Customer Lifetime Value in the Mobile Phone Market in Iceland

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Customer Lifetime Value in the Mobile Phone

Market in Iceland

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Customer Lifetime Value in the Mobile Phone

Market in Iceland

Author

Anna Guðrún Birgisdóttir Hverfisgata 23 220 Hafnarfjörður, Iceland Telephone: 00354 6617171 E-mail: anna_gudrun@hotmail.com Student number: s1660411 University of Groningen

Faculty of Economics and Business Master Thesis Business Administration Specialization Marketing Research First supervisor: Dr. M.C. Non Second supervisor: Dr. H. Risselada

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Management Summary

Customer lifetime value is becoming of high interest to many businesses, especially those who provide any kind of services. Customer lifetime value gives the business an idea about how valuable a customer is to the business and enables it to target the most valuable ones in order to retain them. In the resent years, the mobile telephone market in Iceland has become increasingly competitive as new telecommunications companies entered the market. This has resulted in consumers who search for better prices and service or products. The consequences are that customer churn has increased and it is necessary for mobile phone providers to be able to predict churn accurately as this in turn affects customer lifetime value which decreases as the probability of churn increases. Customer lifetime value can then be used to segment the customer database which makes it easier to custom-make services and products which suit the customers’ needs. Two classification methods (1) logistic regression and (2) decision tree were used on two separate sets of data where customers were labeled as churn and non-churn in order to make a model that predicts churn. The data sets consisted of post-paid customers on one hand and pre-paid customers on the other. This model was then used to calculate customer lifetime values of all customers at an Icelandic telecom which then gave some insight into which customers are most valuable and what characterizes them. The customers were then segmented based on their customer lifetime value.

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Preface

After a long journey working on this research I want to thank those who have in any means supported me or assisted me on the way.

I first would like to express my gratitude to my supervisor Dr. Marielle C. Non at the University of Groningen in the Netherlands. She has been very patient and extremely helpful during this time as it is not easy conducting this type of work mainly through emails. Her advice and comments have been valuable and helped me to see this through. My gratitude to Dr. Hans Risselada for his comments on improvements.

I want to thank my contacts at Telecom X for their support and interest in this research as well as patience. I also want to thank them for the opportunity to write this thesis in cooperation with Telecom X and for providing me with the necessary data and information to be able to work on this analysis.

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List of Figures

Figure 2-1: Conceptual model of the Customer Lifetime Value ... 17

Figure 3-1: An example of a decision tree for churn...23

Figure 3-2: An example of a ROC curve... 26

Figure 4-1: The time window of the analysis...30

Figure 5-1: ROC curve for the logistic regression in the post-paid training sample………..46

Figure 5-2: ROC curve for the decision tree in the post-paid training sample ... 52

Figure 5-3: ROC curve for the logistic regression for the pre-paid training sample ... 63

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List of Tables

Table 2-1: Market share in the mobile phone market in Iceland ... 4

Table 2-2: Market share in the post- and pre-paid mobile phone markets in Iceland in 2008 and 2012 ... 5

Table 3-1: Confusion matrix ... 24

Table 4-1: Distribution of the data used in the training and testing sets ... 28

Table 5-1: Marital status of customers in the post-paid training sample ... 33

Table 5-2: Family size of customers in the post-paid training sample ... 34

Table 5-3: Residence of customers in the post-paid training sample……….………..36

Table 5-4: Crosstable of Status*Gender in the post-paid training sample ... 35

Table 5-5: Cronbach’s alpha for the components for the post-paid training sample ... 39

Table 5-6: Comparison of PCA and PA eigenvalues in the post-paid training sample ... 41

Table 5-7: Results from the logistic regression for the post-paid training sample ... 42

Table 5-8: Classification Table for the logistic regression for the post-paid training sample ... 46

Table 5-9: Classification table for the logistic regression for the post-paid testing sample ... 48

Table 5-10: Risk estimates of different growing methods for the post-paid training sample ... 50

Table 5-11: Classification table for unpruned decision tree in the post-paid training sample ... 50

Table 5-12: Classification table for pruned decision tree in the post-paid training sample ... 50

Table 5-13: Classification table for decision tree in the post-paid testing sample ... 52

Table 5-14: Marital status of customers in the pre-paid training sample ... 54

Table 5-15: Family size of customers in the pre-paid training sample ... 55

Table 5-16: Residence of customers in the pre-paid training sample………..………...58

Table 5-17: Crosstable of Status*Gender for the pre-paid training sample ... 55

Table 5-18: Cronbach’s alpha for the components for the pre-paid training sample ... 58

Table 5-19: Comparison of PCA and PA eigenvalues for the pre-paid training sample... 59

Table 5-20: Results from the logistic regression in the pre-paid training sample ... 60

Table 5-21: Classification Table for the logistic regression for the pre-paid training sample ... 62

Table 5-22: Classification table for the logistic regression for the pre-paid testing sample ... 64

Table 5-23: Risk estimates of different growing methods for the pre-paid training sample ... 65

Table 5-24: Classification table for the unpruned decision tree for pre-paid training sample ... 65

Table 5-25: Classification table for the pruned decision tree for pre-paid training sample ... 66

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1. Introduction

Economies today are becoming primarily service-based and companies get a large part of their revenue from creating and sustaining long-term relationships with their customers (Kumar and Shah, 2009). Most companies are concerned with the revenue that their customers generate, as well as the associated cost of acquiring and maintaining these customers. One of the biggest benefits of retaining an existing customer is that the profits that he generates over time tend to accelerate. One reason for this is that revenues from customers usually grow over time. They often start using a new product or service slowly in the beginning but as they become more accustomed to it, they use it more. Another reason is that it is more efficient to serve old, existing customers which can reduce costs. Customers’ familiarity with the company’s products and services makes them less reliant on employees for assistance. Existing customers who are satisfied also act as referrals as they recommend the company to others. The final reason is that in some industries, existing customers even pay higher prices than new ones, as the new ones are often offered special trial discounts when they start the relationship with a company. One major concern is to ascertain which of the customers will be most profitable. Upon such discovery companies may aspire to retain these customers for some time as repeat purchases by established customers normally require less marketing effort, as much as 90% less, compared to new customers who are purchasing for the first time (Berger and Nasr, 1998; Dahr and Glazer, 2003). Companies should be aware of their customers worth, attempt to understand their lifetime value and in turn apply it as a guiding concept for marketing decisions and in developing marketing strategies.

