The proceedings of the International Conference ...

The proceedings of the International Conference

MARKETING – FROM INFORMATION TO DECISION 6th Edition

8-9 November, 2013 Cluj-Napoca ROMANIA

Editors: Ioan PLĂIAŞ Raluca CIORNEA

International Conference “Marketing – from information to decision” 6th Edition 2013

Customer segmentation based on the value of consumption patterns in telecommunications Mihai Florin BĂCILĂ Babeş-Bolyai University, Faculty of Economics and Business Administration, România [email protected] Adrian RĂDULESCU Business Logic System Ltd [email protected] Ioan Liviu MĂRAR Business Logic System Ltd [email protected]

ABSTRACT The purpose of relationship marketing is to maintain clients and increase their loyalty. In order to attain these objectives, mobile operators must identify subscriber segments and provide services that best suit their needs. This study aims to identify behaviour patterns among the prepaid subscribers of a mobile operator. The aim of the analysis is to carry out a segmentation of subscribers based on their spending of credit on local or international calls, SMSs and data. The K-mean cluster analysis was applied to group subscribers in segments. The average sum of squares error indicator was used to determine the internal cohesion of clusters, and, to identify the differences between clusters, the ANOVA and Tukey post-hoc tests were utilised. The study led to the identification of nine subscriber segments with different behaviour, the results obtained offering the mobile operator the possibility to better adapt their marketing strategies to their subscribers’ needs. Keywords: customer segmentation, relationship marketing, telecommunication JEL Classification: M31

1. Introduction The marketing environment in which mobile operators carry out their activity features an intense competition for clients and increased degree of mobility. As clients prefer services that offer them a high level of satisfaction, these companies must know subscribers’ needs and be able to adapt their offer to the possible changes in subscribers’ expectations. Thus, instead of targeting all subscribers with the same campaigns, they should be approached differently, according to their needs, characteristics and behaviour (Bose and Chen, 2010). Consequently, mobile operators cannot focus only on the creation and implementation of strategies to attract new clients, but they also have to concentrate on maintaining existing clients and increasing their value. Market liberalisation and globalisation constrain mobile operators to keep their clients in order to be able to preserve their market share in the context of increasing costs for attracting new subscribers (Tripathi and Siddiqui, 2010:49). Most of the times, increasing the loyalty and value of present customers is as important as and sometimes easier to achieve than attracting subscribers from competitors. (Tsiptsis and Chorianopoulos, 2009: 291).

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International Conference “Marketing – from information to decision” 6th Edition 2013 Creating and developing a closer relationship with subscribers involves adopting and implementing the principles of customer relationship management. To be able to maintain the competitive advantage and enhance subscribers’ loyalty, mobile phone operators must find and develop unique and customised subscriber interaction solutions. Subscriber relationship management systems are used for this purpose (Uturyté-Vrubliauskiené and Linkevicius, 2013). In case of mobile operators, the marketing department is in charge of increasing subscriber loyalty (Owczarczuk, 2010). Payne (2005: 22-23) defines customer relationship management (CRM) as a “strategic approach that is concerned with creating improved shareholder value through the development of appropriate relationships with key customers and customer segments”. CRM unites the potential of relationship marketing strategies and IT to create profitable, long-term relationships with customers. CRM advantages for companies are: customizing offers and communications, increasing the retention rates, customer loyalty and profitability, growth in sales (Diller, 2000: 32; Bruhn, 2001: 117). Relationship marketing aims to identify the best and most valuable customers with whom to develop long-term mutually advantageous relations (Niarn and Bottomley, 2003). For a better management of customer relationship, it is necessary to collect information on the value of customers as they are not all equal. Some are more valuable than others. Clients with a higher value for the company should be offered better services, while those having a lower value should benefit from fewer services (Keropyan and Gil-Lafuente, 2012). Segmentation is an extremely important marketing concept within CRM because improving relationships with existing customers involves a better segmentation and understanding of customers (Storbacka, 1997). 2. Aspects related to segmentation The provision of mobile services allows the collection of a significant amount of information. Millions of individuals, in thousands of different places can make tens or hundreds of transactions in a short timespan thus leading to billions of events that can be registered. To collect and store such a significant amount of data, there are special methods that have recently appeared and have been developed (Foss and Stone, 2001: 67). Market segmentation is an essential component of strategic marketing planning used to define markets and to efficiently allot resources (Assael and Roscoe, 1976; Piercy, 2002). This activity allows the company to design valuable and customised services and offers for clients and prospects (Feldman, 2006: 24) as services can no longer be designed, provided and marketed without taking into account the needs and expectations of customers and their heterogeneity (WedelandKamacura, 2000:3). The concept of segmentation is based on the fact that individuals that share the same behaviours and needs are more likely to have a more homogenous response to the marketing actions of the company (Dibb, 1999). Market segmentation refers to the process of dividing existing or potential clients in several groups to increase the internal homogeneity of each segment and to maximize the heterogeneity between them (McDonaldsandDunbar, 2004: 34; PainaandPop, 1998: 103). It must not be regarded as a simple tool for identifying subscriber segments because it has strategic and tactical consequences. From the strategic point of view, segmentation allows the identification of valuable customers, markets and segments that will be approached and the positioning of valuable offers. From the operational point of view, it helps companies to better understand customers in order to develop closer relationships with them focusing on the interaction with customers and on influencing their behaviour (Dibb, 2001:196; Storbacka, 1997:481). Market segmentation has several advantages: a better understanding of clients, determining the most attractive customer segments, effective allocation of resources, correct positioning of products and services on the market, designing customised incentives, choosing the best

