Predicting customer churn is one of the most popular machine learning tasks. What tools and methods exist for this? Find out from this article.
- Sahar F. Sabbeh. Machine-Learning Techniques for Customer Retention: A Comparative Study
- Federico Castanedo, Gabriel Valverde. Using Deep Learning to Predict Customer Churn in a Mobile Telecommunication Network
- Philip Spanoudes, Thomson Nguyen. Deep Learning in Customer Churn Prediction
- Praveen Asthana. A comparison of machine learning techniques for customer churn prediction
For any business, customers are the basis for its success and revenue and that is why companies become more aware of the importance of gaining customers’ satisfaction. Customer relationship management (CRM) supports marketing by selecting target consumers and creating cost-effective relationships with them. CRM is the process of understanding customer behavior in order to support organization to improve customer acquisition, retention, and profitability. Thus, CRM systems utilize business intelligence and analytical models to identify the most profitable group of consumers and target them achieve higher customer retention rates. Those models can predict customers with high probability to churn based on analyzing customers’ personal, demographic and behavioral.
Machine-learning techniques have been widely used for evaluating the probability of customer to churn. Based on a survey of the literature in churn prediction, the techniques used in the bulk of literatures fall into one of the following categories:
- Regression analysis;
- Tree – based;
- Support Vector Machine;
- Bayesian algorithm;
- Ensemble learning;
- Sample – based learning;
- Artificial neural network;
- Linear Discriminant Analysis.
The result of the study:
Federico Castanedo, Gabriel Valverde. Using Deep Learning to Predict Customer Churn in a Mobile Telecommunication Network
Customer churn is defined as the loss of customers because they move out to competitors. It is an expensive problem in many industries since acquiring new customers costs five to six times more than retaining existing ones. In particular, in telecommunication companies, churn costs roughly $10 billion per year. A wide range of supervised machine learning classifiers have been developed to predict customer churn. In general, these models base their effectiveness in the feature engineering process which is usually time consuming and thus tailored to specific datasets.
To apply deep learning models to predict customer churn prediction in a mobile telecommunication network, we implemented a multi-layer feedforward architecture and introduced a novel way to encode input data to learn hierarchical representation on real datasets. Our experiments suggest multi-layer feedforward models are an effective algorithm for predicting churn and capture the complex dependency in the data. Experiments shown that the model is quite stable along different months, thus generalize well with future instances and do not overfit the training data.
As companies increase their efforts in retaining customers, being able to predict accurately ahead of time, whether a customer will churn in the foreseeable future is an extremely powerful tool for any marketing team. The paper describes in depth the application of Deep Learning in the problem of churn prediction. Using abstract feature vectors, that can generated on any subscription based company’s user event logs, the paper proves that through the use of the intrinsic property of Deep Neural Networks (learning secondary features in an unsupervised manner), the complete pipeline can be applied to any subscription based company with extremely good churn predictive performance. Furthermore the research documented in the paper was performed for Framed Data (a company that sells churn prediction as a service for other companies) in conjunction with the Data Science Institute at Lancaster University, UK. This paper is the intellectual property of Framed Data.
We present a comparative study on the most popular machine learning
methods applied to the challenging problem of customer churning prediction in the telecommunications industry. In the first phase of our experiments, all models were applied and evaluated using cross-validation on a popular, public domain dataset. In the second phase, the performance improvement offered by boosting was studied. In order to determine the most efficient parameter combinations we performed a series of Monte Carlo simulations for each method and for a wide range of parameters. Our results demonstrate clear superiority of the boosted versions of the models against the plain (non-boosted) versions. The best overall classifier was the SVM-POLY using AdaBoost with accuracy of almost 97% and F-measure over 84%.