Predicting Customer Churn Using CLV in Insurance Industry
نویسندگان
چکیده مقاله:
Today, increased level of customer awareness caused themto access to the other suppliers easily and they can get their servicesfrom the competitors with similar or even better quality and same price.Therefore, focusing on customers and preventing them to leave, has beenthe most important strategy for any company. Researches have shownthat retaining former customers is cheaper than attracting new ones. Inthe proposed model in this article we first identified important factorscausing customers in insurance industry, to have a specific behavior byusing a k-means clustering algorithm, and then we tried to predict thefuture behavior of them by a logistic regression. Our model accuracy is98%.
منابع مشابه
Predicting customer potential value an application in the insurance industry
For effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article we introduce a model for predicting the potential value of a current customer. Furthermore, we discuss and apply different modeling strat...
متن کاملPredicting credit card customer churn in banks using data mining
In this paper, we solve the customer credit card churn prediction via data mining. We developed an ensemble system incorporating majority voting and involving Multilayer Perceptron (MLP), Logistic Regression (LR), decision trees (J48), Random Forest (RF), Radial Basis Function (RBF) network and Support Vector Machine (SVM) as the constituents. The dataset was taken from the Business Intelligenc...
متن کاملNeighborhood Cleaning Rules and Particle Swarm Optimization for Predicting Customer Churn Behavior in Telecom Industry
Churn prediction is an important task for Customer Relationship Management (CRM) in telecommunication companies. Accurate churn prediction helps CRM in planning effective strategies to retain their valuable customers. However, churn prediction is a complex and challenging task. In this paper, a hybrid churn prediction model is proposed based on combining two approaches; Neighborhood Cleaning Ru...
متن کاملAnalysing Customer Churn in Insurance Data - A Case Study
Designing a new application of knowledge discovery is a very tedious task. The success is determined to a great extent by an adequate example representation. The transformation of given data to the example representation is a matter of feature generation and selection. The search for an appropriate approach is difficult. In particular, if time data are involved, there exist a large variety of h...
متن کاملPredicting Customer Churn: Extreme Gradient Boosting with Temporal Data
Accurately predicting customer churn using large scale time-series data is a common problem facing many business domains. The creation of model features across various time windows for training and testing can be particularly challenging due to temporal issues common to time-series data. In this paper, we will explore the application of extreme gradient boosting (XGBoost) on a customer dataset ...
متن کاملAnalysis of Customer Churn prediction in Logistic Industry using Machine Learning
Customer churn prediction in logistics industry is one of the most prominent research topics in recent years. It consists of detecting customers who are likely to cancel a subscription to a service. Recently, logistics market has changed from a rapidly growing market into a state of saturation and fierce competition. The focus of the logistic companies has therefore shifted from building a larg...
متن کاملمنابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ذخیره در منابع من قبلا به منابع من ذحیره شده{@ msg_add @}
عنوان ژورنال
دوره 2 شماره 1
صفحات 39- 49
تاریخ انتشار 2015-03-01
با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.
میزبانی شده توسط پلتفرم ابری doprax.com
copyright © 2015-2023