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.     

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.     

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.     

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.     

Identifying purchase-history sensitive shopper segments using scanner panel data and sequence alignment methods

Prior segmentation research has primarily focused on market behavior, lifestyle and socio-demographics rather than on purchase histories. In this article, the authors propose the use of sequence alignment methods to segment customers based on their purchase histories. The principles underlying the method are discussed, and the method is illustrated using scanner panel data. The findings suggest that, compared to the conventional methods, the proposed method results in a better segment solution that captures both the shopping-frequency and variety-seeking information. These findings have important implications for differentiation and market positioning strategies.     

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.     

Simultaneous positioning and segmentation analysis with topologically ordered feature maps: a tour operator example

Positioning analysis seeks spatial representations of brands. Segmentation analysis searches for perceptually or preferentially homogeneous consumer segments. While these analytical steps often are taken sequentially, the positioning and segmentation problems are interrelated and need to be treated simultaneously. Topologically ordered feature maps (self-organizing maps, SOM) are neural network models for feature extraction and classification. Extracting prototypes corresponds with finding market segments; ordering them topologically resembles a perceptual mapping exercise. SOM modeling may thus be relevant for deriving low-dimensional and parsimonious representations of multidimensional profile data. The application of SOMs is explored in a case study on tour operator images. The results simultaneously inform about the firms’ image positions and their perceptually homogeneous customer segments.     

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.     

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股和H股市场软分割因素研究——兼论推出QDII的步骤和时机

本文以同时发行A股和H股的16家双重上市公司为样本,利用固定效应模型对H股折价率作了实证分析。研究发现:A股和H股市场的软分割因素主要有公司规模、股份流动性以及A股流通股股东的被补偿预期等。本文认为,QDII将成为消除A股和H股市场分割的重要制度之一,并对推出QDII的步骤和时机提出了看法。     

A股和H股市场软分割因素研究--兼论推出QDII的步骤和时机

本文以同时发行A股和H股的16家双重上市公司为样本，利用固定效应模型对H股折价率作了实证分析。研究发现：A股和H股市场的软分割因素主要有公司规模、股份流动性以及A股流通股股东的被补偿预期等。本文认为，QDⅡ将成为消除A股和H股市场分割的重要制度之一。并对推出QDⅡ的步骤和时机提出了看法。     

The Use of Cones to Determine the Market Shares of Service Establishments in a Single Market

This article makes use of the shape of cones to describe the characteristics of market demand at a single location. It then uses the physical properties of cones and their intersections, supported by spacial economic theory, to determine the market shares of service establishments located within a single market. The results not only attach quantitative values to the importance of central place, but also emphasise the flexibility of the model's structure and point to a number of additional uses to which it may be applied. Finally, the model establishes a distance decay function which is based on economic reasoning.     

Segmentation of visiting patterns on web sites using a sequence alignment method

In this article, a new method is illustrated for segmentation of visiting patterns on a web site. Instead of clustering users by means of a Euclidean distance measure, in our approach users are partitioned into clusters using a non-Euclidean distance measure, called Sequence Alignment Method (SAM). This method ensures that sequential relationships, represented by the order of elements, are taken into account. In experiments using real traffic data on the web site of a Belgian telecom provider, the performance of SAM is compared with the results of a method based on Euclidean distance measures. Empirical results show that SAM identifies segments presenting behavioral characteristics not only with regard to content but also considering the order of pages that are visited on a web site.     

Business Market Segmentation Procedure for Product Planning

This paper demonstrates a market segmentation procedure that responds to the information needs associated with business product marketing. We outline several important criteria that such a procedure that addresses those criteria. We illustrate use of the procedure by applying it to the US information processing market with considerable success. We close with a discussion of the uses and limitation of the procedure and the need for further research.     

高阶累积量在多参数联合分选中的应用

目前复杂体制雷达大量出现,侦收到的有效脉冲数少,传统的分选方法存在分选难、参数利用率低、可靠性差等问题,而多参数联合分选方法是解决上述问题的重要手段之一。为此,提出了一种提取信号的脉内特征参数高阶累积量,联合较为稳定的脉间参数,利用改进支持向量机的分选算法。仿真证明高阶累积量归一化的特征值对不同调制类型信号具有良好的区分度,并且通过对复杂体制雷达信号进行分选实验证明,分选结果准确率高。     

Using involvement for segmenting the adoption of e-commerce in travel

While the adoption of e-commerce in the purchasing of leisure travel is gaining momentum, higher levels of adoption can be achieved if greater knowledge of the market for e-commerce is obtained. This research uses two variables, involvement with computers for leisure purposes and purchasing leisure travel over the Internet, as a means of segmenting the market with regard to the adoption of e-commerce in the purchasing of leisure travel. A sample of residents of a suburban borough of Lisbon (Portugal) was surveyed and hierarchical clustering was performed on 225 usable questionnaires. The four resulting clusters of residents that emerged were quite different from one another in relation to the adoption of e-commerce in the purchasing of leisure travel and in supporting the internal and external validity of the clustering procedure. One segment that has not been reported in the literature before was identified, comprising those who have moderate involvement with both innovations (the moderate enthusiasts). Moreover, the results suggest that individuals in this cluster may be stronger candidates for the adoption of e-commerce in the purchasing of leisure travel that computer fanatics.     

Application of the Information Bottleneck method to discover user profiles in a Web store

ABSTRACT

The paper deals with the problem of discovering groups of Web users with similar behavioral patterns on an e-commerce site. We introduce a novel approach to the unsupervised classification of user sessions, based on session attributes related to the user click-stream behavior, to gain insight into characteristics of various user profiles. The approach uses the agglomerative Information Bottleneck (IB) algorithm. Based on log data for a real online store, efficiency of the approach in terms of its ability to differentiate between buying and non-buying sessions was validated, indicating some possible practical applications of the our method. Experiments performed for a number of session samples showed that the method is capable of separating both types of sessions to a large extent. A detailed analysis was performed for the number of clusters ranging from two to seven, and the results were compared to those achieved by applying the most common clustering algorithm, k-means. Increasing the number of clusters generally leads to better results for both algorithms. However, IB demonstrated much higher average efficiency than k-means for the corresponding number of clusters, and this superiority was especially clear for lower number of clusters. The IB-based division of user sessions into seven clusters gives the mean entropy value of 0.28, which means the 95% separation of sessions of both types. Furthermore, a big advantage of our approach is that it gives a possibility to analyze the probability distribution of session attributes in individual clusters, which allows one to discover hidden knowledge about common characteristics of various user profiles and use this knowledge to support managerial decisions.     

CHAID分析在抽油烟机产品市场细分中的应用

市场细分是企业战略营销的重要组成部分。根据一些调查数据显示,把抽油烟机市场细分变量区分为广度细分变量和深度细分变量,如果与传统市场细分方法进行对比,可了解到CHAID细分方法的优越性,方能用该方法对抽油烟机整体市场和潜在市场做以细分。