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International Conference “Marketing – from information to decision” 6th Edition 2013 distribution channels, winning a competitive advantage as a result of product and service customisation and of the creation of valuable offers as well as of the superior harnessing of market potential (Dibb and Simkin, 2010: 113; Kiang et al., 2006: 36; Feldman, 2006: 24; Tsiptsis and Chorianopoulos, 2009: 190). Practically applying segmentation comes with significant disadvantages: the costs of approaching markets are higher than in the case of mass marketing, the identified segments can be small and mobile, there can be segments in which, despite similar demographic characteristics, individuals have different consumption behaviours, the implementation of marketing actions can be difficult (Young, Oti and Feigin 1978:411, Dibb 2001:196; McDonalds and Dunbar 2004:431). The efficiency of segmentation in case of CRM has increased in recent years due to the development of marketing techniques through databases. Profile extraction techniques offer marketers better methods for the segmentation of customers and for the customisation of their marketing strategies to the specific needs of each customer segment (McCarty and Hastak, 2007: 656). Generally, marketers can segment their customer base using geographic, demographic, psychographic, socio-economic and behavioural criteria, as well as criteria related to the psychological attitude towards a certain product or service (Kotler and Keller, 2006:247; McDonalds and Dunbar, 2004:35-37; Tsiptsis and Chorianopoulos, 2009:191-193). The majority of companies use segmentation based on demographic information. For a better definition of subscriber segments on the market, it is recommended that, besides demographic information, mobile operators carry out segmentation based on information related to the needs, consumption behaviour, preferences and perceptions of subscribers, churn probability, subscriber potential, lifetime and value, subscriber value evolution. Market segmentation is recommended only in case of heterogeneous markets (Dibb and Simkin, 2010: 115). The criteria used for market segmentation must have certain characteristics and must lead to the creation of segments that fulfil the some conditions. Segments should be (Dibb, 1999; Kotler and Keller, 2006; Paina and Pop, 1998; Tonks, 2009): - measureable so that the size, purchasing power and other characteristics can be exactly quantified and determined; - substantial and have a benefit potential that justifies the creation of special marketing schemes for their approach; - homogenous, each one being distinct from the point of view of subscriber profiles and needs; - accessible and differentiable in order to be approached and used by means of different marketing actions; - actionable which means that they allow the creation of effective schemes to attract and provide services to customers; - stable, meaning that they have to preserve their characteristics for a longer period; - consistent with the mission, culture and structure of the organisation, with the marketing strategy and all existing resources. Bayer (2010: 248) stated that the segmentation types most often used by mobile operators are based on subscribers value, subscriber behaviour, subscriber lifecycle and churn probability. These types of segmentation are used for different situations and they focus on various aspects.

