Study on the Value Based Market Segmentation Method ... · Study on the Value Based Market Segmentation Method: Empirical Study of Korean Mobile Handset Market 1Yonghee Shin, 2Hyori

Study on the Value Based Market Segmentation Method: Empirical Study of Korean Mobile Handset Market

1Yonghee Shin, 2Hyori Jeon, 3Munkee Choi

1, First AuthorETRI, [email protected] *2,Corresponding AuthorETRI, [email protected]

3KAIST, [email protected]

Abstract We suggest a value market segmentation method for high-tech markets. Our research scope is the

mobile handset market, which has the technology characteristics of a high-tech industry such as short cycle innovations, short technology development periods, and quick changes in customer needs. Thus, researchers and company strategists would use value or benefit segmentation method for high-tech market segmenting, because the characteristics may react to frequent changes. A value based market segmenting items are stable items for a long time, and so precisely explain customer needs. Specially, values related with product’s attributes have been shown to be more powerful items. In this paper, we try to show the superiority of a value market segmentation method through a Korean mobile handset market empirical case study.

Keywords: High-Tech Market, Mobile Handset Market, Value Based Market Segmentation, Chaid Analysis, Cluster Analysis

1. Introduction

As competition among firms becomes fiercer, only those firms with a competitive edge can survive in the world market. The most important factor is to appropriately define a target market of products and to quickly identify the customer's needs in the market. In 2010, Apple is one of the most successful firms at finding the proper market for their new media products, such as iPhone [1]. As seen with Apple’s case, a key success factor is how fast a company can catch their customers’ needs and how accurately they can estimate market trend changes. In Korea, on the other hand, there have been some technological failures (such as cityphone, 2.4 wireless LAN, and one-phone) which were largely the result of a lack of understanding of the market, technology trends, and changes in the economic environment. In most cases in the IT industry, customers have shown a tendency to jump from one preference to another because they were affected by radical changes in environmental factors. Under such circumstances, when firms did not perceive the changes and did not meet the customer's needs, they failed to create a new market. Such cases used to be especially noticeable in the high-tech industry, including IT. The characteristics of the IT industry are short-cycle innovation, short technology development periods, and quick changes in customer needs [2], [3], [4]. Thus, in high-tech industries, understanding everything about the customers is the most important factor.

Although many things in the high-tech industry can change very quickly, the firm that meets the customer's needs with their product is sure to succeed. [5] To achieve this, firms must first divide customers into groups with similar requirements and buying characteristics. Firms believe that their marketing processes are linked to how effectively their customer base is segmented, because market segmentation helps companies satisfy diverse customer needs while maintaining certain scale economies [6].

In this paper, we suggest a value market segmentation method for high-tech markets. According to other research, value-based market segmentation method is more powerful than other previous methods [7], [8], [9], [10], [11]. To show the superior of value market segmentation, we use an empirical analysis about Korean mobile handset market. The following sections consist of literature reviews of market segmentation, and empirical study of mobile handset.

Study on the Value Based Market Segmentation Method: Empirical Study of Korean Mobile Handset Market Yonghee Shin,Hyori Jeon,Munkee Choi

Advances in information Sciences and Service Sciences(AISS) Volume4, Number11, June 2012 doi: 10.4156/AISS.vol4.issue11.12

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2. Literature Review

In industrialized countries, market segmentation is an essential element of marketing. Goods can no longer be produced and sold without considering customer needs and recognizing the heterogeneity of those needs [11]. The general concept of market segmentation was first1 presented formally by ref. [12], and many definitions have been proposed. Market segments consist of groups of people or organizations that are similar in terms of how they respond to a particular marketing mix or in other ways that are meaningful for purposes of marketing planning [13], [14], [15]. A market can consist of subgroups that are similar in certain ways, such as demographics, lifestyles, and type of business [16]. The benefits are a better understanding of customers’ needs and wants, leading to more effective marketing programs and greater insight into the competitive situation [10]. Early segmentation efforts were intuitive and pragmatic, based on experience in the market. This was supplemented by basic customer survey research to learn more about the demographics, usage habits, and attitudes. While these efforts had advantages such as easy-to-use data, simple methods, and low cost, they offered limited explanations of real customer purchasing patterns. For example, consider two married white men who each earn $45,000 a year. According to the demographical factors, they belong to the same segmented market. However, we cannot be assured that they will buy the same products, because they have each different preference about consumption. To extend these early and rather simple segmentation efforts, many researchers suggest value-based market segmentation methods using factors such as patterns of behavior, needs, values, benefits, and attitudes. Also, more sophisticated multivariate methods can search for meaningful patterns of responses [17], [18], [19], [20], [21].