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the CLV metric so appealing is its capacity to acquire, grow, and retain customers who are considered profitable to the company, and to foster profitable CRM through proper marketing interventions. CLV has therefore become known as a key customer value metric that is necessary to manage customers’ profitability and by maximizing CLV, and therefore customer equity (the sum of the lifetime values of the company’s customers), companies can increase their profits (Abe, 2009; Borle et al., 2008; Gupta et al., 2006; Kumar and Shah, 2009; Venkatesan and Kumar, 2004).

Companies today have vast opportunities to interact directly with customers by collecting and mining information and subsequently tailoring their products and offerings accordingly. Customers even expect to interact closely with the respective companies and have some influence on the creation of the products and services which they purchase and use. Companies wishing to stay competitive have therefore transcended from simply marketing products to the mass, towards cultivating and serving their customers on a more customized basis, resulting in maximization of customer lifetime value. Communication consequently becomes reciprocal and is individualized or tightly targeted at narrow segments. By promoting the company’s products or services to the customer in this manner, the company can build long-term relationships with its customers (Rust et al., 2010). Customer relationships evolve over time, as do the customer’s needs and wants. Companies can utilize the information they gather and any changes therein, by providing customers with updated offers on different products or services. The changes can for example be tracked with demographic data and customer purchase patterns (Rust et al., 2010).

Use of interactive and database technology allows companies to accumulate a wide range of data about individual customers’ needs and preferences. This data can then be used to equally customize products and services. The more companies learn about their customers’ needs, the better they can respond to their requirements and offer exactly what customers want, when they want it. This gives a company a great competitive advantage (Pine II et al., 1995).

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or not focus on in particular. Segmenting the customer base in this manner makes it easier to find suitable responses, for example to profitable relationships that should be invested in to win back or grow, or in turn to manage costs to make segments that are lower-margin worthwhile or even to terminate customer relationships in unattractive segments (Niraj et al., 2001; Rigby et al., 2002). Companies can use predictive modeling to identify the customers who are most profitable, as well as those customers with the greatest profit potential and those likeliest to cancel their accounts (Davenport, 2006). By using CLV, companies can develop their long-term relationships with customers and define their strategies better.

In this thesis, CLV for an Icelandic telecom will be calculated and an attempt made to shed light on the factors that influence CLV. In the next section, background on the telecommunications industry and the telecom will be given. In the subsequent section thereafter the research questions are presented.

1.1 Telecommunications Industry

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CLV. The mobile service providers should be able to predict the churn rate for individual customers to see which subscribers are at risk of changing services and to calculate their customer lifetime values to sort out the most valuable ones. This information can then be used to improve customer segmentation and implement them in making strategies directed at customers (Kim et al., 2004; Wei and Chiu, 2002).

1.1.1 Telecommunications Industry in Iceland

Companies in telecommunications in Europe have undergone extensive transformations since the 1980s, primarily due to the deregulation and liberalization of the European telecommunications market. They have gone from being public monopolies, owned and governed by the state, to being privatized and market driven (Eliassen and From, 2007). This liberalization began somewhat later in Iceland, in the late 1990’s to early 2000. In 2011 five telecoms provided mobile phone services in Iceland, both fixed (post-paid) and pre-paid subscriptions. They are Siminn hf., Fjarskipti ehf. (Vodafone), Nova ehf., IP-fjarskipti ehf. (Tal) and Alterna Tel. ehf. Over all, at the end of 2010 there were 375430 mobile subscriptions in total, which is an increase of more than 15% in subscriptions since 2008. Table 2-1 shows the development of the market share of each of the telecommunications companies in the mobile phone market in Iceland in 2008, 2010 and 2012.

Table 2-1: Market share in the mobile phone market in Iceland

Telecommunications company Market share

2008 2010 2012 Siminn 51.6% 41.8% 37.4% Vodafone 34.9% 30.9% 28.9% Nova 8.2% 22% 28.3% Tal 5.4% 4.5% 5.0% Alterna ... 0.8% 0.4%

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Nova had more than doubled its market share. Tal also saw some increase in market share but Vodafone a decrease like Siminn.

Table 2-2: Market share in the post- and pre-paid mobile phone markets in Iceland in 2008 and 2012 Telecommunications

company

Market share in post-paid subscriptions 2008 2012 Siminn 54.0% 48.3% Vodafone 37,1% 33.7% Nova 4.8% 11.6% Tal 4.0% 5.6% Alterna ... 0.8%

In the post-paid subscriptions market, Siminn and Vodafone have strong market positions and can be looked at as market leaders. Nevertheless, both telecoms have, as stated above, lost some of its market share to Nova and Tal. In the pre-paid mobile phone market (see Table 2-2, the table on the right), Siminn no longer has the market leading position. Nova is now the market leader with 49.3% from only 12.5% in 2008. Siminn has 23.2% market share, which is down from 48.4% in 2008. The market share for both Vodafone and Tal has also decreased since 2008 (Post- and Telecom Administration, 2010 and 2012). Here Siminn has lost its market leading position to Nova and the competition seems to be strong between the three largest telecoms, Siminn, Vodafone, which used to be second, and Nova. The aforementioned shows that the competition in the mobile phone market has changed rapidly over the resent years, as it has gone from being an almost duopoly with two players to a more competitive environment. In the beginning of 2011, a new telecommunications company, Hringdu, was established, making the competition even fiercer. Telecom X is for example prohibited from bundling its products/services meaning it cannot offer more than one product or service together as one combined product or offer a discount on one product if another one is bought simultaneously. There are further restrictions on offering valuable customers special offers or advertising special packages of products or services, making it more difficult for the telecom to market its products and grow its business. Another fact that sets the telecommunications industry in Iceland apart from other neighboring countries is that in Iceland companies do not apply binding contracts. This is not a consequence of legal requirements, but rather an example of development spurred by the strong competition within the local market. The outcome is that customers do not have to sign a contract binding them with one telecom for any given time period. Customers can therefore switch telecom providers whenever they choose, perhaps making them even less loyal, as those who seek good deals will have a higher probability of churning. New customers tend to be more prone

Telecommunications company

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to be lost within the first few years. The customers who churn accounts every few years are more likely to be younger, less-established households, with fewer relationships with the company and fewer total products. This is in line with current developments at the telecom.

