Searching for latent class segments in technological services

The focus in this article is on uncovering segments that describe mobile phone users and on investigating the nature of these segments to get some relevant information. We use a phased approach to reach this objective. First, two segmentation bases have been compared in order to select the best variables for the identification of groups. This is done using a latent class modelling approach. Second, two different segmentation techniques are examined looking for the best model fit. Finally, the model that achieves the best results is applied to the most significant segmentation variables with the purpose of uncovering segments that describe mobile phone users.     

Using segment level stability to select target segments in data-driven market segmentation studies

Market segmentation is widely used by industry to select the most promising target segment. Most organisations are interested in finding one or a small number of target segments to focus on. Yet, traditional criteria used to select a segmentation solution assess the global quality of the segmentation solution. This approach comes at the risk of selecting a segmentation solution with good overall quality criteria which, however, does not contain groups of consumers representing particularly attractive target segments. The approach we propose helps managers to identify segmentation solutions containing attractive individual segments (e.g., more profitable), irrespective of the quality of the global segmentation solution. We demonstrate the functioning of the newly proposed criteria using two empirical data sets. The new criteria prove to be able to identify segmentation solutions containing individual attractive segments which are not detected using traditional quality criteria for the overall segmentation solution.     

Segmentation of both reviewers and businesses on social media

Employing online consumer reviews, this research develops a market segmentation procedure that is feasible to businesses present on social media. Because online reviews typically encompass large numbers of both reviewers and businesses, this data structure allows for both reviewer segmentation and business segmentation. This two-side segmentation approach segments not only reviewers in the preferences expressed in their reviews, but also businesses in their business practices specified in the reviews. Whereas common existing segmentation approaches predominantly use survey and transaction data, the proposed procedure uses publicly available and detailed consumption information in such reviews. A large number of product features elicited from such reviews lead to rich and detailed profiling of both reviewer segments and business segments. Using restaurant reviews on Yelp, this research demonstrates how the proposed procedure can help businesses develop segmentation strategies on social media.     

Conditional market segmentation by neural networks: a Monte-Carlo study

An artificial neural network (ANN) algorithm is proposed that incorporates both market segmentation and discriminant (regression) analysis of the segments. The method simultaneously estimates the models relating consumer characteristics to market segments, i.e., subjects are assigned to (unique) segments so that subjects within a class show similar purchase behavior and share the same characteristics (psychographics/sociodemographics). Parameters of all models are estimated by the backpropagation algorithm. The performance of the ANN methodology is assessed in a Monte-Carlo study. In contrast to the usual stepwise approach adopted in segmentation studies, our study found that simultaneous segmentation and discrimination are preferable for finding an overall optimum in that this way clusters are formed not only to create homogeneous submarkets but also to show a good dicriminatory behavior.     

Speaking the same language: the relevance of neuro‐linguistic programming to effective marketing communications

Individuals process each and every encounter with the external environment using sensory system representations (pictures, sounds, feelings, etc.). Studies of neuro‐linguistic programming have indicated that each individual tends to have a preferred sensory representational system with which they will internally code these experiences. Market segments are comprised of individuals who, although possessing similarities across traditional segmentation bases, also have different preferred sensory modalities. Therefore, if consumers within any given target market segment do not all talk the same sensory language, marketers are failing to get their message across to everyone within that segment. This study is concerned with examining the efficacy of television advertising in communicating to those within a target group by exploring the links between the advertisements that ‘speak’ to them most and their preferred representational systems. It finds that participant reactions to those advertisements that they regarded as having the most effect were expressed predominantly in language relating to their preferred sensory representational systems. Furthermore, where participants with different representational systems chose the same advertisement as having the most effect, the reasons for their choice differed on a sensory basis.     

A Preliminary Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting

ABSTRACT

This practitioner note proposes a new approach considering two-stage clustering and LRFMP model (Length, Recency, Frequency, Monetary and Periodicity) simultaneously for customer segmentation and behavior analysis and applies it among the Iranian Fintech companies. In this practitioner note, the K-means clustering algorithm and LRFMP model are combined in the customer segmentation process. After initial clustering, for a better understanding of valuable customers, additional clustering is implemented in segments that needed further investigation. This approach contributes to a better interpretation of different customer segments. Customer segments, consisting of 23524 business customers are analysed based on their characteristics and appropriate strategies are recommended accordingly. The first stage clustering result shows that customers are best segmented into four groups. The first and fourth segments are clustered again and the final 11 groups of customers are determined. This note provides a systematic and practical approach for researchers and practitioners for segmentation, interpretation, and targeting of customers especially in the B2B setting and the Fintech industry and helps managers to make effective marketing strategies and enhance customer relationship and marketing intelligence.     

Coupon characteristics and redemption intentions: A segment-level analysis

The authors investigate how different segments of consumers react to different coupon characteristics, such as face value and method of distribution. They utilize a latent segmentation approach to identify the underlying segments. The empirical analysis suggests that different segments of consumers place varying emphasis with regard to economic benefits, psychic benefits, effort costs, and substitution costs. A further examination of the derived segments with respect to consumer correlates such as psychological, attitudinal, behavioral, and demographic characteristics reveals that coupon-related consumer characteristics, rather than demographics, exhibit significant and meaningful differences across these segments. Implications of the segment-level analysis for evaluating coupon drops and managing promotional expenditures are also discussed. © 1998 John Wiley & Sons, Inc.     

