168
Int. J. Business Innovation and Research, Vol. 8, No. 2, 2014
Customer-oriented benefit segmentation: an integrated approach Mohammad Hasan Aghdaie*, Parham Fami Tafreshi and Majid Behzadian Department of Industrial Engineering, Shomal University, P.O. Box 731, Amol, Mazandaran, Iran E-mail:
[email protected] E-mail:
[email protected] E-mail:
[email protected] *Corresponding author Abstract: Segmentation is a common and important task for most marketing departments. Besides, other marketing decisions are influenced by market segmentation results. Benefit segmentation is one of the best approaches for market segmentation among others. In this paper, we proposed a novel hybrid benefit segmentation approach which applied two-stage clustering, conjoint analysis, and Delphi technique to segment customers according to their benefits. More precisely, we used two-stage clustering based on main benefits derived from conjoint analysis to classify customers into different segments. Conjoint analysis was used to assess and balance dissimilar aspects of consumers’ needs. Delphi method was used for the selection of the most important criteria and their levels which conjoint analysis required all of them for evaluation. This approach did not only help managers to segment customers but also to provide a way to analyse consumer behaviour, determine the marking strategy and extract the importance of each attribute in each segment. Keywords: market segmentation; benefit segmentation; two-stage clustering; Ward’s method; K-means clustering; conjoint analysis; Delphi method; laptop market; Iran. Reference to this paper should be made as follows: Aghdaie, M.H., Tafreshi, P.F. and Behzadian, M. (2014) ‘Customer-oriented benefit segmentation: an integrated approach’, Int. J. Business Innovation and Research, Vol. 8, No. 2, pp.168–189. Biographical notes: Mohammad Hasan Aghdaie received his Bachelors and Masters in Industrial Engineering from Shomal University, in Amol. He is the author of more than 21 scientific papers in international conferences and international journals which were published, accepted or under reviewing. His current research interests include operations research, decision analysis, multiple criteria decision analysis, operations research interfaces with other fields, especially marketing, market segmentation, marketing research and modelling, market design and engineering, data mining, application of fuzzy sets and systems, creative thinking and problem solving and pricing. Parham Fami Tafreshi is a senior student of Industrial Engineering at Shomal University. His current research interests include both theoretical and empirical fields and his primary research interests are operations research, advertising planning, marketing strategy, decision making, pricing and new production development. He also actively researches interdisciplinary issues such as scenario generation, hybrid strategy and customer behaviour. Copyright © 2014 Inderscience Enterprises Ltd.
Customer-oriented benefit segmentation
169
Majid Behzadian is an Assistant Professor at the Department of Industrial Engineering, Shomal University. He received his MS and PhD in Industrial Engineering from Tarbiat Modares University. His current research interests include product design and development, multicriteria decision-making, and strategy deployment. He has published two books and several journal publications. He is an ad hoc reviewer for the European Journal of Operational Research, Computers and Industrial Engineering, Applied Mathematical Modelling and International Journal of Energy Sector Management, among others.
1
Introduction
With too demanding customers and highly competitive marketplace, satisfying customers is a hard task. Due to many kinds of customers with different characteristics, needs, and wants a company cannot satisfy the whole market simultaneously (Aghdaie et al., 2011). Many companies or researchers have applied market segmentation as a useful approach to deal with these issues (Lee et al., 2012; Hemalatha et al., 2009; Ali and Bharadwaj, 2010; Hemalatha, 2011). This approach was introduced by Smith (1956) in a marketing literature and after over 50 years, market segmentation still to be a central topic of many current studies and market practices (Chaturvedi et al., 1997). According to Kotler (1999), market segmentation refers to the identification and separation of a market into distinct subsets of customers, where any subset may conceivably be selected as a target market to be reached with a distinct marketing mix. Marketers often use market segmentation to find the most proper and attention-grabbing segments of a market for a specific product (i.e., goods, services, and ideas) to concentrate their marketing efforts on them in an effective way (Lee et al., 2006). A crucial and common issue with market segmentation for both scientists and practitioners is how to partition a market. To distribute heterogeneous customers of a market into several homogenous subsets, Kotler (2001) suggested general bases in marketing literature such as geographic, demographic, psychographic, and behavioural. More precisely, there are a number of characteristics, for example, life style, loyalty status, nationality, benefits, etc. which are used for segmentation (Kotler, 2011). Benefit segmentation is developed by Haley (1968) and it is obviously superior to other approaches and has shown its advantages as a proper approach to identify and develop subset constructions among other approaches (Loker and Perdue, 1992; Morrison, 1996). After defining market segmentation bases, another important question is choosing an appropriate market segmentation technique or techniques. Although, there are a lot of market segmentation techniques in the marketing literature, clustering methods are commonly applied in practice (Wedel and Kamakura, 2001). In clustering problems, K-means algorithm which is developed by MacQueen (1997), is one of the most popular, simple, and frequently used algorithms (Hung and Tsai, 2008). Although many clustering algorithms have been developed, the K-means algorithm has been extensively applied by scientists (Jain, 2011). Besides, K-means algorithm can deal with the large sample sizes associated with market segmentation studies (Anil et al., 1997). This method assumes that the number of clusters is already known by the users, which, unfortunately, is not true in practice (Wu and Wunsch, 2009). Therefore, identifying the number of clusters in advance becomes a very important topic
170
M.H. Aghdaie et al.
in cluster validity (Dubes, 1993). One of the approaches to solve this problem is using hierarchical clustering techniques for finding the number of clusters and then follows cluster analysis by K-means algorithm which is suggested by Punj and Stewart (1983). This integration has been named two-stage cluster analysis. Ward’s (1963) method is one of the best and most popular hierarchical clustering techniques (Mingoti and Lima, 2006). For improving the quality of clustering analysis, these two techniques have been integrated in this research. Another important market segmentation technique is conjoint or trade-off analysis. Conjoint analysis is a multivariate technique that has been developed since 1960 for understanding how individuals develop preferences for buying goods (Raghavarao et al., 2010). The general idea behind conjoint analysis was that humans evaluate the overall desirability of complex good based on a function of the value of its separate parts (Orme, 2005). Also, this method is a powerful and effective method for assessing benefits in segmentation (Green and Srinivasan, 1978a; Cattin and Wittinik, 1982; Green and Krieger, 1991). Therefore, using conjoint analysis as a tool for benefit segmentation can be a good combination. Despite the fact that some previous researchers used these tools for market segmentation lonely, few of them have been integrated these approaches. Besides, there are a few studies which combine two-stage clustering and conjoint analysis for market segmentation. There is no evidence in the literature that any of them used all of these techniques with Delphi method together. Vividly, this is the first study which focuses on laptop market in Iran. In today’s market segmentation problems, scientists and marketers try to consider numerous consumer attributes and their benefits simultaneously. This approach can handle massive volume of data to recognise segments and extract futures of each segment. The main contribution of the study is integration of conjoint analysis, Delphi method, and two-stage clustering, for benefit segmentation of the market. By the late 1980s, with the launch of the first laptop computer, the laptop computers have shown a swift evolution as the generations develop (Aghdaie and Tafreshi, 2012). Its birth combined with its rapid and widespread adoption can be considered as one of the most important developments in the portable computer industry and in information technology over the past three decades. Laptop market is a fast growing market in many countries. In Iran similar to many other countries, this market is growing fast and has a good potential for introducing new products or services. Unfortunately, many companies have not conducted an important market research or segmentation in this market. Therefore, in this research, market segmentation tools are employed to benefit segmentation of Iranian laptop buyers. The remainder of the paper proceeds as follows. In the next section, a literature review of market segmentation and related topics are illustrated in details. In Section 3, the suggested methodology is elaborated. Empirical study on Iran laptop market and data analysis is presented in Section 4. At the end, conclusions and further research are debated in Section 5.