Now, we classify the various market segmentation methods according to segmentation bases2 and define concepts to evaluate the effectiveness of methods. As the general basis, there are two segmentation bases that are independent of products, services, or circumstances, and a product-specific basis that is related to customers, products, services, or particular circumstances [21], [22], [23].

Table 1. Literature Reviews of Market Segmentation Methods

Type Classifying Items Pros & Cons Geographic Seg. Customer Location, Region,

Urban/Rural, ACORN etc Easily acquired data Difficult to determine customer buying motivation

Demographic Seg, Age, Gender, Occupation, Socio-economic group

Easily acquired data Difficult to determine customer attitude, interests

Psychographic Seg. General value, Personality, Lifestyle, Attitudes

Actual personality traits that provide meaningful information Offer push marketing Weak relationship b/w criteria And segments

Behavioral Seg. Rate of usage, Benefits, Loyalty status, Readiness to purchase

Easy to access and measure the historical buying behaviors Difficult to predict future because of stable data pattern

Benefit Seg, Benefits from products or services to purchase

Explain reasons why consumer chooses to buy or prefer particular products Difficult to set up criteria to divide segments

Value has been shown to be a powerful force in governing the behavior of individuals in all aspects

of their lives [24], [25]. The use of value in marketing could be improved if the behavior of consumers could be related to their values. Some researchers have provided a conceptual model connecting values to behavior as a “means-end chain”. Means are objects, such as products or activities. Ends are valued

1 Smith (1956) defined market segmentation as involving viewing a heterogeneous market as a number of smaller

homogeneous markets in response to differing preferences, attributable to consumers’ desires for more precise satisfaction of their varying wants.

2 A segmentation basis is defined as a set of variables or characteristics used to assign potential customers to homogeneous groups [11].


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states of being, such as happiness, security, and accomplishment. A means-end chain explains the major consumer processes that link values to behavior [26]. Many researchers have used the concept of means-end chain in their studies. In most cases, the models using means-end chains partial explained the relationship between means and ends. In table 2, we use a review of past literature to summarize the components of means-end chains.

Table 2. Literature Reviews of Means-end Chain Theory

Definition Researchers

Value

Terminal value (Global value)

Concern with preferred end-states of existence, e.g. happiness, security, accomplishment, etc

Rokeach1973

Closest to a person's self-concept Howard 1977

Instrumental Value (Domain specific

value)

Related to modes of behavior

Rokeach1973 Howard 1977

Be at an intermediate level to bridge the gap b/w the general global value and benefit as evaluative beliefs about product's attributes

Vinson et al. 1977

Benefit

Any result (physiological or psychological) occurring directly or indirectly to the consumer’s behavior, either sooner or later

Haley 1968 Myers 1976

Values-benefit linkage: values provide benefits with positive or negative valences

Rokeach1973

Customer provides two benefits (functional and practical ones) that are derived from products' attributes benefit-attribute linkage

Young & Feigin (1975)

Attributes

Description of a product consisting of the physical features, ingredients, or characteristics of a product

Myers 1996

Figure 1. Means-end Chain Theory

According to table 2, customers generally consume products under their own value systems, as illustrated by the Means-end chain model. To identify meaningful different market segments and obtain a rich stock of information to predict changes in the segmented market, we must segment the market in consideration of the criteria related to the use-value system, such as general value (terminal value), domains specific value (instrumental value), and benefits from a product’s attributes, all based on the means-end chain theory.


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3. Research Methodology

Our research scope is the mobile handset market. The mobile handset market has the technology characteristics of a high-tech industry, such as short cycle innovations, short technology development periods, and quick changes in customer needs. The characteristics may react to frequent changes in the environmental factors in the market. Thus, researchers and company strategists would rather use value or benefit segmentation related to the product-specific attributes, because the value and benefit factors are stable items for a long time [27], [28]. In summary, we need a long time stable market segmentation method for high-tech market owing to the rapid market’s environmental changes. A value- or benefit based method is the right thing.

