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This paper discusses the dos and don'ts of market segmentation analysis. Market segmentation analysis is younger than the journal Tourism Review, but nevertheless has a rich history in tourism research and continues to be extensively used by both tourism researchers and industry.

After a brief overview of the origins of market segmentation analysis and its uptake in tourism, a number of key considerations are discussed, which are critical to ensuring that practically useful and reliable market segments emerge from the analysis.

Do accept that market segmentation is exploratory. Do spend a lot of time ensuring you collect high-quality data. Don’t use ordinal data. Don’t use correlated variables. Do ensure your sample size is large enough. Don’t use factor-cluster analysis. Do conduct data structure analysis. Don’t complicate things.

This is a perspective study; it offers a concise discussion of key issues in market segmentation analysis and directs the interested reader to resources where they can learn more about each of these issues.

It is important to remain creative when conducting segmentation research, as many different ways to segment a market can exist. Five main bases are discussed: geographic, demographic, psychographic, behaviouristic and image. This is followed by an overview of the main techniques used to establish and verify segments, including automatic interaction detector, conjoint analysis, multidimensional scaling and canonical analysis.

The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

The underlying managerial rationale for segmenting markets is well established, with the marketing literature citing a range of benefits for businesses adopting a segmentation approach. Yet organisations frequently encounter difficulties in implementing segmentation principles. Even in the industrial marketing literature, where the most practical implementation guidance is offered, it is suggested that organisations tend to over‐emphasise the mechanics of segmentation, while failing to correctly implement the findings. This suggests that, if organisations are to benefit from applying segmentation principles, two fundamental questions should be addressed. The first concerns the basic conditions which must be met if implementation is to be effected. That is, it must be possible to map the dimensions developed on to usable customer characteristics. The second concerns the costs and benefits of the segmentation solution in relation to the proportion of variance in customer requirements which it explains. Addresses these questions in an industrial marketing context, using a literature review and quantitative analysis of data from the European car parts after market. The analysis shows that whilst traditional segmentation methods can be used to identify certain segments, these segments do not readily map on to implementable dimensions. Furthermore, it is suggested that even a relatively wide use of structural independent variables explains only a very small proportion of the individual variability in customer requirements. This suggests that much segmentation analysis may be poorly directed and also of rather limited practical value.

Organizations wishing to apply the principles of market segmentation often face problems putting the theory into practice. All too often the required background analysis is inadequate or poorly structured or the translation of segmentation strategy into marketing programs is impeded. To be successful, segmentation must lead an organization through a process which undertakes background analysis, determines strategy and develops marketing programs. However, there are a number of points at which the process can break down. Shows how the segmentation program described has tackled these difficulties, leading several management teams through the analysis, strategy and program elements of the market segmentation process. A range of benefits arise from the program. Primary benefits are that the process puts the customer first, maximizes resources and emphasizes strengths over competitors. Secondary benefits relate to the development of a more market‐focussed company culture and the building of inter‐ and intra‐organizational relationships.

Demonstrates the usefulness of combining retail market segmentation with competitive analysis as a very effective method to understand the dynamics of retail markets and to analyse strategic options for supermarket chains. Attempts to advance the market segmentation research by narrowing the gap between the academically oriented research on segmentation and the practical application of segmentation research. Applies the recommended methodology to a largescale investigation and discusses the empirical findings along with strategic implications for supermarket retail chain organizations.

Despite the well‐documented benefits which segmentation offers, businesses continue to encounter implementation difficulties. This raises concerns about the cause of these problems and how they might be overcome. These concerns are addressed in this paper in the form of three questions: Is segmentation a good idea? If segmentation is such a good idea, why does it sometimes fail? What can be done to reduce the chance of failure? A mix of published evidence and case examples is used to explore these questions. The paper concludes by suggesting that if marketers are to overcome their segmentation implementation difficulties, they need practical guidance at three stages in the segmentation process. Before the project begins they must understand the role of success factors contributing to a successful result. During the segmentation project the qualities of the emerging segments must be clarified. After segmentation is complete the question of segment attractiveness must be considered. There is currently a gulf between the priorities of academics and practitioners carrying out segmentation. If this is to be bridged, further research is needed to provide guidance on segmentation success factors.

The purpose of this paper is to determine the best approach to customer segmentation and to extrapolate associated rules for this based on recency, frequency and monetary (RFM) considerations as well as demographic factors. In this study, the impacts of RFM and demographic attributes have been challenged in order to enrich factors that lend comprehension to customer segmentation. Different types of scenario were designed, performed and evaluated meticulously under uniform test conditions. The data for this study were extracted from the database of a global pizza restaurant chain in Turkey. This paper summarizes the findings of the study and also provides evidence of its empirical implications to improve the performance of customer segmentation as well as achieving extracted rule perfection via effective model factors and variations. Accordingly, marketing and service processes will work more effectively and efficiently for customers and society. The implication of this study is that it explains a clear concept for interaction between producers and consumers.