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International Conference “Marketing – from information to decision” 6th Edition 2013 3. Research methodology Mobile operators’ customers are divided, at a basic level, in legal entities and individuals, the latter being afterwards divided according to the relationship they have with the operator, into postpaid and prepaid subscribers (Tsiptsis and Chorianopoulos, 2009: 292-293). This study was carried out among individuals, prepaid subscribers. They do not have a contractual relationship with the mobile operator and they purchase credit in advance. They do not receive an invoice and pay for services before using them. The study analysed 9.993 prepaid subscribers. The data used, concerns the months between May and December 2012 and between January and April 2013. The analysis did not take into account the persons that have not recharged their credit in 2013 (January, February, March and April) and those that have spent nothing on calls, SMSs or data services in 2013. The following section presents the classification of subscribers in several categories using the following variables: the total value of the last 12 months’ recharges, the total value of the SMSs sent in the last 12 months, the total amount spent on data services in the last 12 months, the total value of the last 12 months’ on-net calls, the total value of the last 12 months’ off-net calls and the total value of the last 12 months’ international calls. The SPSS programme was used to group customers in segments and the method used was the non-hierarchical K-Mean Cluster method. This algorithm is aimed at segmenting the population so as to ensure a minimum variance within each cluster. Before performing this analysis, it is necessary to establish the number of segments the population will be divided in. This method involves the following steps (Mooi and Starstedt, 2011:256-259): - The algorithm randomly selects the centre of each cluster; - The Euclidian distance between each element and the centre of the cluster in calculated; - The geometric centre of each cluster is recalculated; - The distance between each object and the central values of clusters is calculated, and objects are distributed in clusters according to the minimum distance to the centre of clusters. Steps 3 and 4 are repeated until the best solution is found. This analysis aims to group customers in several basic groups, depending on the value of their behaviours (the value of recharges, the amount spent on on-net calls, off-net calls, international calls and data, as well as the value of sent SMSs. A logarithm was applied to the data in order to reduce the differences between values. Further one all were standardized. The factors will be listed in a descending order from the point of view of their contribution to the distribution of population in groups (see Table 1): value of recharges, value of on-net calls, value of off-net calls, value of sent SMSs, amount spent on data and value of international calls. As the value of Sig. is less than 0.05, the results are relevant: Table 1. ANOVA Analysis Cluster Error Mean Square df Mean Square Zscore(LnTvRec) Zscore(LnTvCallsOnn) Zscore(LnTvCallsOffn) Zscore(LnTvCallsInt) Zscore(LnTvsms) Zscore(LnTvdata)

889.681 793.631 745.315 191.847 671.096 563.200

8 8 8 8 8 8

.281 .350 .374 .538 .398 .374

df 9890 9758 9513 3306 8910 7183

F 3164.457 2266.418 1992.472 356.472 1684.719 1506.465

Sig. .000 .000 .000 .000 .000 .000

To express and assess the solution of the segmentation analysis, the following aspects need to be taken into account: the number of segments and the size of each segment, the cohesion of clusters and the difference between them (Tsiptsis and Chorianopoulos, 2009). Following the analyses (the internal homogeneity of the resulting groups and the distances between them) and taking into consideration practical aspects (the size that segments must have to be

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International Conference “Marketing – from information to decision” 6th Edition 2013 substantial and the number of consumer groups that can be managed by the mobile operator), it was decided that the population should be divided in nine segments. The following table contains information regarding the size of the resulting segments. As the Table 2 shows, there is no dominant segment (see table below) so as to be necessary to perform another segmentation analysis on the population. There is neither a very small segment so as to be necessary to study it separately. Table 2. Cluster-based population structure Cluster 1 2 3 4 5 6 7 8 9

Number of individuals in the cluster 1,137 790 1,555 1,150 647 1,092 1,368 943 1,249

Percentage of the total number of individuals 11.45 7.95 15.66 11.58 6.52 10.99 13.78 9.50 12.57

Percentage of the total value of recharges 16.95 2.08 16.96 5.26 0.76 32.37 5.10 8.03 12.49