Customer-oriented benefit segmentation
2
171
Literature review
2.1 Market segmentation Market segmentation was first raised by Smith (1956), an American marketing researcher, and the concept was further promoted by many scholars and has been applied by many companies. Smith (1956) recognised the existence of heterogeneous in the customers’ needs and wants, based on the economic theory of imperfect competition which is introduced by Robinson (1938). Segmentation is based on the idea that consumers are heterogeneous in needs, wants, preferences or other market related behaviour (Trinh et al., 2009). This idea plays an important role in marketing according to many authors (Smith, 1956; Frank et al., 1972; Wind, 1978; Dickson and Ginter, 1987; Weinstein, 1994; Dibb and Stern, 1995; McDonald and Dunbar, 1998; Wedel and Kamakura, 2001). Market segmentation refers to the process of splitting customers, or potential customers, in a market into different groups, or segments, within which customers share a similar level of interest in the same, or comparable, set of needs satisfied by a distinct marketing proposition (McDonald and Dunbar, 2004). Applying market segmentation is more useful than mass marketing because of some reasons. A company can use market segmentation to expand own business by better understanding of customers’ wants and needs (Aghdaie et al., 2013). Market segmentation techniques can give marketing researchers a leading view because defining appropriate segments can form the basis for effective targeting and predicting of potential customers (O’Connor and O’Keefe, 1997). Market segmentation strategy was considered as an alternative to product differentiation strategy to deal with diversity in the market (Geraghty and Torres, 2009). It remains a key decision area for companies undertaking marketing and strategic planning (McDonald, 1995). It provides bases for other marketing decisions (Weinstein, 1994). Kotler (1980) stated that the importance of executing marketing segmentation analysis encompasses better perception of the market to find a most proper position for a production in a market, selecting the most suitable segment as target marketing, realising opportunities in existing markets, and obtaining competitive advantage through product differentiation. In addition, market segmentation increase efficacy and effectiveness of business processes of the organisation and leads to increase revenue and extents economic benefits which are provided by consumers’ society (Chiu et al., 2009).
2.1.1 Benefit segmentation Benefit segmentation is one of the best market segmentation approaches among others. Haley (1968) introduced the concept of benefit segmentation to marketing literature and since then it has been used in both consumer and business markets (Wedel and Kamakura, 2001). According to Haley (1968), an important reason for its superiority is that benefits sought by customers are the fundamental reasons for the existence of true market segments. Besides, Haley (1968) claimed that these benefits are the basic reasons for the heterogeneity in consumers’ choice behaviour and determine the consumers’ behaviour much more accurately than do other segmentation variables such as demographic, geographic, life style, price, income, etc. In addition, this approach is casual while other segmentation approaches are descriptive (Haley, 1968). The major objective of benefit segmentation is to identify the benefits sought by market segments
172
M.H. Aghdaie et al.
and accordingly, to deliver such benefits in the most efficient and effective way (Chung et al., 2004).
2.2 Clustering techniques Clustering is a useful technique for the discovery of some knowledge from a dataset and it’s an exploratory method which can use to solve classification problems (Chiu et al., 2009). Cluster analysis is widely used in customer segmentation (Chiu and Tavella, 2008; Dillon et al., 1993). Also, this technique is a convenient method commonly used for the identification and definition of market segments (Hong, 2012). Hierarchical and partitional are two major groups of cluster analysis (Jain and Dubes, 1988; Jain et al. 1999). In another view; clustering methods can be classified into hierarchical and non-hierarchical groups. Previous researchers have noted that there are over 50 clustering methods in marketing literature and these methods have been applied to market segmentation problems (Milligan and Cooper, 1985). K-means algorithm is an old clustering algorithm which belongs to non-hierarchical group. Though K-means was first proposed over 50 years ago, it is still one of the most widely used algorithms for clustering (Jain, 2011). The main reasons for its popularity are ease of implementation, simplicity, quick, efficiency, and empirical success (Forgy, 1965; Mirkin, 2005). Besides, it can accommodate the large sample sizes associated with market segmentation studies (Anil et al., 1997). One of the disadvantages of the K-means algorithm is that the number of clusters must be supplied as a parameter (Gan et al., 2007). For identifying the number of clusters which K-means algorithm is required, Ward’s method was used. This integration forms a new clustering method which was called two-stage clustering method. Punj and Steward (1983) suggested that this integration is a feasible solution for clustering. The reason is that hierarchical methods, like Ward’s minimum variance method, can determine the candidate number of clusters and starting point that non-hierarchical methods, like the K-means method, need, while non-hierarchical methods can provide better performance with the specified information (Kuo et al., 2002). Ward’s minimum variance was used because it has been worked in earlier studies (Hair et al., 1995; Malhotra, 1993). Also, this method can do clustering appropriately with ordinal data (Everitt, 1993). In addition, Ward’s method achieves better results with respect to other hierarchical clustering methods except in the presence of outliers (Sharma, 1996; Punj and Steward, 1983). Furthermore, this method tends to create segments such that the variation within these segments does not increase too radically (Hardle and Simar, 2003).
2.3 Conjoint analysis The origins of conjoint analysis can be traced back to the 1920s, but the starting point could be 1964 by mathematical psychologists to solve sophisticated problems (Luce and Tukey, 1964). The general idea was that humans evaluate the overall desirability of a complex product or service based on a function of the value of its separate parts (Orme, 2005). This method emphasises on heterogeneity of consumers and estimates the desirability with importance effects for each subject (McFadden, 1986). In contrast to the compositional tools, conjoint analysis is de-compositional (Jaeger et al., 2001). In a de-compositional approach, peoples’ preferences scores elicited from their responses with a back-door and indirect way. Conjoint or trade-off analysis is one of
Customer-oriented benefit segmentation
173
the best multivariate techniques which used to understand how customers develop preferences for buying goods (Hair et al., 1995). In the design, conjoint analysis was introduced as one of the market research hard tools for making more appealing products in future (Akao and Mazur, 2003; Venkata Subbaiah et al., 2011). Base on Kamakura (1988), conjoint analysis is especially helpful in the identification and understanding of benefit segments. Also, this method is a powerful and effective method for assessing benefits in segmentation (Green and Srinivasan, 1978a; Cattin and Wittinik, 1982; Green and Krieger, 1991).