Customer relationship management, which aims to manage record and evaluate customer interactions, is generally regarded as a vital tool for companies that wish to be successful in the rapidly changing global market. The prediction of customer behaviors is a strategically important and difficult issue because of the high variance and wide range of customer orders and preferences. So to have an effective tool for extracting rules based on customer purchasing behavior, considering tangible and intangible criteria is highly important. To overcome the challenges imposed by the multifaceted nature of this problem, the authors utilized artificial intelligence methods, including k-means clustering, Apriori association rule mining (ARM) and neural networks. The main idea was that customer clusters are better enhanced when segmentation processes are based on RFM analysis accompanied by demographic data. Weighted RFM (WRFM) and unweighted RFM values/scores were applied with and without demographic factors and utilized to compose different types and numbers of clusters. The Apriori algorithm was used to extract rules of association. The performance analyses of scenarios have been conducted based on these extracted rules. The number of rules, elapsed time and prediction accuracy were used to evaluate the different scenarios. The results of evaluations were compared with the outputs of another available technique.

The results showed that having an appropriate segmentation approach is vital if there are to be strong association rules. Also, it has been determined from the results that the weights of RFM attributes affect rule association performance positively. Moreover, to capture more accurate customer segments, a combination of RFM and demographic attributes is recommended for clustering. The results’ analyses indicate the undeniable importance of demographic data merged with WRFM. Above all, this challenge introduced the best possible sequence of factors for an analysis of clustering and ARM based on RFM and demographic data.

The work compared k-means and Kohonen clustering methods in its segmentation phase to prove the superiority of adopted segmentation techniques. In addition, this study indicated that customer segments containing WRFM scores and demographic data in the same clusters brought about stronger and more accurate association rules for the understanding of customer behavior. These so-called achievements were compared with the results of classical approaches in order to support the credibility of the proposed methodology. Based on previous works, classical methods for customer segmentation have overlooked any combination of demographic data with WRFM during clustering before proceeding to their rule extraction stages.

Since its conception over 60 years ago by Frederick in 1934, the concept of segmentation has gained increasing importance, in both the consumer and the business domains. Examination of research within the latter domain indicates that, although considerable amounts of research have been carried out, these efforts appear to focus on sub‐areas of segmentation such as the development of segmentation bases and models, at the expense of a more strategic view. This not only has resulted in a diffused understanding of the subject‐matter but also is posited to have slowed the progress of theory development and research in business segmentation. Presents a comprehensive conceptualisation of business segmentation in the form of an integrating framework of business segmentation, aimed at raising new research agendas which could lead to a better understanding of existing gaps between theory and implementation and better recommendations to practitioners and assisting further development of theory in business segmentation.

This study aims to capture the views of executives about the merit of using the two segmentation approaches (quantitative vs qualitative). Furthermore, this study aimed to examine costs and benefits for two different segmentation approaches, using a minimax simple cost-benefit analysis (CBA) matrix.

A total of 16 semistructured interviews were conducted with executives within the University of Sharjah (UoS). Furthermore, a minimax approach was applied to the CBA study.

Evidence in this study found that the financial cost of quantitative segmentation approaches was higher than qualitative approaches. However, the decision-makers trusted the quantitative approach more regardless of the incurred costs. The study also found that there was a limited knowledge about social marketing and segmentation among executives.

Limitations of this study relate to the methodology applied, the sample selected and the lead research. Another factor is selection bias, which limited this study to one organization’s executives. It is conceivable that middle-level management would have had the desire to participate because they make the recommendations to top management in decision-making. The researcher did not collect precise data on time taken to design, implement and analyses the two segmentation studies, which qualified the precision of the CBA. Also, the fact that the sample includes participants from a relatively narrow range of disciplines should be noted as a limitation of the study.

The current study provides a case study demonstrating how CBA provides a dollar amount estimate permitting alternate segmentation approaches to be compared and contrasted, assisting in the value estimation of any social marketing project.

The paper draws upon two streams of the literature: social marketing and CBA. The paper focused on the understanding of the literature, CBA offers a technique applicable to demonstrating cost savings that can be derived from choosing one method over another. Moreover, CBA assists in understanding the benefits or potential opportunity cost both financially and nonfinancially.

This paper presents one of the first studies conducting a CBA to compare and contrast two segmentation approaches in social marketing. The study provides interesting insights into the perceptions of management executives over alternative research methods, although the results are limited to a case study.