A quality segmentation of the population involves a high concentration of the cluster’s elements around the central values (centroids) of clusters. To determine the quality of cluster cohesion, we analysed, for each cluster, the maximum (Euclidian) distance to the central values and we obtained reduced values. Moreover, to determine the distance between the members of each cluster and the central value of the cluster, we calculated the Average sum

AverageSSE 

1 dist (ci , x) 2   N iC xCi (Tsiptsis

of squares error indicator using the formula and Chorianopoulos, 2009: 98), where C stands for the total number of clusters, ci represents the centre of cluster i, x represents the value for a member of cluster i. The resulting value is 1.22, which indicates a high cohesion. The distances between the central values of the final segments are big so there is no need to reduce the number of segments and to unify two resulting groups. The smallest distance is between clusters four and eight, and the biggest distance is between clusters five and six. To determine the differences between clusters, we used the ANOVA test which indicated significant differences (at a significance level of 0.000) between groups in case of each variable taken into consideration. The post-hoc Tukey test confirmed (at a significance level of 0.000) that the averages of the six variables taken into consideration significantly differ in case of the nine clusters. The most valuable cluster is cluster six, which comprises 10.99% of the analysed clients, but it contributes 32.7% to the total value of recharges. The least valuable cluster is cluster five which contains 6.52% of the analysed clients, but only contributes 0.76% to the total recharged value. Cluster 1 2 3 4 5 6 7 8 9

Table 3. Distances between the central values of the final segments 1 2 3 4 5 6 7 8 3.727 2.254 2.858 4.655 2.398 2.304 2.361 2.009

3.125 2.193 2.083 4.623 2.300 2.246 3.018

2.516 4.442 2.313 3.015 1.989 2.622

2.667 3.220 2.513 1.799 1.955

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5.770 2.817 3.737 3.947

4.249 2.554 2.762

2.663 2.408

2.012

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International Conference “Marketing – from information to decision” 6th Edition 2013 Cluster 1 – This cluster is made up of 1,137 individuals and comprises subscribers with highvalue recharges. These subscribers’ recharges amounted to an average of 141.13 M.U. each, within 6 months. In absolute value, their credit was distributed as follows: 57.42 M.U. on onnet calls, 35.25 M.U. on off-net calls, 3.22 M.U. on international calls, 3.94 M.U. on SMSs, and 0.03 M.U. on data. In figures we used following notations: TvRec - total recharge value, TvCharge - total charge value, TVCallson/TVCallsoff/TVCallsint - total value of on-net/off-net/international calls, TvSMS - total value of SMSs, TvData - total value of data, DcallsOnn/DcallsOffn/Dcallsint duration of on-net/off-net/international calls and Dcharpaid/Dcharunpaid - duration of paid/unpaid traffic generated by subscriber. As a percentage of the total recharged (spent) value, they used their credit as follows: 40.68% (46.63%) on on-net calls, 24.98% (28.62%) on off-net calls, 2.28% (2.61%) on international calls, 2.79% (3.20%) on SMSs, and 0.02% (0.03%) on data. In absolute value, they had on average 2,028.87 minutes of on-net calls, 275.93 minutes of off-net calls, 13.81 international minutes, 1,498.52 minutes of paid traffic generated by the subscriber and 2,950.90 minutes of unpaid traffic generated by the subscriber.

Figure 1. Cluster 1 variation (percentage) from average population values Compared to the average values for the entire population, the subscribers in this cluster spend 73.30% more on-net calls, 99.73% more on off-net calls, 34.99% less on international calls, 15.57% less on SMSs, 99.36% less on data. Taking into account the structure of the population, these clients prefer off-net and on-net calls, showing no interest in SMSs and international calls as well as a lack of interest in data services. Compared to the average of the population, they charge (consume) 48.00% (48.03%) more. These clients spent 87.25% of the total recharged value. Cluster 2 – This cluster consists of 790 people and includes subscribers with a very low recharge value. On average, these subscribers’ recharges in the past 12 months amounted to 24.95 M.U. In absolute value, they spent 10.99 M.U. on on- net calls, 0.74 M.U. on off-net calls, 0.59 M.U. on international calls, 0.57 M.U. on SMSs and 2.89 M.U. on data services. In terms of percentage, from the total recharge value they spent 44.05% (50.72%) on on-net calls, 2.98% (3.43%) on off-net calls, 2.36% (2.72%) on international calls, 2.31% (2.66%) on SMSs and 11.59% (13.34%) on data services.