2.4 Delphi method The Delphi method originated in a series of studies that the RAND Corporation conducted in the 1950s (Okoli and Pawlowski, 2004; Melnyk et al., 2009). According to Linstone and Turoff (1975) some of the general characteristics of this method can be stated as follows: This technique may be defined as a method for constructing a group communication process so that the process is effective in allowing a group of individuals, as a whole, to deal with a sophisticated problem. To reach a consensus in Delphi method there are some feedback of individual contributions of information and knowledge some assessment of the group judgment or view; some opportunity for individuals to revise views; and some degree of anonymity for the individual responses. According to Murry and Hammons (1995) this method is made of five steps: a
select the qualified experts
b
conduct the first round of a survey
c
conduct the second round of a questionnaire survey
d
conduct the third round of a questionnaire survey
e
integrate expert opinions and to reach a consensus and steps (c) and (d) are normally repeated until a consensus is reached on a particular topic.
The primary goal was to develop a method which can use to reach the most dependable consensus of a group of experts (Dalkey and Helmer, 1963). The Delphi method accumulates and analyses the results of anonymous experts that communicate in written, discussion and feedback formats on a particular topic (Tsai et al., 2010). In addition, Delphi is a very flexible tool which permits to reach a consensus, through the collection of experts’ opinions on a given issue during successive stages of questionnaire and feedback (Vidal et al., 2011). According to Skulmoski et al. (2007), this method is appropriate as a research tool when there is partial knowledge about a dilemma or phenomenon. The Delphi technique is being increasingly used in many sophisticated areas in which a consensus is to be reached in group meeting (Chan et al., 2001). This methodology has also been used recently in many fields for receiving general agreement between experts (such as Scott, 2001; Scott and Walter, 2003; Bryant and Abkowitz, 2007; Tavana et al., 2012; Büyüközkan, 2004). However, in this process it is possible to submerge differences of opinion and thus suppress the existence of uncertainty (Linstone and Turoff, 1975).
174
3
M.H. Aghdaie et al.
Proposed methodology
In this paper, we propose a benefit segmentation approach with integration of conjoint analysis, two-stage clustering and Delphi method. The suggested approach is expected to provide accurate market segments for better marketing strategy decision making. For this purpose, a six-step methodology is proposed in the paper, as shown in Figure 1. In the first step, the most suitable market segmentation base has been selected. Benefit segmentation was selected as a major base. Because benefits sought by customers are the fundamental reasons for the existence of true market segments (Haley, 1968). After that, two famous market segmentation tools with respect to benefit segmentation have been chosen. In the second step, conjoint analysis procedure was used to priorities customers’ needs. Also, conjoint analysis was used as a main base for two-stage clustering and benefit segmentation. One of the critical point of conjoint analysis is accurate selection of attributes and their levels. Therefore, third step employs Delphi methodology to select the most important attributes and their levels. In the fourth step, questionnaire was designed and sampling survey was conducted. The benefits derived from conjoint analysis are used as an input to two-stage clustering technique in the fifth stage. At the end, analysis of the results including conjoint analysis, two-stage clustering and benefit segmentation were represented in this stage. Figure1
Schematic representation of the process proposed for benefit segmentation process
Step 1
Identification of market segmentation base and tools
Step 2
Conjoint analysis procedure
Step 3
Delphi method
Step 4
Questionnaire design and sampling
Step 5
Two-stage clustering
Step 6
The analysis of the results
3.1 Conjoint analysis procedure Conjoint analysis is an appropriate market research tool which is used for understanding how individuals develop preferences for products and benefit segmentation studies (Naes et al., 2001). Also, conjoint measurement analysis plays an important role in marketing (Hardle and Simar, 2003; Chaudhuri and Bhattacharyya, 2009; Wang, 2011). Conjoint analysis is one of the most frequently applied market research tools which is able to design and price a product or a service simultaneously (Akao and Mazur, 2003; Orme, 2005).
Customer-oriented benefit segmentation
175
The following are the essential stages to perform a conjoint analysis procedure (Wedel and Kamakura, 2001; Gustafsson et al., 2003): 1
Determination of the attributes and levels: the selection of attributes and attribute levels which together make up alternative product concepts is the first step in conjoint analysis procedure (Gil and Sánchez, 1997). These attributes reflect key product features which consumers can used to evaluate the product. Also, attributes’ levels should cover the whole range of representative levels (Halbrendt et al., 1991). Therefore, successful conjoint analysis needs an appropriate selection of attributes and levels. For the purpose of this paper, attributes and levels selected based on available literature survey and interviews with laptop seller managers. At the end, Delphi methodology was used as decision making tool for final selection and we obtain six attributes which defined in their levels, as shown in Ttable 1. The main attributes were: price, size, processor speed, guarantee duration, hard disk capacity and RAM.
2
Stimulus set construction: For the purpose of this paper, a full-profile approach is selected. Full-profile conjoint has been a mainstay of the conjoint analysis community for decades (Orme, 2005). By academics suggestion, the full-profile approach is useful for measuring up to six attributes (Green and Srinivasan, 1978b). Besides, this analysis could be used for paper-and-pencil studies (Orme, 2005). Also traditional full-profile approach can measure interactions between attributes. Creating the profiles is another part of this step. Usually, a factorial or fractional factorial design is used (Naes et al., 2001). In this study, this tool is used to design the product profiles. In this approach, the number of hypothetical profiles of laptops is obtained by multiplying the number of levels associated to each attribute. This method can generate a large number of product profiles (in our case: 4 × 4 × 4 × 4 × 4 × 3 = 3,072 hypothetical profiles). It is difficult, from a consumer’s point of view, to evaluate a large number of product concepts. Therefore, it is necessary to select a sample of product profiles, but maintain the effectiveness of sorting and evaluating the relative importance of a product’s multi-dimensional attributes. A fractional factorial design has been chosen to reduce the number of profiles to 29. A special class of fractional design, called orthogonal arrays was used for this reduction. Here, two sets of data were obtained. One, estimation set, consisting of 25 stimuli, was used to calculate part-worth functions for the attribute levels. The other, holdout set, consisting of four stimuli, was used to assess reliability and validity. The orthogonal arrays (orthoplan) were generated by SPSS-18.0 software. So, total 29 design cards resulted and therefore respondents have to evaluate questionnaires consisting of 29 cards. For the survey purpose, we have used Metric Conjoint Analysis. Here, respondents were required to provide preference ratings for the laptop package described by 29 profiles in the estimation set and 4 profiles in the holdout set. The ratings were obtained using ten-point scale (1= least preferred, 10 = most preferred). An example of profile card was depicted in Table 2. Table 3 shows a few numbers of profiles and an example of a profile card, respectively.
3
Stimulus presentation: choosing the method of data collection: questionnaire was used as a stimulus in this study.
4
Calculating part-worth utility for each level of attributes.
176
M.H. Aghdaie et al.
5
Calculating the relative importance of each attributes.
6
Evaluating and interpreting the results.
