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.     

Integration of Self-Organizing Feature Maps and Genetic-Algorithm-Based Clustering Method for Market Segmentation

Clustering analysis has been widely applied in the area of market segmentation. Conventional research usually uses the multivariate analysis method. Recently, due to promising results of computational intelligence techniques in engineering, they are also considered for market segmentation. Among them, genetic algorithms (GAs) are theoretically and empirically found to provide globally near-optimal solutions for various complex optimization problems. Because GA is good at searching, it can cluster the data according to their similarities. In addition, artificial neural networks also have high performance in both engineering and management. Hence, this research proposes a novel 2-stage method, which first uses self-organizing feature maps (SOMs) to determine the number of clusters and then employs a GA-based clustering method to find the final solution (it is defined as S + G in this research). The results of simulated data via a Monte Carlo study show that the proposed method outperforms the other 2 methods: K means, which uses SOM to determine the number of clusters, and SOM followed by K means, based on both within-cluster variations (SSW) and the number of misclassifications. To further verify the proposed approach, a real-world problem, wireless telecommunications industry market segmentation, is employed. The results also show that the proposed method has the lowest SSW among the 3 methods.     

Scenario-based strategy maps

Strategy maps are designed to help execute strategy and bring predictive qualities to key performance indicators by linking them according to perceived cause-and-effect relationships. However, in our experience strategy maps are often extrapolations of past performance and are seldom sufficiently linked to possible future states. In this article, we argue that scenario analysis could play an important role in the design of strategy maps, as it is an effective method to look at the future. Through the development of scenarios, organizations can think creatively about possible discontinuous future states and can prepare themselves for multiple plausible futures, not only the one they expect to happen. Therefore, scenario-based strategy maps could enable organizations to face strategic uncertainty in a more effective way and make them more sustainable in the longer term. This article highlights the strengths and weaknesses of strategy maps and scenario analysis, and outlines a method to develop scenario-based strategy maps both in theory and by presenting a significant example.     

A spatial interaction model for deriving joint space maps of bundle compositions and market segments from pick-any/J data: An application to new product options

We propose an approach for deriving joint space maps of bundle compositions and market segments from three-way (e.g., consumers x product options/benefits/features x usage situations/scenarios/time periods) pick-any/J data. The proposed latent structure multidimensional scaling procedure simultaneously extracts market segment and product option positions in a joint space map such that the closer a product option is to a particlar segment, the higher the likelihood of its being chosen by that segment. A segment-level threshold parameter is estimated that spatially delineates the bundle of product options that are predicted to be chosen by each segment. Estimates of the probability of each consumer belonging to the derived segments are simultaneously obtained. Explicit treatment of product and consumer characteristics are allowed via optional model reparameterizations of the product option locations and segment memberships. We illustrate the use of the proposed approach using an actual commercial application involving pick-any/J data gathered by a major hi-tech firm for some 23 advanced technological options for new automobiles.     

Country and consumer segmentation: multi-level latent class analysis of financial product ownership

The financial services sector has internationalized over the last few decades. Important differences and similarities in financial behavior can be anticipated between both consumers within a particular country and those living in different countries. For companies in this market, the appropriate choice between strategic options and the resulting international performance may critically depend on the cross-national market structure of the various financial products. Insight into country segments and international consumer segments based on domain-specific behavioral variables will therefore be of key strategic importance. We present a multi-level latent class framework for obtaining simultaneously such country and consumer segments. In an empirical study, we apply this methodology and several alternative modeling approaches to data on ownership of eight financial products. Information is available for 15 European countries, with a sample size of about 1000 consumers per country. We find that both country segments and consumer segments are highly interpretable. Also, consumer segmentation is related to demographic variables such as age and income. Our conclusions feature implications, both academic and managerial, and directions for future research.     

Group Decision Support Using Fuzzy Cognitive Maps for Causal Reasoning

Cognitive maps have been used for analysing and aiding decision-making by investigating causal links among relevant domain concepts. A fuzzy cognitive map (FCM) is an extension of a cognitive map with the additional capability of representing feedback through weighted causal links. FCMs can be used as tools for both static as well as dynamic analysis of scenarios evolving with time. An FCM represents an expert's domain knowledge in a form that lends itself to relatively easy integration into a collective knowledge base for a group involved in a decision process. The resulting group FCM has the potential to serve as a useful tool in a group decision support environment. An appropriate methodology for the development and analysis of group FCMs is required. A framework for such a methodology consisting of the development and application phases is presented.     

The owner's edge: Brand ownership influences causal maps

Understanding the coherence between the attributes of a brand is a key asset for marketers managing brand equity. This study proposes consumer causal maps as a powerful instrument to achieve this purpose. These maps shed light on how different consumer groups think about the brand. Compared to non-owners, brand owners have been able to develop more expertise regarding the specific brand, which leads them to have more extensive causal maps. An exception occurs for the category leader, for which owners and non-owners have equally extensive maps. The surprising finding of this study is that the leading brand seems to encompass the ingredients for the causal maps of the other brands in the category. The results highlight how management should address owners and non-owners differently, in particular if a brand is far from category leadership.     

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.     

晚清时期武昌舆地学会的舆图出版及其经营特点

光绪二十二年(1896)成立的武昌舆地学会是中国第一个地理学会。该学会译印了大量的外国地图,并用西方先进的测绘方法和印刷技术编制出版了700余幅中外地图,在晚清地图学史上占据举足轻重的地位。本文论述了邹代钧与武昌舆地学会的创立,武昌舆地学会编制地图的特色及其科学价值,以及武昌舆地学会的经营特点等,以期对现代地理学会、地图学的发展提供历史借鉴。     

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.     