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International Conference “Marketing – from information to decision” 6th Edition 2013 In absolute value, on average, they had 389.42 minutes of on-net calls, 4.28 minutes of off-net calls, 3.59 minutes of international calls, 250.91 minutes per subscriber of paid data traffic and 899.23 minutes per subscriber of unpaid traffic.

Figure 2. Cluster 2 variation (percentage) from average population values Compared to the average values for the entire population, subscribers in this cluster spend 66.82.% less on on-net calls, 95.78% less on off-net calls, 88.06% less on international calls, 87.63% less on SMSs and 45.68% less on data services. With reference to the structure of the population, these subscribers prefer on-net calls and, to a lesser extent, data traffic, revealing a high lack of interest in off-net calls, international calls and SMSs. Compared to the population average, they recharge (consume) 73.83% (73.94%) less. These subscribers spent 86.86% of the recharged value. Cluster 3 – This cluster consists of 1,555 persons and includes subscribers with an average recharge value. On average, these subscribers are those whose recharges in the past 12 months amounted to 103.26 M.U. In absolute value, they spent 34.95 M.U. on on-net calls, 18.74 M.U. on off-net calls, 0.17 M.U. on international calls, 5.50 M.U. on SMSs and 10.73 M.U. on data services. As a percentage of the recharge value, subscribers spent 33.85% (39.17%) on on-net calls, 18.15% (21.00%) on off-net calls, 0.16% (0.19%) on international calls, 5.33% (6.17%) on SMSs and 10.39% (12.02%) on data services. In absolute value, on average, they had 1,942.25 minutes of on-net calls, 158.73 minutes of off-net calls, 0.49 minutes for international calls, 1,221.13 minutes per subscriber of paid data traffic and 3,100.65 minutes per subscriber of unpaid traffic.

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Figure 3. Cluster 3 variation (percentage) from average population values Compared to the average values for the entire population, subscribers in this cluster spend 5.51% more on on- calls, 6.19% more on off-net calls, 96.56% less on international calls, 18.04% more on SMSs and 101.63% more on data services. With reference to the structure of the population, these subscribers prefer on-net and off-net calls, data traffic and SMSs, revealing a very high lack of interest in international calls. Compared to the population’s average, they recharge 8.29% (7.29%) more. These subscribers spent 86.43% of the recharged value. Cluster 4 – This cluster consists of 1,150 people and includes subscribers with a low recharge value. On average, these customers are those who recharged 43.35 M.U. in the past 12 months. In absolute value, they spent 6.61 M.U. on on-net calls, 7.84 M.U. on off-net calls, 5.11 on international calls, 2.05 M.U. on SMSs and 5.22 M.U. on data services. These subscribers spend their credit on all types of mobile services. As a percentage of the total recharged value, subscribers spent 15.25% (17.61%) on on-net calls, 17.26% (19.94%) on off-net calls, 11.79% (13.62%) on international calls, 4.73% (5.46%) on SMSs and 12.06% (13.93%) on data services. In absolute value, on average, they had 495.29 minutes of on-net calls, 59.73 minutes of offnet calls, 37.23 minutes of international calls, 258.15 minutes per subscriber of paid data traffic and 1,122.46 minutes per subscriber of unpaid traffic. Compared to the average values for the entire population, subscribers in this cluster spend 80.05% less on on-net calls, 57.60% less on off-net calls, 57.60% less on off-net calls, 3.35% more on international calls, 56.10% less on SMSs and 1.80% less on data services. Regarding the structure of the population, these subscribers prefer international calls, data traffic, SMSs, on-net and off-net calls. Compared to the population’s average, they recharge (consume) 54.54% (54.88%) less. These subscribers spent 86.58% of the recharged value.