Value has been shown to be a powerful force in governing the behavior of individuals in all aspects of their lives [24], [25]. The use of value in marketing could be improved if the behavior of consumers could be related to their values. Some researchers have provided a conceptual model connecting values to behavior as a “means-end chain”. Means are objects, such as products or activities. Ends are valued states of being, such as happiness, security, and accomplishment. A means-end chain explains the major consumer processes that link values to behavior [26].

Customers generally consume products under their own value systems, as illustrated by the Means-end chain model. To identify meaningful different market segments and obtain a rich stock of information to predict changes in the segmented market, we must segment the market in consideration of the criteria related to the use-value system such as domains specific value.

The empirical study has three parts. First, we choose a set of consideration variables related to topics from literature reviews and mobile handset studies. Then, we set the final concrete values through FGD and pilot test. Finally, we do the main survey, get the implications, and show the usefulness of our research model to analyze the main survey’s results.

Find the index using literature reviews

For values specific to the mobile handset industry, we used found 35 of the general variables, along with the instrumental values of RVS. These 35 variables, which were reflected by mobile handset characteristics, were changed into general variables.

FGD to find the proper variables for pilot test

We conducted a survey using the FGD (Focus Group Discussion) method to filter the index values and determine the variables for the pilot test. The variables were filtered by FGD. The number of variables of values specific to mobile handsets was reduced from 35 to 20. We used customer responses to change the filtering variables, which were then used in the pilot test. We use the laddering method and cleared up the chain of “Attribute-Benefit-Value” for mobile handsets. The FGD survey was completed from July 7th ~ 11th, 2007 using six sub-groups of mobile service users who lived in or around Seoul and who ranged in age from 13 to 45 years old. The six customer groups for FGD analysis are: female high school students, male university students, single women aged 25~35 who are office workers, single men aged 25-35 who are office workers, housewives aged 36~45 years old, and married male office workers aged 36~45. Pilot test to determine the final variables for analysis of mobile handset market segmentation

The purpose of the pilot test was to screen the final variables as 12 variables in Table 3 for validity and reliability. The pilot test (conducted from August 9th ~ 27th, 2007) consisted of face-to-face interviews with a convenience sampling of 600 mobile phone users with ages ranging from 13 to 59 years old. The FGD was analyzed by tree structure, confirmatory factor analysis, and CHAID methods [26].

4. Empirical Study The main survey was conducted from October 25th ~ November 15th, 2007 through face-to-face

interviews using a proportional stratified sampling method of 1,700 mobile phone users from 13 to 59 years old. For a sample size of 1,700 users, the sampling error with a confidence interval of 95% is ± 2.4%P. Those surveyed all lived in Seoul and five other large metropolitan areas in Korea. For a more


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accurate main survey, we chose the participants based on a proportional stratified sampling method which considered regional ratio, sex ratio, age structure, market shares of telecommunication service, etc [16]. In October 2007, there were three Korean telecommunication service providers: SKT, KTF, and LGT, with respective market shares of 50.5% (SKT), 31.6% (KTF), and 17.9% (LGT).

Table 3. The Proportional Stratified Sampling of Regional and Sex Ratio

Sex Region

Male Female Total Sample Ratio Sample Ratio Sample Ratio

Seoul 401 23.6 401 23.6 802 47.2 Gyeonggi 99 5.8 98 5.8 197 11.6

Busan 142 8.4 144 8.5 286 16.8 Daegu 97 5.7 97 5.7 194 11.4

Daejeon 56 3.3 56 3.3 112 6.6 Gwangju 55 3.2 54 3.2 109 6.4

Total 850 50.0 850 50.0 1700 100.0

Table 4. The Proportional Stratified Sampling of Age and Market Share

ServiceProvider

Age

SKT KTF LGT Total (Ratio) Male Female Sub-total Male Female Sub-total Male Female Sub-total

13~18 37 40 77 32 34 66 12 9 21 164 (2.2) (2.4) (4.6) (1.9) (2.0) (3.9) (0.7) (0.5) (1.2) (9.7)

19~24 52 53 105 29 31 60 11 13 24 189 (3.1) (3.1) (6.2) (1.7) (1.8) (3.5) (0.6) (0.8) (1.4) (11.1)