1.1.2 The Icelandic Telecommunications Company

This research project is conducted for the Telecom X. It offers a full range of telecommunication services, including telephone, mobile phone, television and Internet subscriptions.

The size of the buyers’ market in Iceland is small in general, with just over 318000 people living in Iceland (Statistics Iceland, 2011) making competition in any industry fierce and difficult. For this reason, companies have to both hold on to their existing customers and try to attract new ones. In Iceland five telecoms provide mobile phone service and there are 375430 mobile subscriptions (Post- and Telecom Administration, 2010). This is a similar number of telecoms compared to the other Nordic countries where the population on the other hand ranges from 4-10 million inhabitants per respective country. In an attempt to acquire new customers, telecoms in Iceland have contacted customers directly who have a subscription with a competitor and offered them deals in order to entice them to switch. This method has in turn resulted in disloyal customers, who seem to leave after a short period of time, following cheaper offers from other competitors. However this method of marketing is less practiced nowadays as it has been shown to be ineffective. Advertising campaigns are also frequent, especially in the market for young customers.

In late 2009 the telecom introduced a pre-paid card service especially aimed at younger people. It had seen a decrease in market share in the age group from 16-34, since the beginning of 2009 most likely because of market actions of other competitors like Nova and Tal. This age group is amongst the most valuable customers, since they both talk more and send text messages more frequently compared to older age groups.

1.2 Research Questions

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In this research, the aim is to answer the following questions:

Marketing research problem

Is Customer Lifetime Value useful for a mobile phone provider?

The Research Questions

1. Which factors have an effect on the customer lifetime value of mobile phone customers?

2. Which factors have an effect on the churn probability of mobile phone customers?

1.3 Structure of the Thesis

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2. Theoretical Framework

In the first section of this chapter is a review of the literature related to the concepts of customer lifetime value and churn. In addition, hypotheses will be formulated which are then used to build the conceptual framework.

2.1 Customer Lifetime Value

Marketing is more or less about attracting customers who are profitable and keeping them. It is not advisable for a company to try to pursue and satisfy every single customer, instead it should concentrate on those customers who generate revenue for the company and are likely to stay for a while. What makes a customer profitable is the amount of revenues that come from a person, household or a company that exceed the company’s customer related costs of attracting, selling and serving a customer. The excess revenues are called customer lifetime value (Berger and Nasr, 1998). Customer lifetime value has been defined in several researches. It is the present value of all future profits that are obtained from a customer over his life of relationship with a company. CLV can be generally defined as the total net profit a company can expect from a customer over their lifecycle (Gupta et al., 2006; Gupta and Lehmann, 2003; Kumar and Shah, 2009; Niraj et al., 2001; Novo, 2004). Long-lifetime customers have for a while been considered to be more profitable to a company. This approach is customer-centric and treats customers as assets and focuses both on acquiring as well as retaining customers. The customers who are retained can then form a basis of sustained competitive advantage (Jain and Singh, 2002). Companies’ actions in marketing have an influence on the behavior of customers, like acquisition, retention and cross-selling. This then affects the CLV of customers or their profitability to a company (Gupta et al., 2006).

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relationship with some customers who turn out to be unprofitable or allocate different resources to different groups of customers depending on their profitability. This is impossible with financial metrics like aggregate profit and stock price of a company. Even if these measures are practical, they have limited diagnostic capability. CLV is on the other hand a disaggregate metric and can therefore be used for the purpose of identifying profitable customers and allocation of resources (Gupta et al., 2006; Kumar and Reinartz 2006).

Today the focus of marketing has gone from being product driven to being customer driven (Rust et al., 2000). Companies increasingly get their revenue from creating and nourishing long-term relationships with their customers, especially as modern economies become largely service-based. Marketing should therefore work on achieving maximum customer lifetime value and customer equity, which is the sum of the lifetime values of the company’s customers, minus their acquisition and retention costs (Gupta et al., 2006; Hanssens et al, 2008). CLV models are useful for market segmentation and the allocation of marketing resources for acquisition, retention and cross-selling. Not all customers have the same value to a company and this demonstrates the need to terminate invaluable customers or allocate resources differently. CLV of current and future customers is also a good proxy of overall firm value (Gupta et al., 2006; Hwang et al., 2004). By understanding the factors that have an influence on the lifetime value of customers, companies can use that knowledge when developing strategies such as loyalty programs and cross-selling (Kumar et al., 2004).

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customer defection and longer customer relationship may entail that a customer will have a higher CLV due to increased cumulative profits (Bolton, 1998; Borle et al., 2008; Reinartz and Kumar, 2000, 2003). Other concepts that are used to categorize customers are

“lost-for-good” and “always-a-share”. In the former case, a customer is considered to be loyal and

committed to one company and is similar as in contractual circumstances. If lost customers return to a company they are treated as new ones. A customer retention model is used to calculate CLV where a retention rate is estimated based on historical data. The retention rate (also the same as 1-churn rate) is the probability that a customer will continue the relationship with a company. In the case of “always-a-share”, customers can easily switch between companies and do not give any one company all of their business. This is equivalent to non-contractual circumstances. A customer migration model is used in these situations to calculate CLV where the recency of last purchase is applied in order to predict the probability that a customer will make a repeat purchase in a period (Berger, and Nasr, 1998; Rust et al., 2004). In the case of this telecom, the customer relationships are of a contractual nature and therefore can be looked as “lost-for-good” if they leave. However, the contracts do not define length since customers of Icelandic telecoms are not bound for a specific time as is the custom in many other countries. They can therefore terminate the relationship whenever they want.

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between those who are highly profitable and those who are less profitable or not at all (Lu, 2003).

A company can build a customer database if it wants to focus on establishing long-term relationships with its customers. With the database, the company can identify its customers, track their transactions and even predict changes in their purchase patterns at an individual level. The information in the databases about customer‘s purchase patterns can also be analyzed to target and retain the right customers and distinguish between active and defected customers (Batislam et al., 2007).

