Churn Problem in Everyday Business

Churn Problem in Everyday Business

DOI: 10.4018/978-1-4666-6288-9.ch001
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This chapter is an introduction into customer relationship management, explaining the modern business environment and techniques to monitor it. As part of that process, churn management is introduced and explained across industries. Throughout the chapter, a wider churn perspective is explained together with several examples from real cases. As the chapter comes to its end, the business approach become more and more involved, and the reader starts to realize the importance of churn management and its complexity. At the same time, the idea of techniques and methodologies as to how it can be managed start to shape key book points.
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1.1 Introduction

While looking for more effective and profitable business, can we afford to lose some customers from time to time? Is it possible to create service (or product) that will be perfect match for every customer? Even to lose some customers over time period in highly competitive environment? Customer churn, also known as customer attrition, customer turnover, or customer defection is a business term used to describe loss of clients or customers. Usually for a business, churn is bad news although it can be good news as well, under specific circumstances. In this book we will focus on techniques used to understand, analyze and manage churn. Although mostly recognizable as negative in terms of business impact, this book will explain many useable and efficient ways how to manage churn and, in best case scenarios, turn it through deep understanding and quality management into additional value for company.

Many businesses with large customer bases, particularly subscriber-based businesses (like telecommunication companies, cable television companies but also banks and retail companies) monitor and manage their churn numbers very closely. The metric tracked is typically known as the „churn rate“ and is expressed as a percentage. Basic calculation to express churn rate is relatively straightforward: number of customers that defected divided total number of customers (Ants Analytics, 2013).

Customer attrition is an important issue for any company, and it is especially important in mature industries where the initial period of exponential growth has been left behind. Churn (or retention, if we look at it from the other side) is a one of the most important application of data mining. Industry uses term churn for example, in telephone industry to refer to all types of customer attrition whether voluntary or involuntary. Churn is a useful word because it is one syllable and easily used as both a noun and a verb (Berry & Linoff, 2004).

For introduction example, let us follow telco1 companies. Telecommunication industry is volatile and rapidly growing, in terms of the market dynamicity and competition. To be aligned with modern market and life needs, it creates new technologies and products on regular basis, which open a series of options and offers to customers. However, one crucial problem that commercial companies in general and telecommunication companies in specific suffer from is a loss of valuable customers. To predict that kind of changes, companies are investing into predictive and advanced analytics models like ones called customer-churn prediction models or widely applicable loyalty analysis models. Researches so far shows that customer who leaves a carrier in favor of competitor costs a carrier more than if they gained a new customer. It is similar in many industries. Therefore, customer-churn prediction can be marked as one of the most important problems that (telecom or other) companies face in general. To be able to manage this problem company needs to understand the behavior of customers, classify performance indicators and look for churn and non-churn customers so that the necessary decisions can be made before churn happens or while it is happening. In more words, the goal is to build up an adaptive and dynamic data-mining model in order to efficiently understand customer behavior and allow time to make the right decisions. By understanding complex environment in structured way company can be more efficient comparing to traditional available techniques, which are generally more expensive and time consuming. As will be shown, advanced analytic methods provide space for different approaches using different methods like Bayesian networks, association rules, decision trees and neural networks (Rashid, 2010) (see Figure 1).

Figure 1.

Dimensions of loyalty (Kumar, 2005)


At the same time company needs to identify and recognize, follow, understand and manage their most loyal followers. Most loyal, from the company perspective, can be also known as most valuable. Questions which pop-up:

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