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The purpose of this paper is to determine the best approach to customer segmentation and to extrapolate associated rules for this based on recency, frequency and monetary (RFM) considerations as well as demographic factors. In this study, the impacts of RFM and demographic attributes have been challenged in order to enrich factors that lend comprehension to customer segmentation. Different types of scenario were designed, performed and evaluated meticulously under uniform test conditions. The data for this study were extracted from the database of a global pizza restaurant chain in Turkey. This paper summarizes the findings of the study and also provides evidence of its empirical implications to improve the performance of customer segmentation as well as achieving extracted rule perfection via effective model factors and variations. Accordingly, marketing and service processes will work more effectively and efficiently for customers and society. The implication of this study is that it explains a clear concept for interaction between producers and consumers.

Customer relationship management, which aims to manage record and evaluate customer interactions, is generally regarded as a vital tool for companies that wish to be successful in the rapidly changing global market. The prediction of customer behaviors is a strategically important and difficult issue because of the high variance and wide range of customer orders and preferences. So to have an effective tool for extracting rules based on customer purchasing behavior, considering tangible and intangible criteria is highly important. To overcome the challenges imposed by the multifaceted nature of this problem, the authors utilized artificial intelligence methods, including k-means clustering, Apriori association rule mining (ARM) and neural networks. The main idea was that customer clusters are better enhanced when segmentation processes are based on RFM analysis accompanied by demographic data. Weighted RFM (WRFM) and unweighted RFM values/scores were applied with and without demographic factors and utilized to compose different types and numbers of clusters. The Apriori algorithm was used to extract rules of association. The performance analyses of scenarios have been conducted based on these extracted rules. The number of rules, elapsed time and prediction accuracy were used to evaluate the different scenarios. The results of evaluations were compared with the outputs of another available technique.

The results showed that having an appropriate segmentation approach is vital if there are to be strong association rules. Also, it has been determined from the results that the weights of RFM attributes affect rule association performance positively. Moreover, to capture more accurate customer segments, a combination of RFM and demographic attributes is recommended for clustering. The results’ analyses indicate the undeniable importance of demographic data merged with WRFM. Above all, this challenge introduced the best possible sequence of factors for an analysis of clustering and ARM based on RFM and demographic data.

The work compared k-means and Kohonen clustering methods in its segmentation phase to prove the superiority of adopted segmentation techniques. In addition, this study indicated that customer segments containing WRFM scores and demographic data in the same clusters brought about stronger and more accurate association rules for the understanding of customer behavior. These so-called achievements were compared with the results of classical approaches in order to support the credibility of the proposed methodology. Based on previous works, classical methods for customer segmentation have overlooked any combination of demographic data with WRFM during clustering before proceeding to their rule extraction stages.

Emotional and affective responses are experienced during service use that determine customer behavior; and for this reason, bank services require an better understanding of the emotions customers feel in service experiences. This research aims to examine whether different customer segments exist in the bank services industry, based on the emotions they experience when using the service.

The factors were examined through confirmatory factor analysis (CFA). Then, two-step clustering analysis was developed for customer segmentation on data from 451 bank service customers. Finally, an Anova test was conducted to confirm the differences among the obtained customer segments.

Our findings show that the emotion-based segmentation is meaningful in terms of behavioral outcomes in bank services. Further, research findings indicate that bank service customers cannot be perceived as a homogenous group, since four customer clusters emerge from our research namely “angry complainers”, “pragmatic uninvolved”, “emotionally attached customers” and “happy satisfied customers”.

Our findings show that the emotion-based segmentation is meaningful in terms of behavioral outcomes in bank services. Further, research findings indicate that bank service customers cannot be perceived as a homogenous group, since four customer clusters emerge from our research namely “angry complainers”, “pragmatic uninvolved”, “emotionally attached customers” and “happy satisfied customers”, being the “angry complainers” the most challenging customer group.

The study is the first one to specifically segment bank customers based on the emotions they experience when using the service.