2.1.1 CLV Model

The CLV model consists of three elements. These are a discount rate, customer churn and margin. These elements will be discussed later in the chapter but first, the CLV model used in this research is shown and explained.

One of the difficulties regarding the prediction of CLV is that there are many models and approaches to apply and they depend also on the industry within which the company operates. The life circumstances of customers also change along with their preferences which can then have an effect on purchasing behavior over different periods. Therefore the length of the period under consideration has to be decided on (Ryals, 2002). Unlike the discounted cash flow approach which is used in finance, CLV can be estimated on the individual customer or segment level. The strength of the telecom’s dataset is that longitudinal transaction data is available for each customer of this company. This makes it possible to calculate CLV at the individual customer level and uncover the customer-centric measures that drive CLV (Kumar and Shah, 2009).

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(2-1)

where,

m margin (ARPU)

d the discount rate (WACC) r the retention rate or 1-churn

2.1.2 Margin

Margin often refers to the net profit of a company (revenue minus costs) divided by revenue. However, in this case the costs are unknown so the metric used in this research will be Average Revenue per User (ARPU). It represents the average revenue a telecom receives divided by the number of subscribers per month. It is frequently used by industry observers and regulators to evaluate the performance of mobile telephone market (McCloughan and Lyons, 2006).

2.1.3 Discount Rate

As with the calculation of CLV, there are different ways of calculating the discount rate. For this research, the most common method was chosen.

Weighted-Average Cost of Capital

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the cost of each capital component by its proportional weight and then summing (Brealey et al., 2004). The equation is as follows:

(2-2)

where,

D market value of the company’s debt E market value of the company’s equity

V E+D

D/V percentage of financing that is debt E/V percentage of financing that is equity Rd cost of debt

Re cost of equity Tc corporate tax rate

2.1.4 Retention rate (1-Churn)

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In the telecommunications industry, churn refers to subscribers moving from one company to another. Subscribers tend to look for better rates or services so many of them churn recurrently, going between providers (Wei and Chiu, 2002). Customer churn is directly incorporated in how long a customer stays with a company and has an influence on the creation of future profit for a company and therefore also in the customer’s lifetime value to that company. It is therefore very important to take into account in the CLV model (Neslin et al., 2006; Hwang et al., 2004). Wheaton (2000) wrote in his article about CLV for bank customers that it is more profitable for a company to retain a mature, high-balance account than to acquire a new account that is lower-balance. The new ones tend to be more prone to be lost in the first few years. The customers who churn accounts every few years are more likely to be younger, less-established households, and buy fewer products from the company. This is in line to what is happening at the telecom. Those customers who have subscribed in the last few years are more likely to churn and go elsewhere.

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2.1.4.1 Customer Churn Determinants

As with the CLV, there are several factors that have an influence on the churn rate. These factors will now be discussed and hypotheses formulated. The hypotheses are stated for post-paid and pre-post-paid customers separately as first of all, there are different features for either type of subscription. There is for example information on the number of products or services bought by post-paid customers and the amount and frequency of refill for the pre-paid customers. Another reason is that, as shown in section 1.1.1, there has been much more churn among pre-paid customers at the telecom as its market share has decreased significantly in the last few years. Therefore, pre-paid customers will probably have higher predicted probability of churn which then leads to lower CLV. Pre-paid customers most likely have lower margin or ARPU as one can imagine they use their phone as little as possible to save their pre-paid credit or have friends within the same network which they can call for free.

Customer Satisfaction, loyalty and relationship length

Whether customers are satisfied with a company or not hinges on how they evaluate the overall experience of their purchase and consumption and also on how the customers perceive the quality of the services. It has become known, along with loyalty, as a strong predictor of customer churn (Eshghi et al., 2007; Seo et al., 2008). Satisfaction has been shown to be a strong predictor of loyalty, especially in the service sector, including wireless service providers (Gerpott et al., 2001; Kim and Yoon, 2004). This emphasizes the significance of both customer satisfaction and loyalty to companies’ survival and growth in the long-term (Edvardsson, et al., 2000; Eshghi et al., 2007). Satisfaction of mobile phone customers can be related to several factors, one of which is the length of the relationship between the customer and the service provider. The longer the duration of the relationship, the more experience and knowledge the customer has about the service provider. This means higher switching costs because if customers switch service provider, they have to give up their familiarity with the provider’s features and have to adapt to different features with the new provider (Seo et al., 2008). Longer customer relationships also indicate greater customer satisfaction (Reinartz and Kumar, 2003). As customers get accustomed to the service offered by the provider and know what they can expect, they get more satisfied than they would be with an unfamiliar provider in a new relationship (Bolton, 1998). Therefore, the following hypothesis is concluded:

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16 Level of Service Usage

Monthly charge, unpaid balances, number of calls, and minutes of monthly use are some of the service usage factors that have been used in previous studies (Keramati and Ardabili, 2011). These factors will be used in this research along with number of text messages sent as a measure of the level of usage by each customer. Ahn et al. (2006) showed that usage is positively related to churn, meaning that heavy users are more likely to churn. Therefore the following hypothesis is stated related to the level of usage:

H2: Level of usage has a positive effect on (a) post-paid and (b) pre-paid customer churn probability.

Customer Demographics

The customer demographic variables taken into account are age, gender, marital status, and geographic area of residence. It is not quite clear how these demographics are related to customer churn probability. As mentioned earlier, Wheaton (2000) suggested that younger customers are more likely to churn than older ones. At the telecom, younger customers might be following either their friends who move to another telecom or they are less loyal and tend to take lower offers when they can or follow new trends. A study by Seo et al. (2008) showed that older customers are more likely to stay with the same provider so the following hypothesis is stated:

H3: Age has a negative effect on (a) post-paid and (b) pre-paid customer churn probability.

2.2 Segmentation

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When the CLV has been calculated for the customers, companies can aggregate the customers to almost any number of discrete segments which can then be used for example to develop acquisition or retention strategies that are relevant and cost effective. Companies that have a large number of customers with small sales to each customer could benefit from models that help segmenting the customer base based on customer lifetime (Jain and Singh, 2002; Kumar and Shah, 2009). Segments with customers who have medium but stable profitability could add a higher potential value to the company than customers who are highly profitable but have a high risk of churning in the future.

