Business Intelligence And Process Modeling

Read Complete Research Material

BUSINESS INTELLIGENCE AND PROCESS MODELING

Business Intelligence and Process Modeling

Abstract

Conventional statistical methods (e.g. logistics regression, decision tree, and etc.) are very successful in predicting customer churn. However, these methods could hardly predict when customers will churn, or how long the customers will stay with. The goal of this study is to apply survival analysis techniques to predict customer churn by using data from a telecommunications company. This study will help telecommunications companies understand customer churn risk and customer churn hazard in a timing manner by predicting which customer will churn and when they will churn. The findings from this study are helpful for telecommunications companies to optimize their customer retention and/or treatment resources in their churn reduction efforts.

Business Intelligence and Process Modeling

Introduction

In the telecommunication industry, customers are able to choose among multiple service providers and actively exercise their rights of switching from one service provider to another. In this fiercely competitive market, customers demand tailored products and better services at less prices, while service providers constantly focus on acquisitions as their business goals. Given the fact that the telecommunications industry experiences an average of 30-35 percent annual churn rate and it costs 5-10 times more to recruit a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business pain. Many telecommunications companies deploy retention strategies in synchronizing programs and processes to keep customers longer by providing them with tailored products and services. With retention strategies in place, many companies start to include churn reduction as one of their business goals. In order to support telecommunications companies manage churn reduction, not only do we need to predict which customers are at high risk of churn, but also we need to know how soon these high-risk customers will churn. Therefore the telecommunications companies can optimize their marketing intervention resources to prevent as many customers as possible from churning. In other words, if the telecommunications companies know which customers are at high risk of churn and when they will churn, they are able to design customized customer communication and treatment programs in a timely efficient manner. Conventional statistical methods (e.g. logistics regression, decision tree, and etc.) are very successful in predicting customer churn. These methods could hardly predict when customers will churn, or how long the customers will stay with. However, survival analysis was, at the very beginning, designed to handle survival data, and therefore is an efficient and powerful tool to predict customer churn.

Objectives

The objectives of this study are in two folds. The first objective is to estimate customer survival function and customer hazard function to gain knowledge of customer churn over the time of customer tenure. The second objective is to demonstrate how survival analysis techniques are used to identify the customers who are at high risk of churn and when they will churn.

Definitions and Exclusions

This section clarifies some of the important concepts and exclusions used in this ...
Related Ads