Market segmentation with choice-based conjoint analysis

Choice-based conjoint analysis has increased in popularity in recent years among marketing practitioners. The typical practice is to estimate choice-based conjoint models at the aggregate level, given insufficient data for individual-level estimation of part-worths. We discuss a method for market segmentation with choice-based conjoint models. This method determines the number of market segments, the size of each market segment, and the values of segment-level conjoint part-worths using commonly collected conjoint choice data. A major advantage of the proposed method is that current (incomplete) data collection approaches for choice-based conjoint analysis can still be used for market segmentation without having to collect additional data. We illustrate the proposed method using commercial conjoint choice data gathered in a new concept test for a major consumer packaged goods company. We also compare the proposed method with ana priori segmentation approach based on individual choice frequencies.     

Efficient theory development and factor retention criteria: Abandon the ‘eigenvalue greater than one’ criterion

Other things being equal, a theory with fewer constructs is preferable over others. In exploratory factor analysis, a common method used in theory development, the most popular factor retention criterion used in marketing is the eigenvalue greater than one rule. Its use often results in over extraction, which leads to the development of less than parsimonious theories. Even the use of confirmatory factor analysis fails to detect the presence of these superfluous constructs. Although several more accurate criteria exist, they are not discussed in major marketing research texts, journals, and popular statistical software packages. In this paper, we appraise popular factor retention practices in marketing, demonstrate how they may lead to the development of inefficient theories, draw attention to a number of resources for choosing appropriate retention criteria, and develop an easy-to-use Web-based engine to effortlessly implement one such method, parallel analysis.     

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.     

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.     

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.     

Market segmentation: A comparison of benefits/attributes desired and brand preference

Benefits/attributes desired is commonly considered the most strategically meaningful market segmentation base. However, in practice, problems in product benefit/attribute specification and data collection are frequently observed. This research compares the performance of benefits/attributes desired and brand preference (a competing segmentation base that provides similar information but is more closely related to purchase behavior) on the criteria of segment identification, brand purchase, and responsiveness to marketing mix elements. Results suggest that brand preference segmentation produces more interpretable and managerially useful segmentation results than benefits/attributes desired. © 1995 John Wiley & Sons, Inc.     

A latent structure factor analytic approach for customer satisfaction measurement

The linkage of customer satisfaction, customer retention, and firm profitability has been well established in the marketing literature, and provides ample justification as to why customer satisfaction measurement (CSM) has been a focal point in marketing decision making. Although aggregate market level research on understanding the determinants of customer satisfaction is abundant, CSM decisions at segment level are possible only if the individual or market segment differences in the formation of overall satisfaction judgments and subsequent heterogeneity in the role these various determinants play are understood. Based on expectancy-disconfirmation theory in customer satisfaction, we propose a maximum likelihood based latent structure factor analytic methodology which visually depicts customer heterogeneity regarding the various major determinants of customer satisfaction judgments involving multiple attributes, and provides directions for segment-specific CSM decisions. We first describe the proposed model framework including the technical aspects of the model structure and subsequent maximum likelihood estimation. In an application to a consumer trade show, we then demonstrate how our proposed methodology can be gainfully employed to uncover the nature of such heterogeneity. We also empirically demonstrate the superiority of the proposed model over a number of different model specifications in this application. Finally, limitations and directions for future research are discussed.     

Retail-consumer commitment and market segmentation

Customer's commitment has become a main mediating goal for many companies. It appears to be a promising market segmentation criterion. In this sense, the present work focuses on commitment as a possible criterion to segment the consumers' market. It starts by analysing the importance of consumer commitment for companies. After the review of the literature of commitment at different relationship levels, a definition and a multidimensional structure is proposed for this construct. The study performs a segmentation analysis of bank customers. Three clusters of clients are identified according to their level of commitment. Main conclusions and its implications for marketing management are pointed out.     

A two-stage model for forecasting consumers’ intention to purchase with e-coupons

E-coupons (electronic coupons) have been a mainstay of online marketing to attract consumers and promote them to repeat purchase, distributing right e-coupons to right consumers is of critical importance. In big data era, analyzing consumers preferences for e-coupons by their online behavior and the impact of data imbalance caused by low active consumers are rarely studied. Thus, we propose a two-stage hybrid model. Firstly, consumer segmentation is implemented to analyze behavioral characteristics for each segment and distinguish low active consumers, then models are constructed for different consumer segments. The proposed model is applied to a real online consumption data. Consumers are aggregated into four segments: potential e-coupons user, low discount sensitive user, high discount sensitive user (including discount preference and fixed preference). The first one is defined as low active consumer segment and others are high active consumer segments. Isolation forest model and logistic regression model are respectively constructed for them. Result shows that data imbalance is effectively relieved, prediction performance is also significantly better than the traditional approaches. Finally, e-coupons’ usage characteristics for each consumer segment are summarized, according to that, companies can increase sales and improve consumer satisfaction as well.     

Determinants of retail bank choice in Nigeria: a focus on gender-based choice decisions

As the Nigerian retail banks customers become more sophisticated, it is now very important that retail banks determine the factors that are important and relevant to the customers’ retail bank choice decisions. By using a survey of retail bank customers, this study evaluates the relative importance attached to retail banks’ choice criteria used by male and female customers in Nigeria. The results show that there are some differences in choice factors used by male and female customers in selecting a retail bank for patronage. The recommendation is that bank managers should take both female and male market segments into consideration when making retail bank strategic planning in order to become competitive. The findings can assist retail bank managers in refining their marketing strategies as a means of overcoming the intense competition that exists in the Nigerian banking system.     