Marketers are interested in the differences between consumers, which can vary considerably. These differences can be based on, amongst other factors, geography, demographics, personality, lifestyle, psychographics, behavior, decision-making processes, purchasing approaches and situation factors. The fact that these differences exist makes it important for a company to develop market segmentation strategies as it is believed to be more profitable to treat specific types of customers in differing ways rather than treating them all the same. The customers with a mobile phone subscription at the telecom will be segmented by separating them in ten deciles based on their individual CLV. The 1st decile includes the top 10% most valuable customers at the telecom according to their CLV and the 10th decile includes the 10% of the least valuable customers.

2.3 Conceptual Model

The conceptual model (see Figure 2-1) is built on the hypotheses in the previous section.

H1 H2 H3

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The conceptual model shows how customer satisfaction, loyalty and length of relationship, level of service usage and customer age affect churn. The margin (ARPU), discount rate (WACC) and churn rate are then used to calculate the customer lifetime value for the telecom’s customers, which in turn can then be used to segment the customer database.

2.4 Summary

This chapter describes the situation in the telecommunications industry in Iceland and the harsh competition in this industry with the arrival of new competitors. The main concept of the thesis, Customer Lifetime Value, is also covered in this chapter. This concept has been the focus of many companies in the service industry all over the world and is getting increasing attention. Companies seek to find out which of their customers have the most value for them and can then use that information to custom their product selection to the customers’ wants and needs or to retain those customers which are in most danger of churning.

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3. Methodology

3.1 Research Design

The research design and related important issues are discussed in this chapter. This research is quantitative as hypotheses formulated in the previous chapter will be tested with numerical data from a customer database owned by the telecom. The sample is described shortly along with the variables in the analysis. Section 3.4 describes the plan of the analysis, where the classification methods used for the churn analysis are explained.

3.2 Sample

The objective was to attain a sample that consists of mobile phone customers at the telecom that is heterogeneous in terms of gender and age. There are two datasets constructed for the quantitative analysis, one consisted of just over 33000 randomly chosen customers with a post-paid mobile phone subscription (subscription paid at the end of the month) at the telecom and the other consisted of around 22000 customers with a pre-paid mobile phone subscription (where customers have to buy recharges when the previous runs out).

The data used for this research is panel data containing usage histories of mobile phone subscriptions. The datasets were based on the customer database and call log provided by the telecom and are monthly aggregated. The sample data set is divided into two parts, training, and validation or testing sets, before executing the analysis. The models are first developed on the training set and then the probability models are validated by using the equation on the testing set. For the post-paid sample, a training sample of 4379 customers was obtained and a testing sample of 28737 customers. For the pre-paid sample, a training sample of 5995 customers was obtained and a testing sample of 15906 customers. The samples are given in more details in Section 4.1.

3.3 Variables

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five categories, customer demographics, billing data, refill history (applies to pre-paid customers only), calling pattern, and call detail records billed (dcr billed). A list of these variables can be seen in Table I-1 in Appendix I. The data did not include any previous targeted marketing efforts or information about competition efforts. Various demographic variables will be used as control variables in the analysis to see whether they have an effect on churn or not. These variables include gender, marital status, family size and rate plan among others. This is discussed further in Section 4.3.

3.4 Plan of Analysis

As discussed in the previous chapter, the individual CLV model consists of three elements. These are the discount rate, the margin/profit and the churn probabilities. The methods used to calculate these elements will be discussed in the following sections. The margin (ARPU) is explained first, then the discount rate. The churn model is discussed last and the two classification methods used to predict churn. The method for calculating the CLV is discussed shortly and finally the method for segmenting the mobile phone customer base. The analyses of data in this research were processed using the Statistical Package for the Social Science (SPSS 19).

The mobile telecommunications market is divided into business and residential customers. For this research, the business customers are excluded given that they primarily use mobile services to earn income and they usually do not decide themselves whether to sign or extend a subscription contract. Since there is much less available information about pre-paid customers than post-pre-paid customers, there will be separate analyses for these two groups. Pre-paid customers are not required to give up their name or any other personal information so usually the available information is restricted to customer behavior like mobile phone usage.

3.4.1 Average Revenue per User (ARPU)

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The most recent figure for the weighted average cost of capital (WACC) at Telecom X will be used for the calculation of CLV.

3.4.3 Churn Analysis

Two methods will be used to predict customer churn, logistic regression and classification trees. These methods have both been widely studied and have good predictive performance (Neslin et al., 2006; Risselada et al., 2010).

Logistic Regression

Binomial logistic regression was conducted to test the hypotheses formed in chapter 2. This type of regression has been broadly used and examined in predictive data mining to predict customer churn in various trades like retail industry, financial services and telecommunications (Samimi and Aghaie, 2011). This method is chosen since the target variable, customer churn, is not continuous but discrete or categorical (churn or not churn). The effect of direct factors (i.e., subscription length, amount of charge, number of calls) on customer churn can be examined with this method. The customers who are going to churn can be discovered with the logistic regression and also what the drivers of churn are. The model was estimated using a fixed set of variables from the dataset as described in section 3.3 above.

The logistic regression is conducted to examine the relationship between the customer churn which is entered into the model as the dependent variable and the other factors (including subscription length, amount of charge and number of calls) which were entered as the independent variables. The basic model for the logistic model can be written as:

(3-1)

where churn is customer churn (a binary class label {0,1}), x is the input data, and the parameters β0 (intercept) and β1 to βm are estimated with the maximum likelihood (ML)

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grows as the sample size gets larger. ML also handles well with data with categorical dependent variables as in this case (Allison, 1999). The Wald chi-square statistic is used to test the significance of the individual coefficients that are obtained through the maximum likelihood estimation (Allison, 1999).

Decision Trees

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Churn

Family size

1 person 2 people; > 2 people

Gender

Female Male

Figure 3-1: An example of a decision tree for churn

Decision trees can be used for segmentation where people are identified as being members of a specific group, or for prediction where rules are formed and used to predict future events like churn, like with the logistic regression. They can also be used to reduce data and for variable screening where useful subsets of predictor variables are selected from a larger set of variables. The dependent and independent variables used in creating decision trees can be nominal, ordinal or scale. There are four methods in SPSS that can be used to grow the decision trees:

 CHAID, which stands for Chi-squared Automatic Interaction Detection where the independent variable which has the strongest interaction with the dependent variable is chosen.