25~29 61 60 121 36 34 70 17 17 34 225 (3.6) (3.5) (7.1) (2.1) (2.0) (4.1) (1.0) (1.0) (2.0) (13.2)

30~35 56 57 113 36 36 72 19 18 37 222 (3.3) (3.4) (6.6) (2.1) (2.1) (4.2) (1.1) (1.1) (2.2) (13.1)

36~39 59 58 117 38 39 77 23 23 46 240 (3.5) (3.4) (6.9) (2.2) (2.3) (4.5) (1.4) (1.4) (2.7) (14.1)

40~45 51 53 104 32 33 65 20 22 42 211 (3.0) (3.1) (6.1) (1.9) (1.9) (3.8) (1.2) (1.3) (2.5) (12.4)

46~49 54 52 106 31 32 63 18 17 35 204 (3.2) (3.1) (6.2) (1.8) (1.9) (3.7) (1.1) (1.0) (2.1) (12.0)

50~55 49 47 96 30 30 60 18 19 37 193 (2.9) (2.8) (5.6) (1.8) (1.8) (3.6) (1.1) (1.1) (2.2) (11.5)

56~59 15 16 31) 5 3 8 2 2 4 43 (0.9) (0.9) (1.8) (0.3) (0.2) (0.5) (0.1) (0.1) (0.2) (2.5)

60~ 3 2 5 2 0 2 2 0 2 9 (0.2) (0.1) (0.3) (0.1) (0.0) (0.1) (0.1) (0.0) (0.1) (0.5)

Total (Ratio)

437 438 875 271 272 543 142 140 282 1700 (25.7) (25.8) (51.5) (15.9) (16.0) (31.9) (8.4) (8.2) (16.6) (100.0)

Factor Analysis: Identifying Structure of Handset Specific Value

In table 5, we perform factor analysis3 to determine the cognitive structure of mobile handset specific values. For factor analysis, we use the extraction method with principal component analysis and varimax rotation. We define the factors that are over 1.0 of Eigen value and 5.0 of factor loading, and verify the consistency and reliability of the inter-factors by using Cronbach’s alpha and scree test [30]. The first factor is named “Prestige or Fame,” in consideration of three aspects. The first aspect is proactive efforts that are explained by “continuous progress” and “self-development.” Another aspect is social success caused by one’s own efforts, as explained by “making others envious” and “self-pride.” The final aspect is the attitude toward anxieties related to effort and success, as explained by ‘”relief,” “self-contentment,” and “seeking joy.” The second factor is called “Achievement or Accomplishment,” because respondents’ seek to “achieve social works” by “managing work quickly and efficiently” based on “their own human network.” The third and final factor is named “Relationship.” It is not connected with achievement, fame, prestige, and accomplishment; instead, it focuses on emotional connections with other people based on an oriental perspective.

3 The factor analysis in our research is meaningful, as the results of KMO (Kaiser-Meyer-Olkin) and Bartlett’s

Sphericity tests are 0.874, and 0.000 (sig.), respectively.


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Table 5. Mobile Handset Specific Value based Rotated Factor Loading Matrix Factor

Variable Factor 1 Factor 2 Factor 3 Prestige Achievement Relationship

Continuous progress .740 .151 -.070

Making others envious .714 .113 .076

Stress relief .685 .262 -.031

Self-pride .683 .326 -.079

Self-development .643 .123 .074

Self-contentment .641 -.035 .250

Seeking joy .619 -.100 .307

Managing work quickly and efficiently .049 .705 .003

Achieving social works .175 .665 -.016

Managing human network .142 .593 .288

Smooth communication -.059 .140 .770

Fellowship and friendship .190 .015 .682

Eigen value 3.293 1.549 1.320

% of Variance 27.439 12.905 11.002

Cumulative % 27.439 40.344 51.346

Cluster Analysis: Clustering Mobile Handset Specific Values We perform cluster analysis by using the scores of the three factors to create sub-groups. The

number of clusters was selected by referring to the dendrogram that is used with hierarchical cluster analysis4, the curve of fusion coefficients, the distribution of segmented markets, and the difference between group-types divided by the criterion of mobile handset specific values. We determined that three clusters were appropriate for our market segmentation research model [16], [31]. Following this decision process, we decided the number of clusters and performed K-mean clustering method. In table 6, we show that the results of our cluster analysis are statistically significant.

