Clement Kirui

Bio: Clement Kirui is an academic researcher. The author has contributed to research in topics: Bayesian network & Probabilistic logic. The author has an hindex of 1, co-authored 1 publications receiving 66 citations.

Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining

Abstract: Customer churn in the mobile telephony industry is a continuous problem owing to stiff competition, new technologies, low switching costs, deregulation by governments, among other factors. To address this issue, players in this industry must develop precise and reliable predictive models to identify the possible churners beforehand and then enlist them to intervention programs in a bid to retain as many customers as possible. This paper proposes a new set of features with the aim of improving the recognition rates of possible churners. The features are derived from call details and customer profiles and categorized as contract-related, call pattern description, and call pattern changes description features. The features are evaluated using two probabilistic data mining algorithms Naive Bayes and Bayesian Network, and their results compared to those obtained from using C4.5 decision tree, a widely used algorithm in many classification and prediction tasks. Experimental results show improved prediction rates for all the models used.

Machine-Learning Techniques for Customer Retention: A Comparative Study

Abstract: Nowadays, customers have become more interested in the quality of service (QoS) that organizations can provide them. Services provided by different vendors are not highly distinguished which increases competition between organizations to maintain and increase their QoS. Customer Relationship Management systems are used to enable organizations to acquire new customers, establish a continuous relationship with them and increase customer retention for more profitability. CRM systems use machine-learning models to analyze customers’ personal and behavioral data to give organization a competitive advantage by increasing customer retention rate. Those models can predict customers who are expected to churn and reasons of churn. Predictions are used to design targeted marketing plans and service offers. This paper tries to compare and analyze the performance of different machine-learning techniques that are used for churn prediction problem. Ten analytical techniques that belong to different categories of learning are chosen for this study. The chosen techniques include Discriminant Analysis, Decision Trees (CART), instance-based learning (k-nearest neighbors), Support Vector Machines, Logistic Regression, ensemble–based learning techniques (Random Forest, Ada Boosting trees and Stochastic Gradient Boosting), Naive Bayesian, and Multi-layer perceptron. Models were applied on a dataset of telecommunication that contains 3333 records. Results show that both random forest and ADA boost outperform all other techniques with almost the same accuracy 96%. Both Multi-layer perceptron and Support vector machine can be recommended as well with 94% accuracy. Decision tree achieved 90%, naive Bayesian 88% and finally logistic regression and Linear Discriminant Analysis (LDA) with accuracy 86.7%.