Consumer innovativeness and consumer decision styles: a confirmatory and segmentation analysis

Consumer decision styles (CDS) are important for marketers because they determine consumer behaviours that are relatively stable over time and hence are useful for market segmentation. The study has confirmed the existence of the original US characteristics as well as put forward two new characteristics specific to the Indian context, namely, ‘dissatisfied shopping consciousness’ and ‘store loyal’. Thereafter, a multi-step cluster analysis was employed to classify the respondents into groups based on their CDS. Five homogeneous and distinct decision-making segments have been identified. In order to validate the cluster solutions, a K-means clustering procedure was performed by taking random initial seeds to set the cluster centres. The paper also seeks to investigate whether consumers’ innovativeness is associated with their CDS. The findings are discussed and recommendations are proffered for managers and future research.     

Use of mental models and cognitive maps to understand students’ learning challenges

Mental models and cognitive maps have been used in college business education as an instructional design technique, assessment tool, and learning strategy. The authors propose a novel use of mental models and cognitive maps as a device to elicit students’ challenges in learning the domain knowledge of a course. Such usage is illustrated in a management information systems course. This student-focused approach can help instructor to better understand students’ learning challenges and enhance teaching effectiveness.     

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.     

A segmentation of Turkish consumers based on their motives to visit shopping centres

This paper aims to contribute to the literature by providing a segmentation of Turkish consumers based on their motives for visiting shopping centres. First, we identified the motives that Turkish consumers had for visiting shopping centres and then we used those motives to segment consumers. Data were collected through a survey from 390 participants living in the six largest cities in Turkey. First, the results showed that Turkish consumers had five groups of motives when it comes to visiting shopping centres. The results of a two-step cluster analysis used for segmentation revealed four distinct segments of Turkish shopping centre visitors. Those segments were named as serious consumers, recreational consumers, enthusiast consumers, pragmatic consumers. These findings suggest that Turkish shopping centre managers need to recognize that Turkish consumers are not a homogeneous unit when it comes to visiting shopping centres. Finally, academic and managerial implications of our findings are discussed.     

Can health “halos” extend to food packaging? An investigation into food healthfulness perceptions and serving sizes on consumption decisions

The purpose of this research is to examine how perceived food healthfulness and package partitioning interact to impact intended and actual consumption. Across three studies, findings indicate that both intended consumption and actual consumption of the perceptually healthier food items increase when packaging is not partitioned. Further, partitioning does not change the intended or actual consumption of foods perceived as less healthy. Accordingly, perceptually healthy foods tend to be consumed more when servings are not partitioned, suggesting a positive health halo leading to a “healthy = eat more” consumption pattern. The role of affect regulation theory and, more specifically, guilt, in this process is examined. These findings have implications for marketers, food manufacturers, and public policymakers interested in reducing obesity.     

Product customization: A profile of consumer demand

Increasingly, many brands are handing over to consumers the control of the design process, allowing for the customization of products, experiences, and services. While there is a clear demand for customization, the general assumption is that such demand is homogeneous among consumers. We propose that heterogeneity exists in consumer demand for customization and that a better understanding of these differences is important for brand managers to effectively develop and deliver to consumers the opportunity to customize. Drawing on configuration theory, we provide insights into consumer market segments for firms seeking strategic fit for more effective customization. Across multiple studies, a Latent Cluster Analysis is utilized to profile customization demand. Four distinct segments are identified: Non-Customizers, New Customizers, Active Customizers, and Lapsing Customizers, while studying their outcome considerations in terms of new product design. The varying patterns of customization preference and consumption patterns among segments provide opportunities for retail and service providers to target customization offers more effectively.     

How do price range shoppers differ from reference price point shoppers?

Existing research demonstrates that reference price models can explain a significant amount of the variation in customers' price perceptions and purchase behaviors. This study extends the reference price literature by introducing the price range model, which proposes that price judgments are based on a comparison of the market price to the entire range of currently available prices. Our results demonstrate that the fit of a structural heterogeneity finite mixture model improves when the price range model is included along with internal and external reference price models and that the price range model explains a substantial proportion of customers' purchase histories in the toilet tissue category. Profile analysis indicates that internal reference price shoppers switch brands much less frequently than the other two segments and respond to feature promotions for their preferred brand(s). External reference price shoppers have an intermediate level of brand preference and respond significantly less than the other two segments to feature and display promotions. Price range shoppers have the lowest brand loyalty and respond most strongly to both feature and display promotions.     

A data mining approach for segmentation-based importance-performance analysis (SOM–BPNN–IPA): a new framework for developing customer retention strategies

Importance-performance analysis (IPA) is a simple but effective means of assisting practitioners in prioritizing service attributes when attempting to enhance service quality and customer satisfaction. The purpose of this study was to demonstrate how IPA can be used with market segmentation to develop customer retention strategies for different market segments. For this purpose, a new framework have been proposed that uses self-organizing maps for customers’ segmentation and back-propagation neural network (BPNN) for implicity drive the importance of service attributes based on their effect on customers’ loyalty in each segment. Then, individual IPA matrixes are developed for each market segment. Also, an example case is presented to demonstrate the implementation and application of the proposed framework. The results of the proposed framework compared with a conventional BPNN-IPA approach indicated that it can increase reliability and applicability of IPA results.