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The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.

In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.

The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.

This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.

This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.

Enterprises build online product community to expect users to contribute: opinion sharing (content contribution) and product consumption (product contribution). Previous literature rarely focused on both. The purpose of this paper is to explain user contribution mechanism by identifying content contribution and product contribution.

This research chose Xiaomi-hosted online product community (bbs.xiaomi.cn) and Huawei-hosted online product community (club.huawei.com) where users can freely share ideas and buy products at the same time. Data were crawled from 109,665 community users to construct dependent variable measurement, and 611 questionnaires were used to verify research hypotheses.

The results indicate that both cognitive needs and personal integration needs have a significant positive impact on browse behavior; social integration needs and hedonic needs have a significant positive impact on content contribution behavior. Browse behavior not only directly affects but also indirectly influences product contribution through content contribution behavior.

Findings of this research provide community managers with useful insights into the relationship between content contribution and product contribution.

This study explains the formation mechanism of user product contribution and reveals the relationship between user content contribution and product contribution in online product community. This paper provides a different way of theorizing user contributions by incorporating uses and gratifications theory into the “Motivation-Behavior-Result” framework.

The purpose of this paper is to investigate the distinct reasons and more common reasons that reduce customer satisfaction and are antecedents to customer churn behavior in the telecommunication industry.

The study adopted the netnography approach to investigate churn behavior by utilizing online user-generated content in qualified social media communities.

The investigation revealed that “data speed issue”, “ineffective relationship building”, “service area coverage issues” and “billing issues” are some of the most important attributes that influence a consumers' decision to churn. Further, the churn consequence influencers model summarizes the attributes that contribute to overall dissatisfaction and finally results in churn behavior. The study found out the application of the netnography approach in a quantitatively dominant research area and stands out with its insights from a rich qualitative data.

Proper clarification of customer expectations and pain points can help reduce customer churn. The study will serve as the basis for developing future churn prediction models that will contribute to the informed decision-making process.

Contributing to research on customer churn behavior, the study offers a novel attempt to study customer satisfaction and customer churn behavior jointly. The paper is the first attempt that contributes to the extant literature by adopting the unique qualitative approach to understand the reasons for telecommunication churn behavior in the emerging Indian market. Another contribution of this research is that the paper shifts the focus of the electronic word-of-mouth (eWOM) literature to the telecommunications industry, thus adding another block to ongoing research in eWOM communication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ OIR-02-2020-0048

Online customer relationship management (CRM) is an important issue for implementing digital marketing of electronic commerce or social commerce. The purpose of this study is to establish valuable markets for discovering customer knowledge from data-driven CRM systems for enhancing growth rates of businesses. Airline or travel agency industries are online businesses in the world. Therefore, the industries in Taiwan will be an empirical case for this study.

This research applied a procedure with an applied proposed model for establishing valuable markets from data-driven CRM systems. However, the study used a proposed customer value model (recency, frequency and monetary [RFM]; RFM model-based), the analytic hierarchy process (AHP) procedure and a proposed equation for estimating customer values.

For enhancing the data-driven CRM marketing of the industries, in this research, the market of air travelers can be partitioned into eight markets by the proposed model. As well, the markets can be ranked by the AHP procedure. Furthermore, the travelers’ customer values can be estimated by a proposed customer value equation.

Via the applied proposed procedure, online airlines, travel agencies or other online businesses can implement the research procedure as their data-driven marketing strategy on their online large-scale or Big Data customers’ databases for enhancing sales rates.

Service providers leverage their corporate reputation management efforts to increase revenues by shaping customer attitudes and behaviours, yet the effects on customer innovation adoption and customer value remain unclear. In an extended conceptualisation of customer-based corporate reputation (CBR), the purpose of this paper is to propose that customer perceived risk, perceived value, and service separation are contingencies of the relationship between CBR and two key customer outcomes: customer new product adoption proneness (CPA) and recency-frequency-monetary (RFM) value.

