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.     

Benefit segmentation in industrial markets

This article investigates whether or not traditional bases of industrial segmentation, such as SIC codes and company size, produce segments that are homogeneous within and heterogeneous between with respect to benefits sought. The study is applied to the acquisition of nonintelligent data terminals. We discovered that the traditional bases do not yield segments that seek significantly different dimensions. Alternatively, a benefit segmentation approach is demonstrated that results in segments substantially different from the traditional approach.     

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 the Analytic Hierarchy Process as a Two-phase Integrated Decision Approach for Large Nominal Groups

When decision makers who comprise a large nominal group face an unstructured decision problem and no simultaneous interactive communications are available, problem identification and consensus building are difficult, if not impossible. Few tools are available to assist decision makers in this situation. The Analytic Hierarchy Process (AHP) has typically been used to evaluate a set of alternatives after a decision problem has been structured as a hierarchy with various levels of criteria above the alternatives. With a group of decision makers, AHP has been used to evaluate those alternatives either by consensus building or by combining judgments or priorities using the geometric mean to aggregate their preferences. In this paper, we extend the use of AHP to a situation involving a large nominal group of dispersed decision makers where the entire hierarchy is not defined at the outset. In particular, we use the AHP as an integrative approach to identify the priorities of the various criteria and then use those priorities to screen and consolidate a large set of potential alternatives. This results in considering a reduced set of alternatives that will be affected by the more important criteria. The consolidated set of alternatives is evaluated by each individual in the group using AHP, combined using the geometric mean, and the results are synthesized to obtain the overall priorities of the alternatives. The approach is demonstrated and evaluated in a case study to select an alunmi anniversary gift to the U.S. Coast Guard Academy with a large nominal group of decision-makers dispersed throughout the United States.     

Creating the Customer: The Influence of Advertising on Consumer Market Segments – Evidence and Ethics

For over half a century market segments have been considered objective groupings of individuals which marketers identify, understand, and target with advertising messages. The process of market segmentation has, therefore, occupied a position of moral neutrality. An increasingly popular method of segmentation is by consumer personality, with advertisers targeting messages to specific personality types. This paper explores personality segmentation, and presents empirical evidence to support the proposition that personality metrics that are used to assign individuals to segments may, in fact, be manipulable by advertising executions themselves. Quite apart from the implications that this has for the business efficacy of the segmentation process, the ethical implications – particularly as applied to children – are considerable.     

The Responsiveness of Households to Energy Demand Management Initiatives: Segmenting by Household Types

Segmentation is often a complex and costly process, which commonly involves identifying groups with differing attitudes and behaviors or demographics. However, the segments generated may not adequately explain differences between consumers, or it can be difficult to identify households for target marketing, which consequently makes it difficult to use segmentation in practice. In this paper we present segmentation by household type as a simple method of segmentation, accessible to nonprofit organizations with limited resources and able to be employed using a secondary data set or with relatively simple data collection and analysis. Despite its simplicity, our findings show that this form of segmentation differentiates well between segments in terms of their actual electricity use, their past efficiency behaviors and preferences for efficiency programs. The results of focus groups and a survey of 4,000 households reveal, among other things, the effect of having children and of marital status on energy use and efficiency behaviors, the high curtailment but low investment behaviors of single parent households, and the low efficiency behaviors of shared households.     

一种基于区域搜索的全局运动估计与补偿算法

开展全局运动估计与补偿研究是进行动态目标检测中的基础和前提。在总结现有运动估计与补偿方法的基础上,提出一种基于图像分割区域的运动性和大小的全局运动估计与补偿算法。首先,通过建立区域搜索的全局运动模型,同时进行区域定性分割和区域大小排序;然后,根据误差最小化准则在指定的分割区域中进行线性递归搜索,利用门限准则寻找出最佳的运动估计参数;最后,根据双线性内插法获得运动位移量。实验结果比较可知,所提算法较三步搜索算法(TSS)和全局搜索算法(FSA)等传统算法具有更高的准确性(图像平移帧差)和实时性(算法运行时间),能够很好地实现运动背景的全局校正。     

Bridging Industrial Segmentation Theory and Practice

Purpose: Segmentation is seen today as a core concept within mainstream marketing. While industrial segmentation has received considerable attention within academic literature, there have been many reports on companies having problems using, applying, and implementing the principles of segmentation. Authors criticize the literature on segmentation for giving too little attention to implementation, providing marketers little help and guidance on how to make segmentation work in a practical environment. This article addresses some of these problems.

Methodology/Approach: The objective of this article is to design a process with methods and activities for segmentation by building on action research case study data from a large Danish industrial company.

Findings: Along with important considerations, this article presents the process of segmentation using a build-up approach based on qualitative market data. The process is practical and easily approachable for companies. The purpose behind segmentation is strategic—identify target segments and facilitate the planning of future product offers. It is argued that segmentation should be dedicated to the purpose and context of the company. This case illustrates how the company adapted segmentation theory to their particular needs.     

Profiling prospective private-label buyers

ABSTRACT

Food retailers have had difficulties increasing their private-label sales during non-recessionary periods. Conventional research leads us to believe it would be ineffective to use traditional segmentation to target prospective private-label buyers because sociodemographics and most psychographics are not strongly linked to private-label attitudes, purchases, or willingness-to-pay. Many studies have concluded that perceived risks are associated with private-label attitudes and are limiting private-label sales. This study explores a new approach to identify potential private-label buyers. The results from an internet panel survey of 605 adults were analyzed with binary logistic regressions. Several scales, which have rarely been employed in grocery shopper studies, were used to profile those individuals who perceive more risks from buying or using private labels. The characteristics of these individuals can help retailers and private-label marketers develop appealing product offerings and target prospective private-label buyers using various marketing tactics to grow their private-label businesses.     

The role of sustainability in profiling voluntary simplifiers

Studies focusing on voluntary simplifiers are gaining in popularity, but doubt remains about the relevance to business of this segment and to what extent this lifestyle is attributable to sustainability-rooted choices. Instead of the commonly used self-reported scales, a novel measurement approach is applied using objective data to identify voluntary simplifiers. Based on equivalent household incomes and level of product possession this research provides, using a large-scale, representative sample, empirical evidence that voluntary simplifiers comprise almost one-sixth of the German population. Results indicate that voluntary simplifiers buy more green products, exhibit a greater environmental and economic sustainability consciousness and share more universalistic values compared to four other uncovered segments, namely well-off consumers, over-consumption consumers, less well-off consumers and poor consumers. From a business perspective, moderate voluntary simplifiers do not exit the market. Instead, they constitute an attractive target group for ecological products and alternative consumption options such as sharing.     

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.     

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.     

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.     

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.     

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.     

How to build a successful private equity portfolio

The route to a successful private equity portfolio is a four‐step process that requires an in‐depth understanding of private equity as an asset class as well as the skills to select promising opportunities from available offerings. The first step is to set out and define general allocation guidelines. These initial guidelines then have to be refined based on the availability of adequate investment opportunities. The third step involves selecting the most attractive funds from a pool of target funds. And lastly the portfolio must be monitored on an ongoing basis to track its overall development as well as its enduring performance. Benchmarking the overall performance of the portfolio as well as individual managers is an integral part of this. In the following sections, each individual step is discussed and explained in more detail. © 2009 Wiley Periodicals, Inc.     

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.