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Modeling Customer Lifetime Value in the Telecom Industry

Flordahl, Peter and Friberg, Joakim (2013) MIO920
Production Management
Abstract
Background
The fierce competition in the telecom industry makes operators heavily invest in acquiring new customers. This is most often done with marketing campaigns and subsidies of handsets. But to be truly profitable, it is crucial not only to attract new customers, but also to make sure they retain with the company for as long time as possible. This turns the mobile operators’ attention to customer lifetime value (CLV). Knowing what drives CLV give ideas of what is best to invest in, and this information can be very valuable for Ericsson in their sales and relationship to the operators.
Purpose
The purpose is to develop a model to analyze what drives customer lifetime value of smartphone users. Furthermore, it will also be... (More)
Background
The fierce competition in the telecom industry makes operators heavily invest in acquiring new customers. This is most often done with marketing campaigns and subsidies of handsets. But to be truly profitable, it is crucial not only to attract new customers, but also to make sure they retain with the company for as long time as possible. This turns the mobile operators’ attention to customer lifetime value (CLV). Knowing what drives CLV give ideas of what is best to invest in, and this information can be very valuable for Ericsson in their sales and relationship to the operators.
Purpose
The purpose is to develop a model to analyze what drives customer lifetime value of smartphone users. Furthermore, it will also be investigated how changing these parameters affects the total CLV, in order to show how different investments increases or decreases the customer lifetime value.
Theoretical Framework
The theoretical framework builds on present CLV theory. Markov chain modeling is used to model the CLV, and ordered probit regression is applied to analyze the survey data.
Methodology
This thesis takes a quantitative approach to model the customer lifetime value. The data used to derive the drivers of CLV is compiled from smartphone user survey questionnaires completed by Ericsson’s Consumer Lab. The calculations are performed by simulating a large number of fictitious company-customer relationship processes in MATLAB.
5
Results
The main result is a model that describes the dynamic relationship between a customer’s preferences and the profit it generates during its lifetime. The model is then applied on six different markets across a number of segments to produce valuable information on how the CLV changes when customer satisfaction in different areas increase. (Less)
Please use this url to cite or link to this publication:
author
Flordahl, Peter and Friberg, Joakim
supervisor
organization
course
MIO920
year
type
M1 - University Diploma
subject
keywords
Customer Lifetime Value, CLV, Telecom, Churn, Retention, Ordered Probit Regression, Simulation
other publication id
13/5452
language
English
id
3917291
date added to LUP
2013-07-03 14:18:25
date last changed
2018-02-21 11:33:33
@misc{3917291,
  abstract     = {Background
The fierce competition in the telecom industry makes operators heavily invest in acquiring new customers. This is most often done with marketing campaigns and subsidies of handsets. But to be truly profitable, it is crucial not only to attract new customers, but also to make sure they retain with the company for as long time as possible. This turns the mobile operators’ attention to customer lifetime value (CLV). Knowing what drives CLV give ideas of what is best to invest in, and this information can be very valuable for Ericsson in their sales and relationship to the operators.
Purpose
The purpose is to develop a model to analyze what drives customer lifetime value of smartphone users. Furthermore, it will also be investigated how changing these parameters affects the total CLV, in order to show how different investments increases or decreases the customer lifetime value.
Theoretical Framework
The theoretical framework builds on present CLV theory. Markov chain modeling is used to model the CLV, and ordered probit regression is applied to analyze the survey data.
Methodology
This thesis takes a quantitative approach to model the customer lifetime value. The data used to derive the drivers of CLV is compiled from smartphone user survey questionnaires completed by Ericsson’s Consumer Lab. The calculations are performed by simulating a large number of fictitious company-customer relationship processes in MATLAB.
5
Results
The main result is a model that describes the dynamic relationship between a customer’s preferences and the profit it generates during its lifetime. The model is then applied on six different markets across a number of segments to produce valuable information on how the CLV changes when customer satisfaction in different areas increase.},
  author       = {Flordahl, Peter and Friberg, Joakim},
  keyword      = {Customer Lifetime Value,CLV,Telecom,Churn,Retention,Ordered Probit Regression,Simulation},
  language     = {eng},
  note         = {Student Paper},
  title        = {Modeling Customer Lifetime Value in the Telecom Industry},
  year         = {2013},
}