Table 1
Conjoint attributes and attribute levels
Attributes A1
Levels, descriptions (number of level in 29 profiles)
Price (USD)
L11: $800 (11/29) L12: $1,200 (6/29) L13: $1,600 (6/29) L14: $2,000 (6/29)
A2
Size (inch)
L21: 11(12/29) L22: 13 (5/29) L23: 15 (6/29) L24:17 (6/29)
A3
Processor speed (CPU Intel)
L31: Core i3 up to 2.26 GHZ (11/29) L32: Core i5 2.26 up to 2.53 GHZ (6/29) L33: Core i5 2.40 up to 2.93 GHZ (7/29) L34: Core i7 2.66 up to 3.33 GHZ (5/29)
A4
Guarantee duration (Year)
L41: 1 year (11/29) L42: 2 year (10/29) L43: 3 year (8/29)
A5
Hard disk capacity (GB)
L51: 250 GB(13/29) L52: 320 GB (5/29) L53: 500 GB (6/29) L54: 750 GB (5/29)
A6
Ram (memory capacity) (GB)
L61: 2 GB(12/29) L62: 3 GB (6/29) L63:4 GB (6/29) L64: 6 GB (5/29)
Table 2
Example of a profile card
Profile number: 10 How likely are you to purchase this laptop?......... Processor speed (Intel)
Core i5 2.40 up to 2.93 GHZ
Hard disk capacity
320 GB
RAM (Memory capacity)
2 GB
Guarantee duration
2 years
Size (inch)
11
Price (USD)
$1,200
Least preferred
Most preferred
1
2
3
4
5
6
7
8
9
10
□
□
□
□
□
□
□
□
□
□
29
…
20
…
10
…
1
9
L11
9
L12
A1
9
L13
9
L14
9
9
L21
L22
A2 L23
9
9
L24
A3
9
9 9 9
L31 L32 L33 L34
9
L41
A4
9
9
L42
9
L43
A5
9
9
9
9
L51 L52 L53 L54
9
9
L61
L62
A6
9
9
L63
L64
Table 3
Profiles
Customer-oriented benefit segmentation 177
Design of profiles
178
M.H. Aghdaie et al.
3.2 Delphi method As it was mentioned, Delphi method was employed to identify the most important attributes and levels for conjoint analysis. We selected the Delphi method for the following reasons: 1
We want to determine conjoint analysis attributes which would influence product concepts so this part of study is an investigation study. Also, this sophisticated selection requires experts who understand our market circumstances and structure. For these reasons Delphi method is an appropriate approach (Okoli and Pawlowski, 2004).
2
In our case, there are a limited number of market experts with suitable knowledge. The Delphi panel size requirements are not very large and it would be practical to solicit up to four panels from 10 to 18 members in size (Paliwoda, 1983).
3
Among other group decision making approaches, this technique does not require to meet the experts physically (Rohrbaugh, 1979). Using Delphi could save time and cost required for collecting experts’ opinions and it’s good for studies in which experts are far from each other (Hanafizadeh and Mirzazadeh, 2011).
4
The Delphi study is adaptable in its design, and easily can control follow-up interviews. These criteria could result the collection of richer data leading to a deeper understanding of the fundamental research questions (Okoli and Pawlowski, 2004).
The Delphi questionnaire included a list of ten attributes. All of them were selected based on available literature survey and interviews with laptop seller managers. Numbers of recommended experts in Delphi method are often a group between 9 and 18, and there is not a general agreement in literature (Vidal et al., 2011). Another important factor in Delphi methodology is choosing qualified experts. According to Skulmoski et al. (2007), some of the critical factors of experts are: having enough knowledge and experience about the survey issues; Capacity, willingness, time to participate and sufficient communication skills. Our panel study in this paper contained 18 experts, 10 of them being academics, 8 of them being industrial laptop market practitioners, 6 of them women and 12 of them being men. Academics were selected based on some factors including number and quality of research papers which are related to our paper, their fields and teaching courses. Industrial practitioners are selected based on experience and university degree. The Delphi questionnaire was distributed to our expert group. The respondent were asked to indicate on a five-point Likert scale to what extend each variable influences customer purchasing behaviour, according to their knowledge about Iran laptop customers. According to Delphi results, six variables identified as the most effective attributes on the consensus of Laptop market experts (see Table 1).
3.3 Questionnaire design and sampling 3.3.1 Data source Survey data were collected through questionnaires, between March 1 and March 3, 2011. The total sample size consisted of 760 respondents, 267 women and 493 men. All respondents are Iranian, aged 13 years old or older, who tend to buy laptop soon. The
Customer-oriented benefit segmentation
179
questionnaires collected a wide range of information including socio-demographic characteristics (age, gender, marital status, education, occupation, income). The age of respondents range from 13 to 55 years, with a mean of 26.54 (SD = 6.95). The questionnaires distributed in Paytakht Shopping Center. Paytakht Shopping Center is the biggest and primary shopping centre for selling and buying laptops. Besides, this place is a hub of laptop computers which is located in capital of Iran, Tehran. The main reason for selecting this centre is that about 70% percent of total laptops which is sold in Iran, has been sold through this centre. This centre is always crowded with customers and many buyers form other cities come there for buying new laptops. Table 4 shows the socio-demographics of the sample. Table 4
Socio-demographic characteristics of the sample (% of respondents, n = 760)
Gender
Income class ($)
Male
64.9
Female
35.1
=1,600
2.8
Age
Education
13 to 18
5.9
Diploma or under
7.1
18 to 24
40.3
Bachelor degree
61.4
24 to 30
33.4
Master degree
25.7
30 to 40
14.5
PhD
5.8
>40
5.9
3.4 Two-stage clustering In this study, two different types of cluster analysis techniques is used to cluster customers based on their main benefits into meaningful homogeneous sub-groups which may exist within in the market. Firstly, Ward’s method is used to determine the optimum number of clusters. Secondly, the K-means clustering technique was applied for final clustering with the initial number of clustering. There are some reasons to apply ward’s method for clustering as follows: 1
This method has worked well in earlier studies (Hair et al., 1995; Malhotra, 1993; Verbeke et al., 2007; Canever et al., 2007)
2
According to Everitt (1993), it can perform well by considering ordinal data.
3
Among hierarchical clustering tools Ward’s method performs better results (Punj and Stewart, 1983; Sharma, 1996).
4
The method tends to decrease variation in each created segment (Hardle and Simar, 2003).
Although Ward’s method has performed successfully in some of the earlier studies, but non-hierarchical method are superior to hierarchical methods (Punj and Stewart, 1983). They are more robust to outliers and the presence of irrelevant attributes (Wedel and
180
M.H. Aghdaie et al.
Kamakura, 2001). A large numbers of non-hierarchical methods are available but Kmeans is the best known and most widely used of those procedures Wedel and Kamakura, 2001). Punj and Steward (1983) suggested that integration of Ward’s method and K-means method can provide better results for clustering. The most important reason for such integration is that Ward’s method can provide the optimal number of clusters and starting point of each cluster which the K-means method requires to determine the final solution due to its efficiency. K-means method with a derived commonly performs better than other methods across all conditions and provides the best recovery of cluster structure (Punj and Stewart, 1983). Also among clustering methods, the K-means method is the most frequently used, since it can accommodate the large sample sizes associated with market segmentation studies (Anil et al., 1997).