Table 6. Results of Cluster Analysis Using Mobile Handset Specific Value Factor Score

Cluster I

N=586, 34.5% Cluster II

N=329, 19.3% Cluster III

N=785, 46.2% F Sig.

Prestige 0.187 -1.533 0.503 201.963 .000

Achievement -0.765 0.451 0.382 737.887 .000

Relationship 0.654 0.139 -0.547 763.163 .000

We find the differences by comparing the average factor loading values of the three clusters.

Cluster I has a very positive attitude towards “relationship” and a very negative attitude towards “achievement.” This cluster is named “honorable relationship seeker”5, because its constituents think that fame and friendship are more important than job achievement. Cluster II is called “communication-oriented social achievement seeker,” and its characteristic is pursuit of inner success through a social life. So, Cluster II has a positive attitude towards “achievement” and a negative attitude towards “prestige.” Lastly, Cluster III prefers “prestige” to “relationship”; in fact, Cluster III has a negative attitude towards “relationship.” This cluster is defined as “achievement-oriented prestige seeker.” Similar to cluster II, people in Cluster III inherently want to win when in competition with others, so they seek to become envy. Last, we profile the demographic, socio-economic, usage pattern and innovative traits per clusters. There are significant differences of three clusters in aspect of willingness to pay, choice factor, usage pattern, innovativeness.

4 It uses the within-average linkage method with squared Euclidean distance. 5 It is similar to type 1 (Guanxi-expanding type) among the four mobile phone user types that were defined using

Q-methodology [27].


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Table 7. Results of Profiling per Clusters

N Mean Std. Deviation F Sig

WTP

1 586 228873.7201 93056.88597

2.862 .057 2 329 229392.0973 107585.45128

3 785 240356.6879 95419.99084

Total 1700 234276.4706 97208.26492

Choice Factor

(%)

Design

1 586 31.4676 15.01782

1.205 .300 2 329 32.6140 16.272453 785 31.0892 14.38775

Total 1700 31.5147 14.98723

Function

1 586 19.4113 10.44087

14.941 .000 2 329 16.2766 8.939813 785 19.6306 9.52259

Total 1700 18.9059 9.82229

Brand

1 586 20.3157 10.00569

2.782 .062 2 329 19.8632 10.976533 785 21.2994 10.37711

Total 1700 20.6824 10.38144

Price

1 586 28.8055 15.68732

5.606 .004 2 329 31.2462 15.699063 785 27.9809 13.85395

Total 1700 28.8971 14.91110

Usage Pattern

(%)

Voice

1 586 72.8345 20.50770

27.701 .000 2 329 81.4465 15.600543 785 72.5592 19.56000

Total 1700 74.3741 19.50299

SMS

1 586 22.5666 17.90124

19.756 .000 2 329 16.3894 13.641863 785 22.9911 16.89580

Total 1700 21.5671 16.86618

Data

1 586 4.5990 7.17960

15.760 .000 2 329 2.1641 5.015573 785 4.4497 7.21784

Total 1700 4.0588 6.89286

Innovativeness

1 586 1.9249 1.15374

49.783 .000 2 329 1.3891 .92756

3 785 2.0892 1.06412

Total 1700 1.8971 1.10192

5. Conclusion

In this paper, we suggest to do value based market segmentation methods for proper high-tech markets and analyze the implications of that method in the case of mobile handset market of South Korea. In general, high-tech markets are uncertain in terms of markets, technological development, and customers’ needs. The value based segmentation model solves these uncertainty problems because it has long-term variables such as handset specific values. So, if strategists segment a high-tech market with our research framework, they will be able to forecast the future of the market more precisely despite market uncertainty.

For further study, dynamic analysis will be possible using the value based market segmentation method suggested in this paper. When users change the basic segmented markets, researchers examine the traces of their market changes. For the tracing analysis, researchers must create a panel related to the research scope and they historically observe how the users’ purchasing of products changes. If they


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get the historical data about users’ churning patterns from the basic segmented markets, the data will be strong and indisputable proof of our new market segmentation method.

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Study on the Value Based Market Segmentation Method ... · Study on the Value Based Market Segmentation Method: Empirical Study of Korean Mobile Handset Market 1Yonghee Shin, 2Hyori