Segmentation by demographic factors is widely used in bank marketing despite the fact that the correlation of such factors with the needs of customers is often weak. Segmentation by expected benefits and attitudes could enhance a bank’s ability to address the conflict between individual service and cost‐saving standardisation. Using cluster analysis segments were formed based on combinations of customer ratings for different attitudinal dimensions and benefits of bank service. The clusters generated in this way were superior in their homogeneity and profile to customer segments gained by referring to demographic differences. Additionally, four characteristic groups of customers were identified showing special preferences for and against information services and technology.

The objective of this paper is to present and discuss the development of a transaction‐based model for segmenting users of internet banking. It aims to employ a random sample of clients of a large Canadian bank in generating the hybrid segments.

The basic transactional profile of the bank's clients was merged with Mosaic's financial segments contained in the Generation5 database. A random sample of 3 percent of a large Canadian chartered bank's clients was drawn from its transaction database. The transaction database employed contains clients from Quebec and the Maritime provinces. The sampling frame consisted of close to one million clients. Two‐step cluster analysis was employed to generate the transaction segment and later merged with the Mosaic financial segment to produce hybrid segments.

Two‐step cluster analysis identified four generic transaction segments which, when cross‐tabulated with the Mosaic financial segments, produced highly stable and interpretable segments. These hybrid segments are clearly superior to conventional life style or psychographic segments produced by classical segmentation methodologies.

The results of this study clearly demonstrate the functional and analytical superiority of hybrid customer segments. Hybrid segmentation, by cross‐tabulating transaction and Mosaic's financial segments, provides banks and financial institutions with superior strategic insights in customer understanding, customer segmentation, customer communication, customer prospecting and targeting.

This paper is the first to present, explain and to demonstrate the nature and the operational procedure to develop hybrid customer/client segments. More importantly, it is the first that goes beyond conventional approaches to segmenting banks' clients who engage in internet banking by integrating clients' transaction profiles and Mosaic financial segments. The resulting hybrid segments are radically different than the conventional, one‐dimensional segments produced by conventional cluster‐based segmentation.

This paper aims to explore and evaluate practice-based segmentation as an alternative conceptual segmentation perspective that acknowledges the active role of consumers as value co-creators.

Data comprising various aspects of customer-to-customer (C2C) co-creation practices of festival visitors were collected across five UK-based festivals, using participant observation and semi-structured interviews with naturally occurring social units (individuals, couples and groups). Data were analysed using a qualitative thematic analysis procedure within QSR NVivo 10.

Private, sociable, tribal and communing practice segments are identified and profiled, using the interplay of specific subject- and situation-specific practice elements to highlight the “minimum” conditions for each C2C co-creation practice. Unlike traditional segments, practice segment membership is shown to be fluid and overlapping, with fragmented consumers moving across different practice segments throughout their festival experience according to what makes most sense at a given time.

Although practice-based segmentation is studied in the relatively limited context of C2C co-creation practices at festivals, the paper illustrates how this approach could be operationalised in the initial qualitative stages of segmentation research. By identifying how the interplay of subject- and situation-specific practice elements affects performance of practices, managers can facilitate relevant practice-based segments, leading to more sustainable business.

The paper contributes to segmentation literature by empirically demonstrating the feasibility of practice-based segments and by evaluating the use of practice-based segmentation on a strategic, procedural and operational level. Possible methodological solutions for future research are offered.

The study of how organizations segment their markets has traditionally taken a prescriptive and analytical approach. More recently, a number of academics and practitioners have voiced concerns over the evident gap between how such concepts are viewed in theory and how they are applied in practice. These issues have already been raised in academic papers, but almost entirely at an abstract level. Introduces a more concrete aspect to the debate by proposing a series of organizational archetypes which illustrate how organizations may segment their markets in practice. These archetypes are developed from a series of mini‐case studies which provide a basis for understanding how organizations may interface with the market at both an explicit and implicit level. Discusses the implications for both academic research and organizational practice.