Customer Lifetime Value as the Basis of Customer Segmentation

SUMMARY

In this article, we examine current trends in customer life-time value and customer segmentation models and identify key issues for future research. CLV-based segmentation is a segmentation approach that groups customers into meaningful segments based upon customer lifetime value and (potentially) other factors. In the article, we discuss the extent to which CLV-based segmentation meets the criteria for effective segmentation. We also identify six areas for future research: (1) models and management of “micro-segments,” (2) using CLV-based segmentation to improve the efficiency of marketing programs, (3) the need for more dynamic CLV-based segmentation models, (4) applying CLV-based customer segmentation to new products and new customers, (5) challenges associated with implementing CLV-based segmentation, and (6) the need for new models that enable firms to segment customers by response to marketing activities and CLV at different points in the customer decision process.     

A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data

We consider the problem of simultaneously performing market segmentation and predicting product choice for a given population of consumers. This behaviour-based segmentation model uses several types of information characterizing individual consumers and their market context: sociodemographic, psychographic and product choice data. These data are combined in a normative conceptual framework that posits the formation of latent segments, each of which displays differing degrees of sensitivity to relevant product attributes. This permits results obtained by the proposed model structure to differ significantly from those of traditional segmentation techniques, which usually produce a set of groups characterized by smaller within-group than between-group variation in sociodemographic and/or psychographic variables. By making the segments behaviour-based, it is argued that the segments produced by the model will be more actionable and more directly relevant to management decision-making. The proposed model is applied to a data set involving choice of a beauty product offered by five national brands. The results show that the explanatory power of the latent segmentation and choice model is far greater than that of the MNL model, which is nested within our more general specification. The types of results available from the model are illustrated through the application.     

Brand morphing across Wal-Mart customer segments

The premise of segmentation theory is that different segments each have a discrete customer profile and behavioral characteristics. At a conceptual level, the recent branding literature recognizes that different sub-cultures or segments could experience different meanings of an organization's brand. However, few quantitative studies address the issue. The current paper combines branding and segmentation theory and offers a new perspective on whether all segments have the same brand meaning. A leading discount retailer, Wal-Mart, is the focus of this Canadian-based investigation. Two segments of Wal-Mart customers are the basis of the study — one segment preferring Wal-Mart and one less attached. The research quantifies the two networks of brand meaning that the two segments associate with the Wal-Mart (corporate) brand. Empirically, brand morphing of the corporate brand occurs, with different brand meanings across the two segments.     

Evaluating the Appropriateness of Market Segmentation Solutions Using Artificial Neural Networks and the Membership Clustering Criterion

The appropriateness of a given segmentation solution is a key consideration in all marketing segmentation studies. By appropriate, it is meant that not only has the optimal segmentation solution been identified, but also that the proper number of segments to market to has been correctly specified. This research focuses on the second, and more fundamental, issue of determining the appropriate number of segments in a marketplace. If the appropriate number of segments is over-specified, marketers may over-segment the market and treat audience segments separately that could effectively be treated inclusively. Conversely, if the appropriate number of segments is under-specified, marketers may under-segment the market and fail to identify distinct, viable segments that should be marketed to separately.The issue of market under- and over-segmentation may be addressed with the membership clustering criterion (MCC), an analytical technique based on fuzzy sets derived from artificial neural networks (mathematical models of animal nervous systems). Using artificial and real world data sets, we empirically test the MCC, compare it to existing methods for determining the number of segments in a market, and demonstrate its advantages in evaluating the appropriateness of marketing to different numbers of market segments.     

Identifying multi-channel value co-creator groups in the banking industry

The purpose of this paper is providing a value co-creation management framework in the banking industry using the data analysis. Moreover, a multi-channel segmentation approach will be developed in order to identify customer segments based on the use of each channel. Managing value co-creation can be defined as determining the channel that may be used by the customer and the kinds of encounters that lie in these channels to have different impacts on the customer for various types of encounters. The model is built on the basis of the related literature and the collaboration of a Customer Relationship Manager in a large bank in Iran. Then, a multi-channel segmentation model is developed based on the RFM variables of the five banking channels for each customer. Next, about 11,000 customers of the bank is segmented by comparing the k-means and DBSCAN algorithms. Finally, by adapting customer segments on the value co-creation framework, three general groups are identified based on the type of encounter that lies in each channel. Moreover, twenty-seven small groups are recognized based on the desirability of the customer use of the channels.     

