Latent class modeling of website users’ search patterns: Implications for online market segmentation

Appropriate modeling of web use patterns may yield very relevant marketing and retailing information. We propose using a model-based clustering approach for market segmentation based on website users’ search patterns. We not only provide a detailed discussion of technical issues such as the problem of the selection of the number of segments, but also a very interesting empirical illustration of the potentials of the proposed approach.     

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.     

Searching offline and buying online – An analysis of showrooming forms and segments

Showrooming is behavior where customers search for information in brick-and-mortar stores and then purchase products online. While the literature conceptualizes showrooming as a one-dimensional variable, we argue that different forms of showrooming exist. We identify four showrooming segments that differ in retailer loyalty, usage of in-store information, devices, place and time of the online purchase. We show further that loyal vs. competitive showroomers differ in psychographic variables, such as price consciousness, desire for social contact and bad conscience during showrooming. The results have important implications for retailers aiming to keep customers in their own channels.     

“I can get no e-satisfaction”. What analytics say? Evidence using satisfaction data from e-commerce

This study mines customer satisfaction (CS) segments using almost 270 thousand responses from a CS survey which ran in 140 e-commerce stores of a European country. To achieve this, it develops and applies a business analytics (BA)-informed framework. Then, it presents examples of how one e-commerce store exploited the extracted CS segments to build automated marketing actions for its customers, ranging from social media sharing strategies for the satisfied segments, to discounts for the less satisfied. This study contributes to customer satisfaction and segmentation literature. The extracted insights can be utilized to support decision making, ranging from targeted advertising for specific customer segments, to benchmarking for companies in similar industries.     

Unprofitable customers and their management

The widespread use of customer relationship management (CRM) applications has provided companies with increasingly detailed insights into the profitability of their individual-level customer relationships. Historically, this information has primarily been used to identify the most profitable customers and to define ways for serving them in an optimal manner. Nowadays, however, companies have become more aware of unprofitable clients, and the fact that these relationships can account for a substantial share of their total customer base. Drawn from a series of research projects conducted by us over the last few years, we herein propose a six-step approach for dealing with such unprofitable customers, a framework we refer to as the ABCs of Unprofitable Customer Management: Avoid their acquisition in the first place, Bear in mind potential rescue operations, Catch the possibility of abandonment, Draw up a cost-benefit analysis, Ensure familiarity with your environment, and Facilitate biting the bullet. We also provide a series of recommendations regarding how best to break up with customers, in order to ensure that firms can separate themselves from unprofitable accounts in the least painful way.     

Customer relationship management in retailing: A content analysis of retail trade journals

The purpose of this research was to increase knowledge and understanding of how retailers use business intelligence and data mining tools to implement customer relationship management (CRM) in retailing. Specific objectives were to (1) identify organization and infrastructure requirements for CRM effectiveness, (2) identify CRM objectives and goals of retail companies, (3) identify data mining tools utilized by retailers to perform CRM functions, and (4) identify CRM strategies used by retail companies. A keyword search within business databases using CRM and CRM identified publications with CRM content. Content analysis was used on articles (N=149) drawn from Stores, Chain Store Age, Harvard Business Review, and Retail Forward over a 5 year period (2000–2005). Selected articles were stored as text files in QDA Miner, a computerized qualitative analysis tool. Key organization/infrastructure needs emerged focusing on data structure, organizational systems, technology structure, and data accessibility. Retailers goals/objectives and strategies focused on marketing, customer service, understanding customers through data analysis and increasing acquisition and retention through customer loyalty programs. Data mining tools identified supported marketing and customer analysis efforts. Findings provide insight into the challenges retailers face as they implement a more customer-centric business strategy.     

New Insights in the Moderating Effect of Switching Costs on the Satisfaction–Repurchase Behavior Link

Existing studies on the role of switching costs (SC) as moderator of the relationship between satisfaction and repurchase behavior are inconclusive. We attempt to explain these inconclusive findings by synthesizing an amplifying and a lock-in effect, and hypothesize a nonlinear moderating effect. In Study 1 (a main study and three replications), we find strong evidence for an inverted u-shaped moderating effect of overall SC. Our results suggest that satisfaction is a particularly important predictor of repurchase behavior in situations characterized by medium-levels of SC. Based on Prospect Theory, Study 2 (a main study and one replication) reveals that this inverted u-shaped moderating effect of SC is stronger for positive (relational and financial) SC than for negative (procedural) SC. We conclude with recommendations for satisfaction management of different customer segments, and describe possibilities to influence customer switching costs in various industries.     

Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model

The recency/frequency/monetary value (RFM) segmentation framework remains a mainstay in the retailing industry to quantify consumer values. However, the RFM model does not consider the added value travelers ascribe to auxiliary services. We extend the RFM framework to an RFM-P model by considering the likelihood of purchasing ancillary services during travel (P). We proposed four traveler groups based on the RFM-P model using unique sales data provided by Chinese airline. The four customer groups were compared through the lens of personal and scenario characteristics to estimate travelers’ purchasing behaviours in airlines. The results help managers make market decisions and fill gaps left by consumer value theory and the RFM model in the retailing industry.     

Web site customer orientations, Web site quality, and purchase intentions: The role of Web site personality

This research extends the concept of salesperson customer orientation to Internet marketing by conceptualizing and studying the effects of Web site customer orientation on perceived Web site quality and consumer behavior. This research also adapts the concept of brand/store personality to Internet marketing by investigating the effects of Web site personality on perceived Web site quality and consumer purchase intention. The model is tested on apparel Web sites using data from multiple sources. Implications of managing Web site personality and Web site customer orientation are discussed.     

International Market Segmentation and Cross-Export Strategies Development Case Study: Iranian Furniture Industry

ABSTRACT

By developing an international market and significant differences between countries in this regard, segmentation becomes an increasingly important concept in marketing. In this article, the international market of Iranian furniture industry is segmented and cross-exporting strategies are developed to increase the market share of Iranian furniture in each segment. To achieve this, two distance functions are introduced based on correlation between export groups to cluster countries with k-means algorithm. After market segmentation for increasing sales in each segment, cross-exporting strategies are predicted by extracting association rules in each segment based on Apriori algorithm to set export baskets.     

基于决策树的数据挖掘方法在CRM中的应用研究

基于ID3算法的CRM数据分类实例证明:决策树的核心问题是选择最佳的划分标准,采取数据挖掘中的决策树分类算法,将其应用于CRM系统中,以从中发现企业产品的销售规律和客户群特征,从而提高CRM对市场活动和销售活动的分析能力,以期为企业更科学、更有效地制定产品开发、生产、销售和服务策略提供有力的技术支持。     

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.     

企业电子商务中客户知识挖掘模型研究

本文从语义的视角构建了面向传统企业电子商务业务的客户知识挖掘模型,实现了在异构、分布式客户数据源语义集成的基础上实施客户知识挖掘过程,推动知识挖掘方法和技术智能化进程,提高客户知识挖掘模式产生的质量。     

The Impact of Service Characteristics on the Switching Costs–Customer Loyalty Link

This research investigates the interrelationship between service characteristics and switching costs and makes two contributions to the service retailing literature: (1) As a means of better understanding the effectiveness of switching costs, the study suggests a two-dimensional typology of switching costs, including internal and external switching costs and (2) it reveals that the effect of these switching costs on customer loyalty is contingent upon four service characteristics (the IHIP characteristics of service). We carried out a meta-analytic review of the literature on the switching costs–customer loyalty link and created a hierarchical linear model using a sample of 1,694 customers from 51 service industries. Results reveal that external switching costs have a stronger average effect on customer loyalty than do internal switching costs. Moreover, we find that IHIP characteristics moderate the links between switching costs and customer loyalty. Thus, the link between external switching costs and customer loyalty is weaker in industries higher in the four service characteristics (as compared to industries lower in these characteristics), while the opposite moderating effect of service characteristics for the internal switching costs–loyalty link is noted.     

数据挖掘及其在企业管理中的应用

企业在管理过程中产生了大量的数据,这些数据的背后隐藏着与企业密切相关的极其重要的知识。聚类、关联规则、序列模式、统计分析、特征规则等数据挖掘方法能从这些海量数据中发现有用的知识,使数据真正成为企业的财富,为企业的决策和发展服务。目前数据挖掘已被广泛应用于银行、电信等行业,用来对客户数据进行正确的分析,挖掘消费模式,预测客户未来的行为,针对客户的需求提供个性化的服务。     

Comparison of customer response models

Segmentation of customers by likelihood of repeating business is a very important tool in marketing management. A number of approaches have been developed to support this activity. This article reviews basic recency, frequency, and monetary (RFM) methods on a set of data involving the sale of beef products. Variants of RFM are demonstrated. Classical data mining techniques of logistic regression, decision trees, and neural networks are also demonstrated. Results indicate a spectrum of tradeoffs. RFM methods are simpler, but less accurate. Considerations of balancing cell sizes as well as compressing data are examined. Both balancing expected cell densities as well as compressing RFM variables into a value function were found to provide more accurate models. Data mining algorithms were all found to provide a noticeable increase in predictive accuracy. Relative tradeoffs among these data mining algorithms in the context of customer segmentation are discussed.     

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.