 Exhaustive CHAID, which is a modification of CHAID. It inspects all possible splits for each predictor or independent variable.

 CRT, which stands for Classification and Regression Trees. It splits the data into homogeneous segments in concern with the dependent variable. The classification tree is generated by using the Gini index of diversity to choose the best splitting decision for the nodes.

 QUEST, which stands for Quick, Unbiased, Efficient Statistical Tree. This method is fast and evades other method’s bias in support of predictors that have many categories. It can only be specified if the dependent variable is nominal.

With both the CRT and QUEST methods, a tree can be pruned to decrease the level of complexity of the tree’s structural design and to avoid overfitting the model. A tree is grown until the stopping criteria are met. The tree is then trimmed automatically to the smallest subtree based on the specified maximum difference in risk.

The advantage that decision trees have over other classification methods, including logistic regression, is that there are no assumptions made regarding the distribution of the independent variables. They can therefore deal with data that is highly skewed along with

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categorical independent variables with ordinal or non-ordinal structure. This reduces the time spent on analysis and the trees are fairly simple to interpret.

3.4.3.1 Model Performance Evaluation

There are several ways to evaluate the performance of a prediction model. Two methods were used in this analysis, confusion matrix and ROC curve. They are described below.

Confusion Matrix

The classification methods (e.g. logistic regression, decision tree) used produce “raw data” during testing which are counts of correct and incorrect classifications from each class. This information can then be presented in a confusion matrix which is a form of contingency table that illustrates the differences between the true and predicted classes for a set of labeled examples. A confusion matrix is shown in Table 3-1. It has four possible outcomes, where Tp

and Tn are the number of true positives (a case is positive and classified as positive) and true

negatives (a case is negative and classified as negative) respectively. Fp (also Type I error) are

numbers of false positives, where a case is negative and classified as positive. Fn (also Type II

error) are the number of false negatives, where a case is positive but classified as negative. Cn

and Cp are the row totals and are the number of truly negative and positive examples. Rn and

Rp are the number of predicted negative and positive examples and N is the overall accuracy

(Bradley (1997), Fawcett (2006)). Table 3-1: Confusion matrix

Predicted class negative positive

Observed negative Tn Fp Cn

class positive Fn Tp Cp

Rn Rp N

Some significant information can be extracted from the table to illustrate certain performance criteria.

Positive predictive value (also called hit rate or recall) is the proportion of positive instances which were classified correctly =

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The false positive value (also called false alarm rate) is the proportion of negative instances which were classified incorrectly as positive =

(3-3)

Negative predictive value is the proportion of negative instances which were classified correctly =

where Rn = Fn + Tn (3-4)

The false negative value is the proportion of positive instances which were classified incorrectly as negative =

(3-5) Sensitivity =

,

where Cp = Tp + Fn (3-6) Specificity = , where Cn = Tn + Fp (3-7) N (Overall accuracy) = or =

(3-8)

In the case of customers at the telecom, those who are in the true positive category are those who churned and correctly classified as churners. Those in the false positive category were non-churners classified as churners and those in the false negative category were churners incorrectly classified as non-churners. Customers in the true negative category were non-churners correctly classified as non-churners. Sensitivity indicates the model’s capability to identify positive results (churn). It is the probability of a customer being predicted as churner, given that the customer has churned. Specificity indicates the model’s capability to identify negative results (non-churn). This is the probability of a customer being predicted as non-churner, given that the customer has not churned. A model with high sensitivity has a low type II error rate while a model with high specificity has a low error I rate. Sensitivity and specificity are also terms associated with ROC curves which will be discussed next.

The ROC Curve

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default threshold) to .7, the model will predict fewer positive predictions. The ROC curve then symbolizes all possible combinations of values in the confusion matrix and it can be used to find the probability threshold which yields the highest overall accuracy for the model.

ROC graphs are two-dimensional graphs where true positives (Sensitivity) are plotted on the Y axis and false negatives (1-specificity) are plotted on the X axis. This graph represents tradeoffs between benefits (True positives) and costs (False positives). An example of a ROC curve is showed in Figure 3-2. The ideal diagnostic test would be in the top left corner (0,1) where 100% sensitivity and 100% specificity are demonstrated. At this point, all positive and negative cases are correctly classified. At the point in the lower left corner, all cases are classified as negative and in the upper right corner, all cases are classified as positive. The cutoff point for the prediction model can be adjusted either to increase the Tp but

at the cost of increasing Fp or decreasing Fp at the cost of decreasing Tp.

Figure 3-2: An example of a ROC curve (Source: Deshpande, 2011)

The diagonal line y = x (blue line) portrays a model which randomly guesses the class (churn or non-churn) and the red line represents the results from the classifier/model. Any test results that are above the diagonal line would be better than random, results below the line would give poor results.

Area under an ROC curve (AUC)

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3.4.3.2 Model Validation

The logistic regression models and decision models created for post- and pre-paid training samples will be validated on separate testing samples which are unbalanced to replicate real world data.

3.4.4 The CLV Calculation

After estimating the average revenue per user, the discount rate and the churn for individual customers in the data sets, the next step is to use these results and estimate the CLV for each customer by using model (2-1) discussed in section 2.1.1.

3.4.5 Segmentation

As stated in Section 2.2, the customer database with post-paid and pre-paid subscription at Telecom X will be segmented based on their individual CLV. There will be ten segments for each type of subscription, which are of equal size. The 1st decile consists of the least valuable customers while the most valuable customers are in the 10th decile. These deciles will be described to give some insight about what the customers within the deciles have in common.

3.5 Summary

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4. Data preparation

To be able to make a churn model, it is essential to have the right data. The data should be information about demographics, revenue and call detail records. The telecom has a data warehouse which stores the necessary data that is required to make a churn model. Section 4.1 discusses the sampling and difficulty with skewness in churn data. Section 4.2 explains the time aspect of the data extraction and Section 4.3 shows the categorization of the features that the database encompasses.