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Figure 4. Cluster 4 variation (percentage) from average population values Cluster 5 – This cluster consists of 647 people and includes subscribers with the lowest recharge value. On average, these subscribers are those who recharged 11.15 M.U in the past 12 months. In absolute value, they spent 1.65 M.U. on on-net calls, 1.57 M.U. on off-net calls, 1.32 M.U. on international calls, 0.35 M.U. on SMSs and 1.06 M.U. on data services.

Figure 5. Cluster 5 variation (percentage) from average population values As a percentage of the total recharged value, subscribers spent 14.82% (16.88%) on on-net calls, 14.07% (16.03%) on off-net calls, 11.88% (13.53%) on international calls, 3.17% (3.62%) on SMSs and 9.52% (10.84) on data services.

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International Conference “Marketing – from information to decision” 6th Edition 2013 In absolute value, on average, they had 65.85 minutes of on-net calls, 10.68 minutes of off-net calls, 8.75 minutes of international calls, 57.12 minutes per subscriber of paid data traffic and 262.06 minute per subscriber of unpaid data traffic. Compared to the average values for the entire population, subscribers in this cluster spend 95.02.% less on on-net calls, 91.11% less on off-net calls, 73.24% less on international calls, 92.41% less on SMSs and 80.08% less on data services. These subscribers spend their credit on all types of mobile services. Compared to the population’s average, subscribers recharge (consume) 88.31% (88.24%) less. These customers spent 86.86% of the recharge value. Cluster 6 – This cluster consists of 1,092 people and includes subscribers with the highest recharge value. On average, these customers are those whose recharges in the past 12 months amounted to 280.69 M.U. In absolute value, they spent 82.34 M.U. on on-net calls, 55.85 M.U. on off-net calls, 17.69 M.U. on international calls , 22.18 M.U. on SMSs and 17.96 M.U. on data services. As a percentage of the recharge amount, subscribers spent 29.34% (33.42%) on on-net calls, 19.90% (22.66%) on calls outside the network, 6.30% (7.18%) on international calls 8.13% (9.26%) on SMSs and 6.39% (7.29%) on data services. In absolute value, on average, subscribers had 3,293.53 minutes of on-net calls, 507.98 minutes of off-net calls, 111.01 minutes of international minutes, 2,831.30 minutes per subscriber of paid data traffic and 4,494.67 minutes per subscriber of unpaid traffic.

Figure 6. Cluster 6 variation (percentage) from average population values Compared to the average values for the entire population, subscribers in this cluster spend 148.54.% more on on-net calls, 216.44% more on off-net calls, 257.52% more on international calls, 388.84% more SMSs and 237.46% more on data services. These subscribers spend their credit on all types of mobile services. Compared to the population’s average recharged value, subscribers recharge (consume) 194.36% (196.24%) more. These subscribers spent 87.79% of the recharged value. Cluster 7 – This cluster consists of 1,368 people and includes subscribers with a low recharge value. On average, these subscribers are those who recharged 35.31 M.U in the past 12 months. In absolute value, they spent 17.21 M.U. on on-net calls, 6.54 M.U. on off-net calls, 0.91 M.U. on international calls, 0.46 M.U. on SMSs and 0.01 on data services.

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International Conference “Marketing – from information to decision” 6th Edition 2013 As a percentage of the total recharge value, subscribers spent 48.76% (55.61%) on on-net calls, 18.54% (21.14%) on calls outside the network, 2.60% (2.97%) on international calls, 1.31% (1.49%) on SMSs and 0.04% (0.05%) on the data services. In absolute value, on average, subscribers had 488.49 minutes of on- net calls, 47.09 minutes for of off-net calls, 5.14 minutes of international calls, 399.90 minutes per subscriber of paid data traffic and 1021.30 minutes per subscriber of unpaid traffic.

