Consumer Preferences for Food Label Information: A Basis for Segmentation

Analysis of consumer reactions to food labeling alternatives using conjoint measurement raises questions concerning policy proposals to provide consumers with information. Conjoint measurement permits analysis of components of consumer perceptions without tedious data collection and analysis procedures. The study indicates that consumer preferences for information vary widely and an optimal policy should provide different labels for different market segments. Increasing the amount of information may reduce its effectiveness among the low income consumers it is intended to help.     

Struggles in optimal labelling: how different consumers react to various labels for genetically modified foods

The labelling of genetically modified (GM) foods is an important policy issue, as consumers' attitudes towards these foods appear to be quite sensitive to information about their potential benefits and risks. Because it is difficult for labels to differ across consumers, differences in reactions to label information could lead to conflicts across consumer groups or to different market outcomes. Using factor and cluster analysis, we uncover three consumer segments with different attitudes to the risks and benefits of GM foods. Our segmentation‐based analysis on consumer reactions to information points out important differences across consumers. Not only did we find differences across segments in how they view the credibility of GM‐related information, we also find differences in how information influences segments' perceptions of the product, and in their likelihood to buy.     

Response style corrected market segmentation for ordinal data

Survey data collected for market segmentation studies is typically ordinal in nature. As such, it is susceptible to response styles. Ignoring response styles can lead to market segments which do not differ in beliefs, but merely in how segment members use survey answer options and which possibly occur in addition to the belief segments. We propose a finite mixture model which simultaneously segments and corrects for response styles, permits heterogeneity in both beliefs and response styles, accommodates a range of different response styles, does not impose a certain relationship between the response style and belief segments, and is suitable for ordinal data. The performance of the model is tested using both artificial and empirical survey data.     

The Influence of Negative Information on Purchase Behaviour

This was a longitudinal study that examined consumer reactions to hazardous product incidents. The purpose of this study was to examine consumer responses to product safety hazard incidents over time, and to identify demographic groups that are either most resistant or most affected by hazard incidents. The results suggest that in the first year after an incident, the crisis managers had only limited control over adverse effects to negative information. In the longer term, consumer reactions were still rather uniform and severe in most of the cases. The effects are examined in the context of key market segments for each product, and with regard to company responses to the incidents. Managers should expect strong negative reactions from some demographic subgroups over time and are advised to closely monitor the impact of hazardous incidents on their target markets.     

Personalized ads on Facebook: An effective marketing tool for online marketers

A growing number of researchers have examined the effects of personalized advertising in traditional media, however, little has been known about personalized advertising on Facebook. The primary objectives of this research are threefold: (1) Develop a comprehensive model that captures the effects of perceived personalized ads on Facebook on customer attitudinal and behavioral reactions (ad credibility, ad avoidance, ad skepticism, ad attitude, and behavioral intention) to the ad; (2) Test hypothesized relationships using two data sets collected through an online survey; and (3) Develop appropriate customer segments based on personal views of personalized ads on Facebook. The paper reveals that eleven out of thirteen hypotheses are supported and that three market segments are identified including Ad Lovers, Ad Accommodators, and Ad Haters. The paper concludes with conclusion and discussions highlighting managerial and research implications.     

Development of consumer techno segmentation and its application to international markets

This study aimed to develop consumer techno segments based on technology‐related psychographic variables in four different countries including the US, Canada, Spain and Italy. The respondents' technology innovativeness, technology opinion leadership, network externality risk and technology anxiety were used as metrics to identify consumer techno segments. Cluster analysis identified three to four distinct techno segments in each country. Multivariate analysis of variance and univariate analyses were used to validate the differences among techno segments in need for change, leisure orientation and e‐shopping preference in each local market. The similarities and differences of each techno segment were examined across different international markets. A discussion and implications were drawn to help marketers develop penetration and market strategies for different international markets by understanding an expected diffusion rate of a new product and of product life cycles for each local market.     

Aesthetic responses to prototypicality and uniqueness of product design

This paper investigates how dimensional measures of product design form influence the aesthetic responses of consumers through the concepts of product prototypicality and uniqueness. We develop and test a model with two different methods. The first is a longitudinal panel of passenger vehicle models available in the U.S. automotive market from 1999 to 2007. The data includes 16 firms, 32 brands, and 137 products (i.e., vehicle models) from four product-based, industry-derived market segments. We also conduct an experiment motivated by the results of the model. The model results suggest prototypical design and aesthetic response can vary by the relative prototypicality and sub-dimensions of the product’s design. Results from both the model and experiment suggest that consumers prefer prototypical design form across the entire passenger car market, but prefer unique design form within specific market segments. Theoretical and managerial implications are discussed.     

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.     

Retention of latent segments in regression-based marketing models

Product design and marketing mix decisions for segmented markets depend crucially on the correct specification of marketing models used as input to these decisions. With real-world data, the true number of segments in a market is unknown. Current evidence from simulation studies suggests that the accuracy of commonly used criteria for determining the number of segments in a market depends on the usage context, including the type of distribution being used to describe the data, the model specification, and the characteristics of the market. This study investigates via simulation the performance of seven segment retention criteria used with finite mixture regression models for normal data. This is one of the most important analysis contexts in marketing research since regression models are used, for example, in conjoint analysis and market response analysis, yet no previous study in either the marketing or statistics literatures explores the segment retention problem for mixture regression models. The study shows that one criterion, Akaike's Information Criterion (AIC) with a per-parameter penalty factor of 3 (AIC3), is clearly the best criterion to use across a wide variety of model specifications and data configurations, having the highest success rate and producing very low parameter bias. Currently, this criterion is rarely, if ever, used in the marketing literature.     