4
Empirical results
4.1 Conjoint analysis for all the customers As it was stated above, we established the utilities for each attribute level using the conjoint analysis method and analysed further using SPSS 18.0 statistical software. Table 5 represents the relative utilities which were obtained and relative importance of each of the attributes. In column four of the Table 5; the average utility scores are shown. The average utility scores reflect desirability of the various aspects of an attribute; with higher suggesting that respondents had a greater preference for that aspect. The scores show not only a preference ranking but also the degree of preference attributes. For example, respondents preferred high processor speeds, but hard disk drive capacity was not very important. For price attribute, the utility estimation is negative. With increase in the price the preference for product becomes lower. Table 5 shows the average importance values of attributes. The average importance values of attributes, provides a bases for comparing the importance placed on each attribute relative other attributes. The second column of the Table 5 shows the relative importance of attributes. The major determination of Iranian laptop buyers’ preferences were price (relative importance 27%), size (relative importance 18%), processor speed (relative importance 16%), guarantee duration (relative importance 16%), RAM (relative importance 14%) hard disk capacity (relative importance 9%). While evaluating the goodness of fit of the estimated conjoint model, we found out that value of Kendall’s tau is 0.840 and the value of Pearson’s R is 0.975. Both of these values are reasonably high and these results are significant at 5% level of significance (asymptotic significance = 0.000) (see Table 7). We have also used four stimuli as validation or holdout stimuli to determine internal validity. Parameters from the estimated conjoint model (using 25 stimuli) were used to predict preferences for the holdout set of stimuli and then they were compared with actual responses by calculating correlation. Considering the Table 7, we have found out that value of Kendall’s tau is 0.887 for the four holdout cases. This value is significantly high (asymptotic significance = 0.087). So, we can say that our conjoint model has high predictive accuracy and internal validity.
Customer-oriented benefit segmentation
181
4.2 Two-stage clustering results After gathering the data, two-stage clustering method was used. In the first step, the optimal number of clusters based on Ward’s method was calculated. The optimal number of clusters was four and consequently profiles of customers were classified into four segments. Also, Schwarz’s Bayesian criterion (BIC) gave the best fit for four clusters. Besides, an average silhouette measure that is slightly greater than 0.5 indicates reasonable partitioning. In the second step, K-means method was used to improve the results from Ward’s method. In performing the K-means method, the initial seed points were taken from the cluster centres on 29 profiles. As a result, the four segments had 158 (21%), 192 (25%), 275 (36%), and 135 (18%) of the potential customers.
4.3 Interpretation of the clusters Figure 2 was used to provide a basis for comparison between mean values of the segments on each profile. This figure gives a simple graphical aid for examining the segments in terms of mean values of profiles with other segments. Here the profiles and mean values on a particular profile are identified along the X-axis and Y-axis, respectively. Figure 2
Representation of mean values on profiles for all the customers and each segment (see online version for colours)
182
M.H. Aghdaie et al.
The most important interpretations of the segments can be defined as follows: •
Segment 1: This segment consisted of 116 (73%) men and 42 (27%) women. Also, this segment consisted of 131 (83%) single and 27 (17%) married respondents. The age of respondents ranged from 14 to 50 years, with mean of 25 (SD = 6.59). The average budget for buying laptop in this segment was $895, with range from $350 to $1,600 (SD = $218). The average computer using time was 243 minutes on every day. The preferred colour for laptop was silver. As, already mentioned conjoint analysis used for balancing different levels of customer. Table 3 shows the average relative importance values of attributes in overall and each segment. The scores show not only a preference ranking but also the degree of preference attributes. The respondents belonged to this segment were very sensitive to price. Price lonely has near 50% of the importance. The major determinations of preferences in this segment were price, size, guarantee duration, processor speed, RAM and hard disk capacity.
•
Segment 2: This segment consisted of 122 (64%) men and 70 (36%) women. 81% of populations in this segment were single and only 19% got married. The age of respondents ranged from 19 to 53 years, with mean of 30 (SD = 6.99). The average budget for buying laptop in this segment was $1,179, with range from $600 to $2,000 (SD = $295). The average computer using time was 252 minutes on every day. The preferred colour for laptop was black. Conjoint results indicate that the major determinations of respondent’s preferences in this segment were size, price, guarantee duration, RAM, processor speed and hard disk capacity.
•
Segment 3: This segment consisted of 241 (88%) men and 34 (12%) women. Besides, this segment consisted of 228 (83%) single and 47 (17%) married respondents. The age of respondents ranged from 14 to 52 years, with mean of 26 (SD = 6.07). The average budget for buying laptop in this segment was $1,689, with range from $600 to $2,000 (SD = $392). The average computer using time was 260 minutes on every day. The preferred colour for laptop was white. Conjoint analysis indicated in this segment that the major determinations of preferences were price, size, guarantee duration, processor speed, RAM and hard disk capacity.
•
Segment 4: This segment consisted of 103 (76%) men and 32 (24%) women. 81% of respondents belonging to this segment were single. The age of respondents ranged from 13 to 55 years, with mean of 24 (SD = 8.73). The average budget for buying laptop in this segment was $1,250, with range from $400 to $2,000 (SD = $293). The average computer using time was 239 minutes on every day. The preferred colour for laptop was black. The respondents belonged to this segment were very sensitive to processor speed and price. Processor speed and price had near 37% and 27% of the importance, respectively. The major determinations of preferences in this segment were processor speed, price, size, guarantee duration, RAM and hard disk capacity.
A6
A5
A4
A3
A2
.668 1.003 1.337
L63 L64
.405
L54 .334
.304
L53
L62
.202
L52
L61
.101
1.867
L43 L51
.622 1.245
1.659
L34 L42
1.244
L33 L41
.830
L32
.828
L24 .415
1.066
L31
1.008
L23
L14 L22
–2.648
L13 .652
–1.986
L12
L21
–.662 –1.324
L11
Overall
.990
.742
.495
.247
.265
.198
.132
.066
1.315
.877
.438
1.108
.831
.554
.277
.240
.415
.433
.295
–6.525
–4.894
–3.263
–1.631
Seg 1
1.574
1.180
.787
.393
.376
.282
.188
.094
2.233
1.489
.744
1.079
.809
.539
.270
2.443
2.745
2.439
1.524
–1.593
–1.195
–.796
–.398
Seg 2
1.487
1.115
.743
.372
.704
.528
.352
.176
1.923
1.282
.641
.566
.425
.283
.142
.697
.886
.833
.538
–.867
–.650
–.433
–.217
Seg 3
The part-worth utility
1.101
.825
.550
.275
.001
.001
.000
.000
1.878
1.252
.626
5.357
4.018
2.678
1.339
–.516
–.194
.000
.064
–3.240
–2.430
–1.620
–.810
Seg 4
13.557
9.179
16.098
16.357
18.205
26.604
Overall
9.510
6.948
10.934
10.173
13.504
48.931
Seg 1
16.013
9.561
18.579
12.946
23.312
19.589
Seg 2
16.345
11.607
12.082
12.082
20.136
20.615
Seg 3
The relative importance
9.121
6.303
12.265
37.154
12.509
22.648
Seg 4
Table 5
A1
Levels
Customer-oriented benefit segmentation 183
The part-worth utility and relative importance for all the customers in each segment
184
M.H. Aghdaie et al.