4.1. Sampling

There are practical problems related to churn modeling. In a company that offers continuous service, such as a telecom or a bank, the percentage of those who defect will always be somewhat small in any time period. Therefore, a sample from the general population of customers will only acquire a comparatively small number of defectors, even if the sample is large. That consequentially means that it is difficult to reliably distinguish between churn (rare events) and non-churn (Rust and Metters, 1996). To deal with this problem, some authors have emphasized that the training set, which is used to estimate the model is a balanced sample which means that it consists of equal numbers of churners and non-churners (Rust and Metters, 1996; Coussement and Van den Poel, 2008). This means under-sampling, where cases which belong to the majority class (here, non-churn) are discarded until there are even numbers of both classes. The distribution of the data used in the training and testing sets for modeling churn for both post-paid subscriptions and pre-paid subscriptions is shown in Table 4.1.

Table 4-1: Distribution of the data used in the training and testing sets Pre-paid subscription Training dataset

Number of customers who churned 2922 Number of customers still active 3073 Testing dataset

Number of customers who churned 1016 Number of customers still active 14890 The training set for post-paid customers consists of 2190 churners and 2189 non-churners. The testing set consists of 827 (2.9%) churners and 27910 non-non-churners. The training sample for pre-paid customers has a total of 8469 in the training sample and almost 38000 in the testing sample. One of the disadvantages with the pre-paid sample though, is the

Post-paid subscription Training dataset

Number of customers who churned 2190 Number of customers still active 2189 Testing dataset

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proportion of missing data. Since customers with a pre-paid subscription do not need to submit personal demographic information about themselves, demographic variables are those with the most missing data or up to 40%. To find out if there is a significant difference between those customers with demographic information and for those without it, independent-samples t-tests were done with all the independent continuous variables. The results were that the means for the independent variables for the two groups were not the same, except for 1 variable, “Average voice outin volume ratio”. Since there is a difference between these two groups, it is likely that it will be necessary to make a separate logistic regression model for the two groups as it is always difficult to fill in missing values, especially for those variables with only two groups like “Gender”. The decision was therefore taken to exclude those customers with no demographic information in both the training and testing sets. The final training set consisted of a total of 5995 customers, 2922 who churned and 3073 which did not. The testing dataset consisted of 1016 churners (6.4%) and 14890 who were still active. The proportion of those who churned with respect to those who did not is higher in the two data sets combined for pre-paid customers, with 17.98% churners. In the two combined post-paid datasets, there are 9.11% churners. One possible reason for this difference could be that it is easier for pre-paid customers to terminate their subscription at the telecom and they could therefore be more prone to follow a better offer at another telecom. Average age is lower for the pre-paid data sets (37 years in the training set and 40 years in the testing set) than for the post-paid data sets (49 years in the training set and 52 years in the testing set). This could be a signal of younger people being less loyal than older people.

4.2. The time aspect

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Figure 4-1 churned during the performance period and therefore is classified as churn (1). Customer B was still active at the end of the performance period and is classified as censoring (0) (Nie et al., 2011).

Observation period Performance period

Feature extraction Class labeling

T1 T2 T3

Customer A Churn

Customer B Censoring

Figure 4-1: The time window of the analysis

All of the customers included in the data set are active at the beginning of the performance period. A longer performance period was used to collect as many churners as possible. Because churn is a rare event in the customer database for the telecom, two different performance periods of five months were used, the first from 1 July 2010 to 1 December 2010 (interval between T2 and T3) with a observation period from April to June 2010 (interval

between T1 and T2). The second performance period was from 1 December 2010 to 1 May

2011 with observation period from September to November 2010. The first and half of the second data set were used to create a balanced training set and the other half of the second data set was used to create the testing set used for validation of the classification models without under-sampling so that it reflects real world data which has a highly skewed class distribution.

4.3. Independent variables

The mobile phone company has a large data warehouse from which the data needed for the analysis in this research can be extracted. Based on previous research in this field, the customer data that will be used to predict churn can be divided in three main descriptor categories that include the input of prospective explanatory descriptors. These descriptors are, as previously said, shown in Appendix I and are personal demographics, revenue and customer behavior (Xie et al. 2009). For this research, the descriptors have been categorized in a little more detail as follows:

Y = 1

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1. Demographics are personal data of a given costumer, such as age, gender, marital status, place of residence, family size, rate plan, whether or not the customer is the registered payer and tenure which is the number of days a customer was or is active and finally customer status which says whether or not he/she churned or not. The variable “Rate plan” had up to 20 different categories where some categories had many cases and other categories had very few cases. For this reason, it was impossible to use this variable and it was removed from the analysis.

2. Billing data shows the number of billed services, number of billed products, billed amount due to mobile phone usage, discount amount a customer receives, total billed amount and ratio of both mobile usage and discount versus total billed amount. These variables apply only to post-paid customers.

3. Refill history applies only to customers with pre-paid subscriptions. These descriptors are refill frequency and amount and total refill frequency and amount.

4. Calling pattern are descriptors created for both post-paid and pre-paid customers. They are related to inside and outside network and abroad call volume and frequency, total originating and terminating call volume and frequency, total sent and received text messages, ratio of inside/outside network and abroad calls versus total originating call volume/frequency, ratio of originating calls versus terminating call volume and ratio of text messages sent versus text messages received.

5. CDR (call detail records) billed are descriptors of charged amount due to inside/outside network and abroad calls, ratio of inside/outside network and abroad calls versus total charged amount, charged amount due to text messages sent inside/outside network and abroad, ratio of inside/outside network and abroad text messages sent versus total charged amount and then total charged amount. These are also created for both types of subscriptions.

Besides the above mentioned descriptors, there are also derivatives such as the maximum value over the three months and average value over the three months. These features are also all listed in Appendix I. The demographic features are extracted at the beginning of the observation period but the features in the other categories are extracted for each of the three months of the observation period.

4.5 Summary

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5. Results

The results from the analysis are presented in this chapter. Firstly, the sample with the post-paid customers will be described in Section 5.1 and the results shown, both for the training and testing samples. Then results for the pre-paid customers are presented in Section 5.2, both for the training and testing samples. The outcomes of the hypotheses presented in chapter 2 are discussed in Section 5.3. Finally, the results from the CLV calculations and the segmentation are presented and discussed in Section 5.3.