Segments of sustainable food consumers: a literature review

Sustainable food consumption is an important aspect of sustainable development. When adopting a sustainable food lifestyle, consumers are confronted with complex choices. Today's food consumption is too complex to be explained by socio‐demographic factors exclusively. A broader perspective is needed. In order to explain behaviour across different consumer segments better, relatively homogenous segments of food consumers were identified by segmenting food consumers based on a wide range of variables. The current study aims to provide an overview of published studies that have segmented consumers with regard to sustainable food consumption. The literature review has been conducted by searching SciVerse Scopus for all relevant articles available until November 2010. The main criterion for including a specific study was the inclusion of empirical analyses of primary data, resulting in consumer segments with regard to sustainable food consumption. Sixteen articles were incorporated in the final analysis. First, the variables used for segmentation and profiling in the included articles have been categorized into three levels of abstraction: personality characteristics, food‐related lifestyles and behaviour. The three levels of abstraction proved to be helpful in categorizing the segmentation studies. The findings indicate that variables on all three levels were efficient in differentiating consumer segments regarding sustainability. In addition, the importance of price and health differed across the segments, although these variables are only indirectly related to sustainability. Second, the three most frequently identified consumer segments with regard to sustainable food consumption were: ‘greens’, ‘potential greens’ and ‘non‐greens’. These segments differed from one another on all three levels of abstraction. This implies that future segmentation studies should include variables on all levels of abstraction to get a complete picture of existing sustainable consumer segments. Marketers should be aware that targeting specific segments based on socio‐demographic variables exclusively is not sufficient. Personality characteristics, lifestyle and behaviour are all important to take into consideration. Attempts at stimulating sustainable consumption might be most effective when differences across consumer segments are taken into account. Future research is needed to explore the characteristics of different sustainable food consumer segments with respect to their potential contributions in promoting sustainable development.     

Acquiring Potential Blood Donors in Large Cities: A Preference-Based Donor Segmentation Study

In contrast to previous segmentation studies that apply mostly demographic segmentation criteria, this study presents a behavioral segmentation approach to explain how potential blood donors in large cities can be most effectively clustered and acquired. The authors conduct a representative online panel study among 2,062 potential blood donors living in the four largest cities of Germany—Berlin, Hamburg, Munich, and Cologne. Applying explorative factor analysis and cluster analysis, the study identifies and describes seven preference-based blood donor segments. Using the segmentation results, the authors derive several blood donor acquisition strategies that are directly linked to the identified preferences.     

The individual dual needs for assimilation and distinctiveness as a segmentation mechanism: developing the concept among students

The present study proposes that the individual's needs for assimilation and distinctiveness serve as a valuable mechanism for consumer segmentation and suggests that augmenting the List of Values with these dual needs enriches the understanding of consumer responses to marketing strategies. Results of a study using a sample of 536 students in an Israeli university identified five segments with significantly different needs for assimilation and differentiation. These segments showed different preferences for marketing communications and distribution channels in their purchasing decision of a cell phone. Mass media and website usage influenced segments marked by high assimilation, while word of mouth influenced segments characterized by high distinctiveness. Augmenting the List of Values with dual needs based segmentation moderated students’ tendencies to favour/dislike marketing communications and distribution channels.     

Customer segmentation using unobserved heterogeneity in the perceived-value–loyalty–intentions link

Multiple facets of perceived value perceptions drive loyalty intentions. However, this value–loyalty link is not uniform for all customers. In fact, the present study identifies three different segments that are internally consistent and stable across different service industries, using two data sets: the wireless telecommunication industry (sample size 1122) and the financial services industry (sample size 982). Comparing the results of a single-class solution with finite mixture results confirms the existence of unobserved customer segments. The three established segments are “rationalists”, “functionalists” and “value maximizers”. These results point the way for value-based segmentation in loyalty initiatives and reflect the importance of a multidimensional conceptualization of perceived value, comprising cognitive and affective components. The present results substantiate the fact that assuming a homogeneous value–loyalty link provides a misleading view of the market. The paper derives implications for marketing research and practice in terms of segmentation, positioning, loyalty programs and strategic alliances.     

Market segmentation

Market segmentation is a crucial marketing strategy. Its aim is to identify and delineate market segments or “sets of buyers” which would then become targets for the company's marketing plans. The advantage to marketing management is that this technique divides total demand into relatively homogeneous segments which are identified by some common characteristics. These characteristics are relevant in explaining and in predicting the response of consumers, in a given segment, to marketing stimuli.

The market can be subdivided by geographic, demographic, psychological, psycho‐graphic or behavioural variables. The advantages and disadvantages of each of these types of segmentation variables are discussed in detail in this paper. Kotier (1984) has identified four requirements that a marketer can use in evaluating the desirability of potential market segments, namely measureability, accessibility, substantiality and actionability. Once a segment has been identified which meets these requirements, it is possible to develop a product or service which meets the unfulfilled needs of this segment. A marketing mix can then be devised to reach the segment identified economically and efficiently. A strategy of market segmentation attempts to regain some of the benefits of the closer association with customers which was the strength of traditional business operations.     

Latent class modeling of website users’ search patterns: Implications for online market segmentation

Appropriate modeling of web use patterns may yield very relevant marketing and retailing information. We propose using a model-based clustering approach for market segmentation based on website users’ search patterns. We not only provide a detailed discussion of technical issues such as the problem of the selection of the number of segments, but also a very interesting empirical illustration of the potentials of the proposed approach.