Figure 7. Cluster 7 variation (percentage) from average population values Compared to average values for the entire population, subscribers in this cluster spend 48.04% less on on-net calls, 62.91% less on off-net calls, 81.42% less on international calls, 90.09% less on SMSs and 99.73% less on data services. With reference to the population’s structure, these subscribers prefer on-net and off-net calls and show a high lack of interest in data traffic, SMSs and international calls. Compared to the population’s average, subscribers recharge (consume) 62.97% (62.78%) less. These customers spent 86.58% of the recharge value. Cluster 8 – This cluster consists of 943 people and includes subscribers with a low to average recharge value. On average, these customers are those whose recharges in the past 12 months amounted to 80.65 M.U. In absolute value, they spent 36.56 M.U. on on-net calls, 2.23 M.U. on off-net calls, 3.56 M.U. on international calls, 4.55 M.U. on SMSs and 5.61 M.U. on data services. As a percentage of the recharged value, subscribers allocated 45.33% (52.40%) for on-net calls, 2.76% (3.19%) for off-net calls, 4.41% (5.11%) for international calls, 5.64% (6.52%) for SMSs and 6.96% (8.05%) for the data services. In absolute value, on average, subscribers had 1,668.14 minutes of on-net calls, 15.36 minutes of off-net calls, 28.38 minutes of international calls, 969.18 minutes per subscriber of paid data traffic and 2714.59 minutes per subscriber of unpaid traffic.

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Figure 8. Cluster 8 variation (percentage) from average population values Compared to the average values for the entire population, subscribers in this cluster spend 10.35% more on on-net calls, 87.38% less on off-net calls, 27.99% less on international calls, 2.53% less on SMSs, 5.53% more on data services. With reference to the population’s structure, these subscribers prefer on-net calls, data traffic, SMSs and they reveal a high disregard to off-net calls. Compared to the population’s mean, subscribers recharge 15.43% (16.11%) less. They spent 86.52% of the recharged value. Cluster 9 – This cluster consists of 1,249 people and includes subscribers with an average recharge value. On average, these customers are those who recharged 94.72 M.U in the past 12 months. In absolute value, they spent 35.29 M.U. on on-net calls, 19.05 M.U. on off-net calls, 11.27 M.U. on international calls, 0.34 M.U. on SMSs and 1.78 M.U. on data services. As a percentage of the amount recharged, subscribers allocated 37.27% (42.62%) for on-net calls, 20.12% (23.01%) for calls outside the network, 11.90% (13.61%) for international calls, 0.36 % (0.41%) for SMSs and 1.87% (2.15%) for data services. In absolute value, on average, subscribers had 1,120.94 minutes of on-net calls, 144.66 minutes of off-net calls, 86.43 minutes of international calls, 980.92 minutes per subscriber of paid data traffic and 1,887.33 minutes per subscriber of unpaid traffic.

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Figure 9. Cluster 9 variation (percentage) from average population values Compared to the average for the entire population, subscribers in this cluster spend 6.55% more on on-net calls, 7.96% more on off-net calls, 127.78% more on international calls, 92.70% less on SMSs and 66.61% less on data services. With reference to the population structure, these customers prefer international calls, on-net and off-net, showing a lack of interest in SMSs and internet traffic. Compared to the average value of the population, they recharge (consume) 0.67% (0.43%) less. These customers spent 86.44% of the recharged value. 4. Research limitations This research has several limitations. Although there are various types of cluster analysis, we have only used one. Other methods of cluster analysis, such as the hierarchical or the fuzzy one, might lead to different conclusions. Another limitation to this study is the absence of subscribers’ identification details as the provision of such details is not compulsory when buying prepaid cards. 5. Conclusions Data warehousing and data mining techniques are tools that can be used by the telecom companies. It is not enough for such a company to collect the data generated within its activity, it also has to analyse it and convert it into marketing information. If managers become aware of the advantages offered by these methods, their companies will be able to identify their subscriber segments, to choose the most attractive ones and to offer them services adapted to their value, characteristics and needs. The study aimed to determine the utility of the K-mean cluster method for customer segmentation. The method used proved to be efficient in processing a significant amount of data leading to the creation of consumer segments that feature a high internal segment homogeneity and a substantial heterogeneity between segments. This study has several managerial implications. One of the conclusions is the fact that most clients use their credit for certain purposes and not for all the services provided by the company, meaning that they have a targeted consumption. Only the subscribers in cluster six

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International Conference “Marketing – from information to decision” 6th Edition 2013 use their credit for all types of services. This is the reason why customers should be approached differently. For those using all types of services, campaigns that aim to increase the consumed value must be conceived and run. For the others, there should be marketing actions to encourage the use of the mobile phone for all types of services offered by the mobile operator. The analysis indicates the existence of different consumer patterns characterised by different needs and preferences, hence the necessity to conceive customized offers and marketing actions for a better approach and treatment of subscribers. References 1.

