Ensembles of probability estimation trees for customer churn prediction

Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both

Handling class imbalance in customer churn prediction

Customer churn is often a rare event in service industries, but of great interest and great value. Until recently, however, class imbalance has not received much attention in the context of data mining (Weiss, 2004). In this study, we investigate how we can better handle class imbalance in churn prediction. Using more appropriate evaluation metrics (AUC, lift), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific modelling techniques (gradient boosting and weighted random forests) compared to some standard modelling techniques. AUC and lift prove to be good evaluation metrics. AUC does not depend on a threshold, and is therefore a better overall evaluation metric compared to accuracy. Lift is very much related to accuracy, but has the advantage of being well used in marketing practice (Ling and Li, 1998). Results show that under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC. Unlike Ling and Li (1998), we find that there is no need to under-sample so that there are as many churners in your training set as non churners. Results show no increase in predictive performance when using the advanced sampling technique CUBE in this study. This is in line with findings of Japkowicz (2000), who noted that using sophisticated sampling techniques did not give any clear advantage. Weighted random forests, as a cost-sensitive learner, performs significantly better compared to random forests, and is therefore advised. It should, however always be compared to logistic regression. Boosting is a very robust classifier, but never outperforms any other technique.rare events, class imbalance, undersampling, oversampling, boosting, random forests, CUBE, customer churn, classifier

CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services

The early detection of potential churners enables companies to target these customers using specific retention actions, and subsequently increase profits. This analytical CRM (Customer Relationship Management) approach is illustrated using real-life data of a European pay-TV company. Their very high churn rate has had a devastating effect on their customer base. This paper first develops different churn-prediction models: the introduction of Markov Chains in churn prediction, and a random forest model are benchmarked to a basic logistic model. The most appropriate model is subsequently used to target those customers with a high churn probability in a field experiment. Three alternative courses of marketing action are applied: giving free incentives, organizing special customer events, obtaining feedback on customer satisfaction through questionnaires. The results of this field experiment show that profits can be doubled using our churn prediction model. Moreover, profits vary enormously with respect to the selected retention action, indicating that a customer satisfaction questionnaire yields the best results, a phenomon known in the psychological literature as the ‘mere-measurement effect’.

Separating Financial From Commercial Customer Churn: A Modeling Step Towards Resolving The Conflict Between The Sales And Credit Department

In subscription services, customers who leave the company can be divided into two groups: customers who do not renew their fixed-term contract at the end of that contract, and others who just stop paying during their contract to which they are legally bound. Those two separate processes are often modeled together in a so-called churn-prediction model, but are actually two different processes. The first type of churn can be considered commercial churn, i.e., customers making a studied choice not to renew their subscriptions. The second phenomenon is defined as financial churn, people who stop paying because they can no longer afford the service. The so-called marketing dilemma arises, as conflicting interests exist between the sales and marketing department on the one hand, and the legal and credit department on the other hand. This paper shows that the two different processes mentioned can be separated by using information from the internal database of the company and that previous bad-payment behavior is more important as a driver for financial than for commercial churn. Finally, it is shown on real-life data that one can more accurately predict financial churn than commercial churn (increasing within period as well as out-of-period prediction performance). Conversely, when trying to persuade customers to stay with the company, the impact of ‘loyalty’ actions is far greater with potential commercial churners as compared to financial churners. Evidence comes from a real-life field experiment.Customer Intelligence, analytical customer relationship management (aCRM), customer churn, attrition research, commercial churn, financial churn, credit risk, out-of-period validation

Proactive business intelligence to give best customer experience to valued social networks in telecoms

To compete, cellular network operators must be very responsive to their customer needs. One effective approach that could make a business more productive is anticipatory decisions while considering customer’s problem. Communication Service Providers must have greater insights into their own Call Details Record data in order to gauge and tune up the company’s performance. CSPs must promptly react to ever changing competitive environment by providing quick and personalized services. However, reacting quickly to changing customer expectations is not a simple task. In this paper, a method has been proposed in order to tackle this challenging problem. Proposed solution is using CDR data as the primary data source to detect valued social networks and monitored the QOS provided to the identified valued social network. Calls of valued customers will be prioritized by inserting priority tag in MSC. The final step of proposed technique deduces a proactive approach in order to retain the high revenue generating customers

A Fuzzy Rule-based Learning Algorithm for Customer Churn Prediction

16th Industrial Conference on Data Mining (ICDM 2016), New York, United States, 13-17 July 2016Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However most of rulebased learning algorithms designed with the assumption of well-balanced datasets, may provide unacceptable prediction results. This paper introduces a Fuzzy Association Rule-based Classification Learning Algorithm for customer churn prediction. The proposed algorithm adapts CAIM discretization algorithm to obtain fuzzy partitions, then searches a set of rules using an assessment method. The experiments were carried out to validate the proposed approach using the customer services dataset of Telecom. The experimental results show that the proposed approach can achieve acceptable prediction accuracy and efficient for churn prediction.European Commission - Seventh Framework Programme (FP7)Marie Curie Action