The reliability of the 29 profiles based on Cronbach’s Alpha measure was good (α = 0.848). Factors with alphas 0.6 or higher, indicates that there is a reasonable level of consistency among the items making up the factor (Hair et al., 1995). An analysis of variance as additional analysis was employed to determine the most significant differences across four segments in terms of 29 profiles. In this study, One-way ANOVA was used to examine which of the profiles differed among the four segments. The univariate F test for each clustering profile showed that there was not a profile among all the 29 profiles that it was not different in four clusters. But, between some of clusters were some indifferent profiles. Table 6 shows the number of indifference profiles between two segments. Segment 4 had the lowest number of indifference profiles based on One-Way-ANOVA results. Table 6
Number of indifference profiles between segments Seg. 1
Seg. 2
Seg. 3
Seg. 4
3
2
11
0
3
Seg. 1 Seg. 2
3
Seg. 3
2
0
Seg. 4
11
3
6 6
While evaluating the goodness of fit of the estimated conjoint model based on data derived from the study, we found out that value of Kendall’s tau is 0.853 and the value of Pearson’s R is 0.976 for all the sample. Besides, these values for each segment are high. Both these values are reasonably high and these results are significant at 5% level of significance (see Table 7). We have also used four stimuli as validation or holdout stimuli to determine internal validity. Parameters from the estimated conjoint model (using 25 stimuli) were used to predict preferences for the holdout set of stimuli and then they were compared with actual responses by calculating correlation. Considering the table (Table 7), we have found out that value of Kendall’s tau is 1.000 for the four holdout cases in overall sample and two segments. Also, value of Kendall’s tau in Segment 4 is 0.833 and only Segment 3 has not a good value (Kendall’s tau = 0.677). So, we can say that our conjoint model has high predictive accuracy and internal validity. Table 7
Correlations Overall
Seg. 1
Seg. 2
Seg. 3
Seg. 4
Pearson’s R
.976
.978
.960
.913
.961
Kendall’s tau
.853
.907
.798
.773
.867
Kendall’s tau for holdouts
1.000
1.000
1.000
.667
.833
5
Conclusions
Market segmentation plays an essential role in modern marketing issues and benefit segmentation is one of the suitable bases for segmenting customers based on their characteristics. In this study, a novel scheme has been presented for benefit segmentation
Customer-oriented benefit segmentation
185
of customers by using two-stage clustering, conjoint analysis and Delphi method. Conjoint analysis was introduced as a good market research tool for benefit segmentation and Delphi method was used for selecting the most important criteria which conjoint analysis was needed. A two-stage clustering method is employed to cluster consumers into different segments based on the main benefits derived from the conjoint study. The rapid advance in laptop technologies, especially the great adoption of laptop in daily life conducts many companies to bring on the market various types of laptop computers. Furthermore, the advances in technology directly influence users’ attitude towards these products. In this paper, Iran laptop market was studied. Results showed that there are differences between overall sample needs and customers in each segment. This means market segmentation should apply as a tool for better satisfying customer needs. Important differences among the four groups were price, processor speed, size and guarantee duration. An integrated methodology can provide real insights into laptop buyers’ decision processes that laptop designers should consider its use much more than they have in the past. However, in this survey Iranian laptop buyers were considered as a sample, it can be interesting to replicate the study on laptop users of different countries and compare the obtained results in a future work. Besides, future research could be using other clustering tools for clustering and segmentation. This study results showed that conjoint attributes and levels significantly influence the part-worth utilities and clusters. However in this paper the most important attributes and levels were selected based on the in-depth literature survey and Delphi method; another study can be identifying new attributes and comparison of the obtained results.
Acknowledgements The authors would like to thank the anonymous reviewers and the Editor-in-Chief of the IJBIR for their helpful comments in preparing this paper.
References Aghdaie, M.H. and Tafreshi, P.F. (2012) ‘Using two-stage clustering and conjoint analysis for benefit segmentation of Iranian laptop buyers’, Proceedings of the 3nd International Conference on Emergency Management and Management Sciences (ICEMMS 2012), pp.816–819, Beijing, China. Aghdaie, M.H. Zolfani, S.H. and Zavadskas, E.K. (2013) ‘Market segment evaluation and selection based on application of Fuzzy AHP and COPRAS-G methods’, Journal of Business Economics and Management, Vol. 14, No. 1, In press. Aghdaie, M.H., Zolfani, S.H. Rezaeniya, N. and Mehri-Tekmeh, J. (2011) ‘A hybrid fuzzy MCDM approach for market segments evaluation and selection’, Proceedings of the International Conference on Management and Service Science (MASS), pp.1–4, Zhengzhou, China. Akao, Y. and Mazur, G., (2003) ‘The leading edge in QFD: past, present and future’, International Journal of Quality and Reliability Management, Vol. 20, No. 1, pp.20–35. Ali, S.S. and Bharadwaj, R.K. (2010) ‘Factor analysis approach of decision making in Indian e-banking: a value adding consumer’s perspective’, International Journal of Business Innovation and Research, Vol. 4, No. 4, pp.298–320.
186
M.H. Aghdaie et al.