Using a predictive survey approach, 1,001 service customers assess the online or offline operations of six multichannel retailers. The hypothesised model is tested using structural equation modelling and multigroup analysis.

The analysis reveals significant linkages of CBR with perceived risk and perceived value, as well as between perceived risk and perceived value and from perceived value to CPA and RFM value. These linkages vary in strength across unseparated (offline) and separated (online) services.

This study uses cross-sectional data to contribute to literature that relates CBR to relevant customer outcomes by considering CPA and RFM value and investigating contingent factors. It provides conceptual and empirical evidence that price appropriateness represents a new CBR dimension.

The results reveal that CBR reduces customers’ perceived risk and positively affects their perceived value, which drives CPA and RFM value. Multichannel retailers can create rewarding customer relationships by building and nurturing good reputations.

This study is the first to link CBR with customer product adoption proneness and value, two important customer measures. It proposes and tests an extended conceptualisation of CBR.

The Purpose of this study is to provide an alternative way to create customer valuation metric while accounting for customer riskiness. Customer relationship management (CRM) emphasizes the importance of measuring customer value. Analytics has paved the way for innovation by providing companies valuable insights into the behavior of customers. Earlier models used to measure customer value do not take into account the types and level of risk posed by customers, such as probability of churn, regularity of purchases, etc. The authors put forth a new and innovative approach to measuring customer value while, at the same time, adjusting for customer riskiness.

Using a non-parametric approach used in the operations research area, the authors create a risk-adjusted regency, frequency, monetary value (RARFM) score for each customer. These scores are used to segment the customers into two groups – customers with high and low RARFM scores. The authors then identify the underlying demographics and behavioral characteristics that separate the two groups.

Findings of this paper indicate that customers who perform the best on the RARFM metric tend to be more experienced, and are more likely to exhibit behavioral tendencies that help them perform well in their jobs, such as purchasing promotional goods that act as sales aid and enhance their performance.

The paper is innovative in its approach in terms of creating a new metric for calculating customer value. Few papers have proposed ways to handle and adjust for customer riskiness. Here, the authors propose three kinds of customer risk. Current paper provides a twist to traditional RFM analysis by creating a RARFM score for each customer, and provides a scientific way of assigning weights to RFM.

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.

Nowadays, the agricultural business environment is expended to the whole world. Transaction records in point of sales and customer relationship management (CRM) systems can be large-scale data for long-established global chain businesses. Thus, the purpose of this paper is to using a proposed data mining approach to discover valuable markets/customers of urban coffee shop industry (retailer) in current environment of Taiwan, which can implement the industry's data-driven marketing strategy on a CRM system.

In this research approach, Ward's method, C5.0 decision tree and a proposed model are applied for discovering valuable markets and mining useful customer rules.

These found markets and discovered rules can be applied on marketing information or CRM system for identifying valuable customers and target markets.

In this study, the CRM system can be the media for the data-driven marketing strategy in environment of Taiwan. The approach of this research can be applied on other businesses for their data-driven marketing strategies as well.

Database marketers often select households for individual marketing contacts using information on past purchase behavior. One of the most common methods, known as RFM variables approach, ranks households according to three criteria: the recency of the latest purchase event, the long-run frequency of purchases, and the cumulative dollar expenditure. We argue that RFM variables approach is an indirect measure of the latent purchase propensity of the customer. In addition, the use of RFM information in targeting households creates major statistical problems (selection bias and RFM endogeneity) that complicate the calibration of forecasting models. Using a latent trait approach to capture a household's propensity to purchase a product, we construct a methodology that not only measures directly the latent propensity value of the customer, but also avoids the statistical limitations of the RFM variables approach. The result is a general household response forecasting and scoring approach that can be used on any database of customer transactions. We apply our methodology to a database from a charitable organization and show that the forecasting accuracy of the new methodology improves upon the traditional RFM variables approach.