Green consumer market segmentation: empirical findings from Portugal

Concern with environmental degradation has led to a new segment of consumers: the green or ecological consumers. Marketers must pay special attention to the needs of this segment. The growing concern with the environment, increased competition and greater selectiveness and demands of consumers represent an immediate challenge for green marketing. This paper aims to identify distinct market segments based on several environmental variables. It also investigates individuals' behaviours and perceptions about green consumerism. The used methodology was the collection of primary information through a direct survey. The study covered 887 Portuguese consumers, aged over 18. The results show that certain environmental and demographic variables are significant in differentiating between the ‘greener’ consumer group and the other segments. Portuguese consumers, despite their support for policies designed to improve the environment, do not always translate their concerns into environmentally friendly actions. Possible implications of these results for firms' marketing strategies are also discussed.     

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.     

Advances in computation,statistical methods,and testing of discrete choice models

This paper gives a brief overview of recent developments in computation, estimation, and statistical testing of choice models, with marketing applications. Topics include statistical models for discrete panel data with heterogeneous decision-makers, simulation methods for estimation of high-dimension multinomial probit models, specification tests for model structure and for brand and purchase clustering, and innovations in numerical analysis for estimation and forecasting. In collaboration with Denis Bolduc, David Bunch, Michael Keane, Don Kridel, and Steve Stern.     

Service Quality in Public Services as a Segmentation Variable

The application of perceived service quality to marketing segmentation makes a significant contribution to customer quality expectations by enabling more efficient segmentation. By means of an empirical analysis, the present work firstly contrasts the validity of perceived service quality measurements in public services. It goes on to analyse the usefulness of this concept as a segmentation criterion. Finally, customer segments are identified as a function of perceived quality and profiles are established for each segment. This study proves that service quality is a useful tool for segmentation in public services.     

A Dynamic Model of Cross-Category Competition: Theory, Tests and Applications

Recent marketing studies use scanner data to diagnose the influence of a change in a brand's marketing mix on other brands in the same category. A few studies also use scanner data to model inter-category effects between substitutes (e.g., tea and coffee) or complements (e.g., tea and sugar). No study models the dynamic effects of cross-category competition though.We present a dynamic model that shows how different products in a typical market basket influence demand of one another. Empirical tests use single-source, market-level data for ice cream and some substitute and complement categories. On the basis of in-sample fit, out-of-sample forecasts, and formal causality tests, this study demonstrates that the marketing mix decisions for ice cream influence the sales and market shares of ice cream toppings and frozen yogurt. As a check on the predictive validity of the model, model parameters, using 16-month data, predict behavior in the subsequent 3 months.     

基于消费者心理模型的营销策略分析

心理模型对消费者的购买决策影响巨大。基于心理模型下的模糊营销,应依据消费者所处场合,匹配性宣传产品,适当性投放产品以建立和顺应模糊心理模型;隐喻心理模型则是依据消费者的心理去塑造商品形象,明确企业的核心竞争力和核心创意,并实践企业的核心创意;潜意识心理模型是依据消费者潜意识的趋同效应、环境效应和促销等购买动因来制定营销策略,从而有利于企业根据不同的心理模型制定相应的营销策略,在激烈的市场竞争中顺应消费者心理,促进消费者做出积极的购买决策。     

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.     

Using diffusion models to forecast market size in emerging markets with applications to the Chinese car market

Marketing managers have to forecast the market size and this forecast guides strategic decisions whether to continue exporting, open new factories or expand existing production operations. Forecasting sales and the market size is a challenging task; even more so in emerging markets where data is limited and the market demand is changeable. This research proposes a novel approach that applies diffusion models using car ownership data to forecast car sales. Car ownership data may be easier to access than sales data in emerging markets but marketing managers are more interested in the sales forecast. Researchers propose using diffusion models to forecast the adoption of new products or products which are new to consumers in a market. This research demonstrates that marketing managers can use diffusion models to predict car sales in China where cars are new products to most consumers in this market. Since the majority of car buyers in China are first time buyers, car manufacturers and retailers must also forecast when the market composition will change. This effectively means predicting when first time car buying will start to slow down and repeat/replacement purchase or second hand car purchase will become more important. To forecast both sales and market composition change, marketing managers must choose a robust model. Managers want insights from models that have been tested robustly especially in less stable market conditions. In this context, this study illustrates the value of using a rolling forecast instead of a fixed horizon approach when comparing and choosing which model to use to forecast both sales and market composition change for the Chinese car market.     

Capturing the Evolution of Customer–Firm Relationships: How Customers Become More (or Less) Valuable Over Time

Few studies have examined the influence of marketing activities while accounting for customer dynamics over time. The authors contribute to this growing literature by extending the hurdle model to capture customer dynamics using a hidden Markov chain. We find our dynamic model performs better than static and latent class models. Our results suggest the customer base can be segmented into four segments: Deal-prone, Dependable, Active, and Event-driven. Each segment reacts differentially to marketing activities. Although catalogs influence both purchase incidence and the number of orders, this marketing activity has the largest impact on purchase incidence across all four segments. In contrast, retail promotions are more likely to influence the number of orders a customer will make for all of the segments except for the Deal-prone segment. For this segment, retail promotions have the strongest impact on purchase incidence.