Anil, C., Carroll, D., Green, P.E. and Rotondo, J.A. (1997) ‘A feature-based approach to market segmentation via overlapping K-centroids clustering’, Journal of Marketing Research, Vol. 34, No. 3, pp.370–377. Bryant, D.L. and Abkowitz, M.D. (2007) ‘Estimation of chemical spill risk factors using a modified Delphi approach’, Journal of Environmental Management, Vol. 85, No. 1, pp.112–120. Büyüközkan, G. (2004) ‘Multi-criteria decision making for e-marketplace selection’, Internet Research, Vol. 14, No. 2, pp.139–154. Canever, M.D., Trijip, H.V. and Lans, I.V.D. (2007) ‘Benefit-feature segmentation: a tool for the design of supply-chain strategy’, Marketing Intelligence and Planning, Vol. 25, No. 5, pp.511–533. Cattin, P. and Wittinik, D.R. (1982) ‘Commercial use of conjoint analysis: a survey’, Journal of Marketing, Vol. 46, No. 3, pp.44–53. Chan, A.P.C., Yung, E.H.K., Lam, P.T.I., Tam, C.M. and Cheung, S.O. (2001) ‘Application of Delphi method in selection of procurement systems for construction projects’, Construction Management and Economics, Vol. 19, No. 7, pp.699–718. Chaturvedi, A., Carroll, J.D., Green, P.E. and Rotondo, J.A. (1997) ‘A feature-based approach to market segmentation via overlapping k-centroids clustering’, Journal of Marketing Research, Vol. 34, No. 3, pp.370–377. Chaudhuri, A. and Bhattacharyya, M. (2009) ‘A combined QFD and integer programming framework to determine attribute levels for conjoint study’, International Journal of Production Research, Vol. 47, No. 23, pp.6633–6649. Chiu, C.Y., Chen, Y.F., Kuo, I.T. and Ku, H.C. (2009)’An intelligent market segmentation system using k-means and particle swarm optimization’, Expert Systems with Applications, Vol. 36, No. 3, pp.4558–4565. Chiu, S. and Tavella, D. (2008) Data Mining and Market Intelligence for Optimal Marketing Returns, Butterworth-Heinemann, London. Chung, K.Y., Oh, S.Y., Kim, S.S. and Han, S.Y. (2004) ‘Three representative market segmentation methodologies for hotel guest room customers’, Tourism Management, Vol. 25, No. 4, pp.429–441. Dalkey, N. and Helmer, O. (1963) ‘An experimental application of the Delphi method to the use of experts’, Management Science, Vol. 9, No. 3, pp.458–467. Dibb, S. and Stern, P. (1995) ‘Questioning the reliability of market segmentation techniques’, Journal of Management Science, Vol. 23, No. 6, pp.625–636. Dickson, P.R. and Ginter, J.L. (1987) ‘Market segmentation, product differentiation, and marketing strategy’, Journal of Marketing, Vol. 51, No. 2, pp.1–10. Dillon, W.R., Kumar, A. and Borrero, M.S. (1993) ‘Capturing individual differences in paired comparisons: an extended BTL model incorporating descriptor variables’, Journal of Marketing Research, Vol. 30, No. 1, pp.42–45. Dubes, R. (1993) Cluster Analysis and Related Issue, World Science Publishing Company, NJ. Everitt, B.S. (1993) Cluster Analysis, Edward Arnold, London. Forgy, E.W. (1965) ‘Cluster analysis of multivariate data: efficiency versus interpretability of classifications’, Biometrics, Vol. 21, No. 3, pp.768–769. Frank, R.E., Massy, W.F. and Wind, Y. (1972) Market Segmentation, Prentice-Hall, Englewood Cliffs, NJ. Gan, G., Ma, C. and Wu, J. (2007) Data Clustering: Theory, Algorithms, and Applications, SIAM, Philadelphia. Geraghty, S. and Torres, A.N. (2009) ‘The Irish wine market: a market segmentation study’, International Journal of Wine Business Research, Vol. 21, No. 2, pp.143–154. Gil, J.M. and Sánchez, M. (1997) ‘Consumer preferences for wine attributes: a conjoint approach’, British Food Journal, Vol. 99, No. 1, pp.3–11.
Customer-oriented benefit segmentation
187
Green, P.E. and Krieger, A.M. (1991) ‘Segmenting markets with conjoint analysis’, Journal of Marketing, Vol. 55, No. 4, pp.20–31. Green, P.E. and Srinivasan, V. (1978a) ‘Conjoint analysis in consumer research: issues and outlook’, Journal of Consumer Research, Vol. 5, No. 2, pp.103–123. Green, P.E. and Srinivasan, V. (1978b) ‘Conjoint analysis in marketing: new development with implications for research and practice’, Journal of marketing, Vol. 54, No. 4, pp.3–19. Gustafsson, A. Herrmann, A. and Huber, F. (2003) Conjoint Measurement: Methods and Applications, Springer, Berlin. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1995) Multivariate Data Analysis, Prentice Hall, Upper Saddle River. Halbrendt, C.K., Wirth, J.R. and Vaughn, J. (1991) ‘Conjoint analysis mid-Atlantic food-fish market farm-raised hybrid striped bass’, Southern Journal of Agricultural Economics, Vol. 23, No. 1, pp.155–163. Haley, R. (1968) ‘Benefit segmentation: a decision-orientated research tool’, Journal of Marketing, Vol. 32, No. 3, pp.30–35. Hanafizadeh, P. and Mirzazadeh, M. (2011) ‘Visualizing market segmentation using self-organizing maps and fuzzy Delphi method-ADSL market of a telecommunication company’, Expert Systems with Applications, Vol. 38, No. 1, pp.198–205. Hardle, W. and Simar, L. (2003) Applied Multivariate Statistical Analysis, Springer, New York. Hemalatha, M. (2011) ‘Segmentation of clearance sales shoppers based on store attributes’, International Journal of Business Innovation and Research, Vol. 5, No. 6, pp.639–662. Hemalatha, M., Sivakumar, V.J. and David Sam Jayakumar, G.S. (2009) ‘Segmentation of Indian shoppers based on store attributes’, International Journal of Business Innovation and Research, Vol. 3, No. 6, pp.651–669. Hong, C.H. (2012) ‘Using the Taguchi method for effective market segmentation’, Expert Systems with Applications, Vol. 39, No. 5, pp.5451–5459. Hung, C. and Tsai, C. (2008) ‘Market segmentation based on hierarchical selforganizing map for markets of multimedia on demand’, Expert Systems with Applications, Vol. 34, No. 1, pp.780–787. Jaeger, S.R., Hedderley, D. and MacFie, H.J.H. (2001) ‘Methodological issues in conjoint analysis: a case study’, European Journal of Marketing, Vol. 35, Nos. 11/12, pp.1217–1239. Jain, A.K. (2011) ‘Data clustering: 50 years beyond K-means’, Pattern Recognition Letters, Vol. 31, No. 8, pp.651–666. Jain, A.K. and Dubes, R.C. (1988) Algorithms for Clustering Data, Englewood Cliffs, Prentice-Hall, NJ. Jain, A.K., Murty, M.N. and Flynn, P.J. (1999) ‘Data clustering: a review’, ACM Computing Surveys, Vol. 31, No. 3, pp.264–323. Kamakura, W.A. (1988) ‘A least squares procedure for benefit segmentation with conjoint experiments’, Journal of Marketing Research, Vol. 25, No. 2, pp.157–167. Kotler, P. (1980) Marketing Management – Analysis, Planning, and Control, 4th ed., Prentice-Hall, NJ. Kotler, P. (1999) Marketing Management: Analysis, Planning, Implementation, and Control, 10th ed., Englewood Cliffs, Prentice-Hall, NJ. Kotler, P. (2011) Principles of Marketing, 14th ed., Prentice-Hall, NJ. Kotler, P. (2001) Marketing Management, Millennium ed., Prentice-Hall, Inc. Kuo, R.J., Ho, L.M. and Hu, C.M. (2002) ‘Integration of self-organizing feature map and K-means algorithm for market segmentation’, Computers and Operations Research, Vol. 29, No. 11, pp.1475–1493.
188
M.H. Aghdaie et al.
