first step of data gathering includes the process of “feature engineering”. In order to predict churn on a particular customer, he or she is compared to another similar group of customers. Such comparison is based on particular pieces of information called “features” about each group of customers. The features include information about customers’ demographics, interactions history with the service, and other customer information relevant to a particular case. For example, customer’s age, level of education, number of times he logged into the app, time since last login, device type, and etc.

2. Building the predictive model

When building a churn prediction model, one of the most critical steps is to properly define what churn actually is, and how it can be translated into a variable that can be used in a machine learning model. The definition of churn is totally dependent on the business model and can differ widely from one company to another.

In order to effectively analyze and control customer churn, it is important to build an effective and accurate customer churn prediction model. Statistical and data mining techniques are utilized to construct the churn prediction models. The data mining techniques can be used to discover interesting patterns or relationships in the data, and predict or classify the behavior. In other words, it is an interdisciplinary area with a general objective of predicting outcomes and employing sophisticated data processing algorithms to discover mainly hidden patterns, associations, and anomalies in customer data.

3. Testing and validation of the model on real customers

When the model is ready and it is ensured that it takes into account all the special needs of a particular business and its customers, the testing period starts. It normally takes up to a few months. The model is fine-tuned according to the results. Such custom-built models have a solid advantage compared to automatically generated models – they stay very flexible and can be developed according to each company’s growing demands.

Conclusion

Customer churn analysis allows to minimize acquisition costs and increase marketing efficiency, preparing a solid base for future marketing analysis and campaigns. Customer churn analysis opens new opportunities for cross-selling and upselling and serves as one of the starting points for customer-driven product development, keeping customers engaged and loyal over time.

InData Labs customers are already enjoying the results of our customized churn prediction models through applying pattern recognition on their data in order to find those patterns that say who is going to leave soon. Not sure where to start with churn analysis in your business? Let’s talk!