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Bose, I. and Chen, X. (2010). Exploring Business Opportunities from Mobile Services Data of Customers: An inter-cluster Analysis Approach. Electronic Commerce and Applications, 9(3): 197-208.

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Dibb, S. (1999). Criteria Guiding Segmentation Implementation: Reviewing the Evidence. Journal of Strategic Marketing, 7(2): 107-129.

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10. Foss, B. and Stone, M. (2001). Succesfull Customer Relationship Marketing, Kogan Page, London. 11. Keropyan, A. and Gil-Lafuente, A.M. (2012). Customer loyalty programs to sustain consumer fidelity in mobile telecommunication market. Expert Systems with Applications, 39(12), 15:1126911275. 12. Kiang, M.Y., Hu, M.Y. and Fisher, D.M. (2006). An Extended Self-organizing Map Network for Market Segmentation – A Telecommunication Example. Decision Support Systems, 42(1): 36-47. 13. Kotler, P. and Keller, K. (2006). Management Marketing, Pearson Education Inc., New Jersey. 14. McCarty, J.A. and Hastak, M. (2007). Segmentation approaches in data-mining: A comparison of RFM, CHAID, and Logistic Regression. Journal of Business Research, 60(6): 656-662. 15. McDonalds, M. and Dunbar, I. (2004). Marketing Segmentation: How to Do It, How to Profit from It, Elsevier Butterworth-Heinemann, Oxford. 16. Mooi, E. and Sarstedt, M. (2011). A Concise Guide to Market Research: The Process, Data and Methods Using IBM SPSS Statistics, Springer-Verlag, Berlin. 17. Niarn, A. and Bottomley, P. (2003). Something Approaching Science? Cluster Analysis Procedures in the CRM Era, Journal of Market Research, 45(2): 241-261. 18. Owczarczuk, M. (2010) Churn models for prepaid customers in the cellular telecommunication industry using large data marts, Expert Systems with Applications, 37(6): 4710-4712. 19. Paina, N. and Pop, M.D. (1998). Cercetări de marketing, Presa Universitară Clujeană, ClujNapoca.

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International Conference “Marketing – from information to decision” 6th Edition 2013 20. Payne, A. (2005). Handbook of CRM: Achieving Excellence in Customer Management, Butterworth Heinemann, Oxford 21. Piercy, N. (2002). Market-Led Strategic Change: Transforming the Process of Going on Market, Butterworth Heinemann, Oxford. 22. Storbacka, K. (1997). Segmentation Based on Customer Profitability – Retrospective Analysis of Retail Bank Customer Bases. Journal of Marketing Management, 13(5): 479-492. 23. Tripathi, S.N. and Siddqui, M.H. (2010). An empirical investigation of customer preferences in mobile services. Journal of Targeting, Measurement and Analysis for Marketing, 18(1): 49–6. 24. Tonks, D.G. (2009). Validity and the Design of Market Segments. Journal of Marketing Management, 25(3/4): 341-356. 25. Tsiptsis, K. and Corianopoulos, A. (2009). Data Mining Tehniques in CRM: Inside Customer Segmentation, John Wiley & Sons Ltd., Chicester. 26. Uturyté-Vrubliauskiené, L. and Linkevicius, M. (2013). Application of Customer Relationship Management Systems in Lithuanian Mobile Telecommunications Companies. Science - Future of Lithuania, 5(1): 29-37. 27. Wedel, M. and Kamacura, W. A. (2000). Market segmentation : conceptual and methodological foundation, 2nd edition. Publisher Kluwer Academic, Dordrecht. 28. Young, S., Oti L. and Feigin, B. (1978). Some Practical Considerations in Market Segmentation. Journal of Marketing Research, 15(3): 405-412.

Discussant: Professor Ioan PLĂIAŞ, PhD., Babeș-Bolyai University of Cluj-Napoca

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