Lee, G., Morrison, A.M. and O’Leary, J.T. (2006) ‘The economic value portfolio matrix: a target market selection tool for destination marketing organizations’, Tourism Management, Vol. 27, No. 4, pp.576–588. Lee, T-R., Lin, C-H., Dadura, A.M. and Ganesh, K. (2012) ‘Promotion of economic animal welfare by market force: a case study’, International Journal of Business Innovation and Research, Vol. 6, No. 3, pp.302–321. Linstone, H. and Turoff, M. (1975) ‘The Delphi method: techniques and applications’, Addison-Wesley, London. Loker, L. and Perdue, R. (1992) ‘A benefit-based segmentation of a nonresident summer travel market’, Journal of Travel Research, Vol. 31, No. 1, pp.30–35. Luce, R.D. and Tukey, J.W. (1964) ‘Simultaneous conjoint measurement-a new type of fundamental measurement’, Journal of Mathematical Psychology, Vol. 1, No. 1, pp.1–27. MacQueen, J.B. (1967) ‘Some methods for classification and analysis of multivariate observations’ Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp.281–297, Berkeley, USA. Malhotra, N.K. (1993) Marketing Research – An Applied Orientation, Prentice-Hall, Englewood Cliffs. McDonald, M. (1995) Marketing Plans, Butterworth-Heinemann,Oxford. McDonald, M. and Dunbar, I. (1998) Market Segmentation, Palgrave Macmillan, Basingstoke. McDonald, M. and Dunbar, I. (2004) Market Segmentation: How to Do It, How to Profit from It, Elsevier Butterworth-Heinemann, Oxford. McFadden, D. (1986) ‘The choice theory approach to market research’, Marketing Science, Vol. 5, No. 4, pp.275–297. Melnyk, S.A., Lummus, R.R., Vokurka, R.J., Burns, L.J. and Sandor, J. (2009) ‘Mapping the future of supply chain management: a Delphi study’, International Journal of Production Research, Vol. 46, No. 16, pp.4629–4653. Milligan, G.W. and Cooper, M.C. (1985) ‘An examination of procedures for determining the number of clusters in a data set’, Psychometrika, Vol. 50, No. 2, pp.159–179. Mingoti, S.A. and Lima, J.O. (2006) ‘Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms’, European Journal of Operational Research, Vol. 174, No. 3, pp.1742–1759. Mirkin, B. (2005) Clustering for Data Mining A Data Recovery Approach, Chapman and Hall/CRC, London. Morrison, A. (1996) Hospitality and Travel Marketing, 2nd ed., Albany, Delmar Publishers, NY. Murry, J.W. and Hammons, J.O. (1995) ‘Delphi: a versatile methodology for conducting qualitative research’, The Review of Higher Education, Vol. 18, No. 4, pp.423–436. Naes, T., Kubberod, E. and Sivertsen, H. (2001) ‘Identifying and interpreting market segments using conjoint analysis’, Food Quality and Preference, Vol. 12, No. 2, pp.133–143. O’Connor, G.C. and O’Keefe, B. (1997) ‘Viewing the web as a marketplace: the case of small companies’, Decision Support Systems, Vol. 21, No. 3, pp.171–183. Okoli, C. and Pawlowski, S.D. (2004) ‘The Delphi method as a research tool: an example, design considerations and application’, Information and Management, Vol. 42, No. 1, pp.15–29. Orme, B. (2005) Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research, Research publishers LLC, New York. Paliwoda, S.J. (1983) ‘Predicting the future using Delphi’, Management Decision, Vol. 21, No. 1, pp.31–38. Punj, G. and Stewart, D.W. (1983) ‘Cluster analysis in marketing research: review and suggestions for application’, Journal of Marketing Research, Vol. 20, No. 2, pp.134–148. Raghavarao, D. Wiley, J.B. and Chitturi, P. (2010) Choice-Based Conjoint Analysis Models and Designs, Chapman & Hall, London.
Customer-oriented benefit segmentation
189
Robinson, J. (1938) The Economics of Imperfect Competition, McMillan, London. Rohrbaugh, J.I. (1979) ‘Improving the quality of group judgments: social judgment analysis and the Delphi technique’, Organizational Behavior and Human Performance, Vol. 24, No. 1, pp.73–92. Scott, G. (2001) ‘Strategic planning for high-tech product development’, Technology Analysis and Strategic Management, Vol. 13, No. 3, pp.343–64. Scott, G. and Walter, Z. (2003) ‘DELPHI findings about internet systems problems, with implications for other technologies’, Technology Analysis and Strategic Management, Vol. 15, No. 1, pp.103–115. Sharma, S. (1996) Applied Multivariate Techniques, Wiley, New York. Skulmoski, G.J., Hartman, F.T. and Krahn, J. (2007) ‘The Delphi method for graduate research’, The Journal of Information Technology Education, Vol. 6, pp.1–21. Smith, W.R. (1956) ‘Product differentiation and market segmentation as alternative marketing strategies’, Journal of Marketing, Vol. 21, No. 1, pp.3–8. Tavana, M., Pirdashti, M., Dennis, D.T., Belaud, J.P. and Behzadian, M. (2012) ‘A hybrid Delphi-SWOT paradigm for oil and gas pipeline strategic planning in Caspian Sea basin’, Energy Policy, Vol. 40, pp.345–360. Trinh, G., Dawes, J. and Lockshin, L. (2009) ‘Do product variants appeal to different segments of buyers within a category?’, Journal of Product and Brand Management, Vol. 18, No. 2, pp.95–105. Tsai, H.Y., Chang, C.W. and Lin, H.L. (2010) ‘Fuzzy hierarchy sensitive with Delphi method to evaluate hospital organization performance’, Expert Systems with Applications, Vol. 37, No. 8, pp.5533–5541. Venkata Subbaiah, K., Durga Prasad, K.G. and Narayana Rao, K. (2011) ‘Customer-driven product planning using conjoint analysis and QFD–ANP methodology’, Vol. 7, No. 3, pp.374–394. Verbeke, W., Vermeir, I. and Brunsø, K. (2007) ‘Consumer evaluation of fish quality as bases for fish market segmentation’, Food Quality and Preference, Vol. 18, No. 4, pp.651–661. Vidal, L.A., Franck Marle, F. and Bocquet, J.C. (2011) ‘Using a Delphi process and the analytic hierarchy process (AHP) to evaluate the complexity of projects’, Expert Systems with Applications, Vol. 38, No. 5, pp.5388–5405. Wang, X. (2011) ‘Two heuristics to the share-of-choice product line design problem’, International Journal of Operational Research, Vol. 10, No. 2, pp.180–193. Ward, J. (1963) ‘Hierarchical grouping to optimize an objective function’, Journal of the American Statistical Association, Vol. 58, No. 301, pp.236–244. Wedel, M. and Kamakura, W.A. (2001) Market Segmentation Conceptual and Methodological Foundations, Kluwer Academic Publishers, New York. Weinstein, A. (1994) Market Segmentation: Using Demographics Psychographics and Other Niche Marketing Techniques to Predict and Model Consumer Behaviour, Probus Publishing, Chicago. Wind, Y. (1978) ‘Issues and advances in segmentation research’, Journal of Marketing Research, Vol. 15, No. 3, pp.317–337. Wu, R. and Wunsch, D. (2009) Clustering, Wiley and Sons, New Jersey.