Understanding multi-level institutional convergence effects on international market segments and global marketing strategy

Dynamic changes in the global marketplace have increased opportunities for marketing strategy standardization due to the convergence of cross-national market segments. An oversimplified understanding of the complexities of this convergence could lead to ineffective global marketing strategy execution. This study develops a multi-level institutional approach to address level-based convergence effects necessary to understanding market segment convergence and its influence on global marketing strategy. A model of influential level effects on global marketing strategy is developed having implications for global marketing academics and practitioners.     

Performance as a classification criterion of tourist origins and destinations

Portfolio analysis has been used as a tool for the study of market segments, namely of tourist destinations. However, a review of the literature did not identify a single case where the performance variable is adjusted to the nature of the object. This article presents a performance proxy that enables a direct comparison between generating countries (origins) and between destinations. The proposed tool is a component of a model for the identification of priority market segments. The instrument is applied to the 15 member states of the European Union (before its expansion on 1 May 2004), for the period from 1996 to 2001.     

Using customer lifetime value to plan optimal promotions

The purpose of this study is to develop a methodology to guide managers in determining the optimal promotion campaigns to be directed towards different market segments in order to maximize the value of customers. For the purposes of this study, a two-step methodology is used, based on stochastic dynamic programming and the classification and regression tree. This methodology groups the customers according to their value. Within this framework, an experiment is conducted in which each of the different promotion campaigns is assigned to different randomly selected groups. The impact of each type of promotion on each type of market segment is analysed in order to find the optimal promotion campaigns appropriate for each. In contrast to previous research, this study takes into account a firm that provides more than one specific type of product or service. In addition, it analyses the impact of widely used types of promotion campaigns compared with the narrow scope of those investigated in previous studies. Therefore, this research presents important insights into managing relations with the customers in a more interactive and profitable way.     

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.     

Shades of gray��the impact of gray markets on authorized distribution channels

Gray markets refer to the diversion of unauthorized products from a market or channel with low prices to another market or channel with higher prices. In this paper, we study the effects of gray markets on the profitability of distribution channel members under different channel structures with different parallel importer identities. We show that a manufacturer can benefit from parallel importation by a third party or by an authorized dealer. However, channel structure is critical in determining both who benefits and the mechanism by which this benefit occurs. In addition, we show whether the parallel importer is a third party or a retailer has significant effects on channel members’ profits. Although this research is motivated by international marketing practices, the modeling approach and the results apply more generally to cases where a manufacturer sells to different customer segments at different prices.     

Product life cycle patterns for pharmaceuticals and their impact on R&D profitability of late mover products

This study describes a cross-sectional and time series analysis of sales data over one decade in four major segments of the market for cardiovascular drugs. We estimate over 200 product life cycles (PLC) using a very flexible mathematical model to account for a variety of PLC shapes. Life cycles are then clustered on the basis of estimated regression coefficients. As a result this analysis leads to the detection of an international PLC classification. Moreover, it turns out that the order of entry is not only crucial to achieve a certain market share level but also to the shape of the drug life cycle and therefore for the long-term economic evaluation of innovative products. The paper provides the initial findings on this subject through a simulation study which is in line with previous research designs and shows the impact of the PLC shape on the net present value (NPV).     

Assessing marketing risk

A model for assessing the risks specific to a marketing strategy is developed and applied to data generated by executive decisions made in the Markstrat environment. The specific risks of a marketing strategy are those associated with changes in primary demand and market share. The riskiness and profitability of different strategies are assessed statistically. Primary-demand risk and return on investments are found to be positively correlated. More volatile, risky, market segments yield higher average returns. The strong temptation to harvest market share is isolated in the positive correlation between harvesting risk and rate of return. The pressure not to build long-term positions is found in the high negative correlation between building risk and return on investments. Overall, the model successfully explains risk-taking behavior in the Markstrat environment, and offers theoretical direction for future research in the real world.     

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.     

新产品市场安全的可拓物元评价模型及应用

新产品进入市场的安全问题是目前众多企业非常关心的实际操作问题,同时营销评价问题是以营销工程为主要支撑的市场营销科学研究之一,但传统的评价方法过于粗糙且不够全面。本文在其他学者的研究结果基础之上通过调查统计分析构建了比较客观全面的市场安全风险因素指标体系,运用可拓理论中的优度评价法建立了新产品入市的市场安全评价模型。实证研究结果表明这种评价方法具有较高的准确性,对于深入研究产品的营销安全评价问题有一定借鉴意义。     

Labelling wool products for animal welfare and environmental impact

The notable growth of the market in recent years indicates apparel consumers' interest in organic fibre products. Yet less is understood about how apparel consumers would respond to labelling for other credence attributes associated with animal‐fibre products, such as animal welfare or eco‐friendliness. An online survey of 507 US consumers was used to compare consumers' reactions with a variety of labelling schemes for wool product attributes, including animal‐friendly, organic and environmentally friendly production. Consumer segments were created based on frequency of label choice, and analysis of variance and multinomial logit regression were used to identify and characterize the demographics and psychographics of the consumer segments that found labelling for animal welfare or environmental concerns appealing. The study identified a segment of consumers (19% of the sample) who were motivated to purchase apparel products labelled for animal welfare. These animal‐focused consumers could be identified with relatively high accuracy from the demographic and psychographic variables in the model. The model variables, which included familiarity with organic products and self‐perceived knowledge about environmental damage related to apparel production, were not effective in identifying the environment‐focused apparel consumers. The results also demonstrated the ability of a general belief in animal rights to motivate the apparel consumers in the sample, suggesting that acting on a concern for animals could be a more powerful motivation for consumer behaviour than acting on a concern for the environment.