5.1 Post-paid customers 5.1.1 Sample description

In this chapter the sample of post-paid customers at the telecom is analyzed. These are customers who receive their bill at the end of each month. The sample used to training the churn model consisted of 2190 churners and 2189 non-churners or total of 4379 post-paid customers.

Since non-churners are still active customers with the telecom, it is impossible to calculate mean values for the independent variables such as tenure as this will continue to an unknown date in the future. Therefore the mean values are calculated for the time period that is used to extract the data. The mean for the customers’ age was 49.19 years (with a SD = 14.828). The youngest customer is 18 years of age and the oldest customer is 98 years of age. There were more males than females in this sample or 2488 (56.8%) and 1891 respectively. Table 5-1 shows the marital status of the customers. Most of them are married/in a registered partnership (53.2%). 28.9% are unmarried, 11.8% are either divorced or separated and 5.0% widowed. Customers with an unknown status were 0.6% of the sample.

Table 5-1: Marital status of customers in the post-paid training sample

Marital status Frequency Percent Cumulative %

Married/registered partnership 2328 53.2 53.2 Unmarried 1265 28.9 82.1 Divorced 441 10.1 92.2 Widowed 218 5.0 97.2 Separated Other

Marital status unknown

73 26 28 1.7 0.6 0.6 98.9 99.5 100.0 Total 4379 100.0

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consists of 26 customers or 0.6%. Regarding the customers’ family size, which is shown in Table 5-2, most customers in the sample were single individuals or 3244 (74.1%). 922 were in a family consisting of 2 people, 169 in a family of 3 people, 35 were in a family of 4 people and 9 were in a family of 5 people or more. Because there is a large difference in the frequencies in the first two categories and the last three, these last three categories where combined into one. The third category includes 213 customers or 4.9% of the sample.

Table 5-2: Family size of customers in the post-paid training sample

Family size Frequency Percent Cumulative %

1 person 3244 74.1 74.1

2 people 922 21.1 95.1

3 people or more 213 4.9 100.0

Total 4379 100.0

The customers were fairly dispersed over the country, considering that 2/3 of the Icelandic population lives within the greater capital area of Reykjavik. Table 5-3 shows the distribution of the sample over the country. The majority of the customers live within the greater capital area, or 2516 (57.4%) followed by 668 customers who live in the Southern part Table 5-3: Residence of customers in the post-paid training sample

Land area Frequency Percent Cumulative %

Capital area 2516 57.5 57.5 Western Iceland 365 8.3 65.8 Northern Iceland Eastern Iceland Southern Iceland Unknown 592 177 668 61 13.5 4.0 15.3 1.4 79.3 83.4 98.6 100.0 Total 4379 100.0

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Table 5-4 shows the results from a chi-square test for independence. This test was done to explore the relationship between customers’ status (churn or non-churn) and other categorical variables. This table shows how many females and males churned or 991 (52.4% of the females) and 1199 (48.2% of the males) respectively. For a 2x2 table like this, there can be overestimation of the chi-square value but the Yates’ Correction for Continuity compensates for that. The value is 7.467, with 1 degree of freedom (df) with an associated significance level of .006 which is smaller than the alpha value of .05 (see Table II-1 in Appendix II). The conclusion is made that the proportion of males who churn is significantly different from the proportion of females who churn. However the value of Phi is -.042 (p = .006) which indicates that the relationship between the two variables in the table is weak (see Table II-2 in Appendix II).

Table 5-4: Crosstable of Status*Gender in the post-paid training sample

Gender

Total

Female Male

Status Non-churn Count 990 1289 2189

% within gender 47.6% 51.8% 50.0%

Churn Count 991 1199 2190

% within gender 52.4% 48.2% 50.0%

Total Count 1891 2488 4379

% within gender 100.0% 100.0% 100.0%

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means that the proportion of those who churn is not significantly different from the proportion of those who are still active.

To see if there was a significant difference in the mean of different continuous variables for those who churned and those who did not, an independent samples t-test was done (see Table II-3 in Appendix II). Out of the 42 variables regarding customer age, tenure and averages, 34 of them had a significance level for the Levene’s test lower than .05 indicating that the variance of scores for the two groups (churners and non-churners) is not the same. The variables where there was no significant difference in the variance were “Average amount gsm”, “Average ratio gsm”, “Average abroad total charge ratio”, “Average text message innet total charge ratio” and “Average text message abroad total charge ratio”. For the t-test for equality of means, which says whether there is a significant difference between those who churned and those who did not, eight variables had a significance value higher than .05. For all the other variables, there is a significant difference in the mean values between the two dependent groups (churn and non-churn). The eight insignificant variables were “Average ratio gsm”, “Average abroad volume”, “Average abroad volume ratio”, “Average abroad frequency ratio”, “Average voice outin volume ratio”, “Average abroad total charge ratio”, “Average text message abroad charge” and “Average text message abroad total charge ratio”. Of the 33 variables with maximum values, three of them had a significance level for the Levene’s test higher than .05 indicating the variance of scores for churners and non-churners were the same. These variables were “Maximum abroad total charge ratio”, Maximum text messages innet total charge ratio” and “Maximum text messages abroad total charge ratio”. Eight variables had a p > .05 for the t-test for equality of means so there was not a significant difference in the mean values between the two dependent groups. These variables were “Maximum voice outin volume ratio”, Maximum abroad volume”, Maximum abroad volume ratio”, Maximum abroad frequency ratio”, “Maximum innet total charge ratio”, “Maximum abroad total charge ratio”, “Maximum text messages abroad charge” and “Maximum text messages abroad total charge ratio”.

Finally, to find out the effect size statistics, eta squared is calculated. This implies the magnitude of the differences between the two status groups. The equation for eta squared is

Eta squared

Customer Lifetime Value in the Mobile Phone Market in Iceland

Customer Lifetime Value in the Mobile Phone

Market in Iceland

Customer Lifetime Value in the Mobile Phone

Market in Iceland

Management Summary

Preface

Table of Contents

List of Figures

List of Tables

1. Introduction

2. Theoretical Framework

3. Methodology

,

4. Data preparation

5. Results