Market segmentation is a powerful and discriminating method of grouping customers categorically so that their needs may be properly addressed. Segmentation can be devised on a geographic, demographic, sociographic or psychographic basis, but a bank will only maintain its competitive edge if all customers are considered within the same perspective. Segments must be evenly balanced so as not to systematically create a vacuum in one market area; if a segment is justifiable in its uniqueness and profitabilitty then it achieves viability. Service benefits must be considered from the customer's perspective as well as the bank's own and, segmentation being a dynamic tool, it must be well thought out and executed with care.

The purpose of this paper is to propose a new RFM model called length, recency, frequency, monetary and periodicity (LRFMP) for classifying customers in the grocery retail industry; and to identify different customer segments in this industry based on the proposed model.

This study combines the LRFMP model and clustering for customer segmentation. Real-life data from a grocery chain operating in Turkey is used. Three cluster validation indices are used for optimizing the number of groups of customers and K-means algorithm is employed to cluster customers. First, attributes of the LRFMP model are extracted for each customer, and then based on LRFMP model features, customers are segmented into different customer groups. Finally, identified customer segments are profiled based on LRFMP characteristics and for each customer profile, unique CRM and marketing strategies are recommended.

The results show that there are five different customer groups and based on LRFMP characteristics, they are profiled as: “high-contribution loyal customers,” “low-contribution loyal customers,” “uncertain customers,” “high-spending lost customers” and “low-spending lost customers.”

This research may provide researchers and practitioners with a systematic guideline for effectively identifying different customer profiles based on the LRFMP model, give grocery companies useful insights about different customer profiles, and assist decision makers in developing effective customer relationships and unique marketing strategies, and further allocating resources efficiently.

This study contributes to prior literature by proposing a new RFM model, called LRFMP for the customer segmentation and providing useful insights about behaviors of different customer types in the Turkish grocery industry. It is also precious from the point of view that it is one of the first attempts in the literature which investigates the customer segmentation in the grocery retail industry.

More and more e-commerce web sites are using online customer reviews (OCRs) for customer segmentation. However, for durable products, customer purchases, and reviews only once for a long time, as while the product review score may highly affected by service factors or be “gently” evaluated. Existing regression or machine learning-based methods suffer from low accuracy when applied to the OCRs of durable products on e-commerce web sites. The purpose of this paper is to propose a new approach for customer segment analysis base on OCRs of durable products.

The research proposes a two-stage approach that employs latent class analysis (LCA): the feature-mention matrix construction stage and the LCA-based customer segmentation stage. The approach considers reviewers’ mention on product features, and the probability-based LCA method is adopted upon the characteristics of online reviews, to effectively cluster reviewers into specified segmentations.

The research finding is that, using feature-mention instead of feature-opinion records makes segment analysis more effective. The research also finds that, LCA method can better explain the characteristics of the OCR data of durable products for customer segmentation.

The research proposes a new approach to durable product review mining for customer segmentation analysis. The segment analysis result can provide supports for new product design and development, repositioning of existing products, marketing strategy development and product differentiation.

A new approach for customer segmentation analysis base on OCRs of durable products is proposed.

The purpose of this paper is to present a multichannel segmentation approach to identifying customer segments based on actual customer channel usage in the post-purchase phase in the health insurance industry.

A multinomial regression model and count regression models were estimated to describe the profiles of customer segments and the frequency of channel usage based on generations and sociodemographic variables.

This study identified generational differences in channel usage. Single female customers from the Pre-Boomer or Baby Boomer generation and customers living in states with lower incomes are more likely to use call centres. Website users tend to live in regions with higher per capita income. Multichannel users are, on average, more frequent users of both the website and call centres. In terms of sociodemographics, they display a more heterogeneous profile.

The proposed segmentation needs to be enriched with additional variables such as customers’ health status or channel usage motivations.

Customers, who are male, married and from Generations Y and X, are more likely to use the website. Their propensity to switch to a digital channel could be investigated further to develop targeted migration strategies. Multichannel users are, on average, more frequent users of all channels. To avoid increased channel costs, segments should be analysed in terms of their size and profit potential to help allocate marketing investment more efficiently.

As opposed to existing research, the proposed segmentation approach is based on transactional data of channel usage from a real company, combined with analyses using generations and sociodemographic variables.