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The purpose of this paper is to develop a systematic approach for business customer segmentation.

This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments.

Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as “best,” “valuable,” “average,” “potential valuable” and “potential invaluable” according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation.

The success of the proposed approach relies on the availability and quality of customers’ data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment.

The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers.

The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies.

This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.

This paper aims to examine project management segments based on customer satisfaction drivers and loyalty rather than traditional demographic or behavioural variables.

Data were gathered over 18 consecutive months, and 3,129 surveys were completed using a questionnaire. The statistical methods included partial least squares (PLS) structural equation modelling, finite mixture segmentation, prediction-oriented segmentation (PLS-POS) and multi-group analysis (PLS-MGA).

The findings indicate the existence of three segments among system delivery project customers based on the differences in the strengths of the path coefficients in the customer-centric structural model. In Segment 1, satisfaction based on the proposal was crucial for loyalty, with the value-for-money construct negatively impacting the repurchase intent construct. Segment 2 had a solid value-for-money orientation. In Segment 3, the critical path indicated that satisfaction drove repurchase intention, with satisfaction based mainly on the installation.

The research contributes to the segmentation theory by introducing a new way to segment the systems delivery projects customers based on the perceived strength of the relationships in a customer-centric structural model, which aligns with traditional segmentation theory in a way that most segmentation analyses do not. A new segmentation approach to the domain of project management theory is presented. Based on the results, treating the system delivery project customer base as a single homogenous group can lead to managerially misleading conclusions.

The purpose of this paper is to detect different behavioral groups and the dominant patterns of customer shifts between segments of different values over time.

A new hybrid methodology is presented based on clustering techniques and mining top-k and distinguishing sequential rules. This methodology is implemented on the data of 14,772 subscribers of a mobile phone operator in Tehran, the capital of Iran. The main data include the call detail records and event detail records data that was acquired from the IT department of this operator.

Seven different behavioral groups of customer shifts were identified. These groups and the corresponding top-k rules represent the dominant patterns of customer behavior. The results also explain the relation of customer switching behavior and segment instability, which is an open problem.

The findings can be helpful to improve marketing strategies and decision making and for prediction purposes. The obtained rules are relatively easy to interpret and use; this can strengthen the practicality of results.

A new hybrid methodology is proposed that systematically extracts the dominant patterns of customer shifts. This paper also offers a new definition and framework for discovering distinguishing sequential rules. Comparing with Markov chain models, this study captures the customer switching behavior in different levels of value through interpretable sequential rules. This is the first study that uses sequential and distinguishing rules in this domain.

To better understand the characteristics of Indonesian Muslims, this study uses cluster analysis to group upper-middle-class Muslims based on psychographic variables related to participation in cash waqf for productive purposes.

This study used mixed methods to build and analyse the segmentation of upper-middle-class Muslims towards cash waqf and propose scenarios for a cash waqf model based on the findings.

This study identified six clusters for upper-middle-class Muslims related to the participation in cash waqf for productive purposes. All clusters show heterogeneous values of all factors. Although relatively few Muslims perform cash waqf for productive purposes, the high scores for the economic rational, family and community factors indicate great potential for the development of various cash waqf models for investment purposes. The next challenge will lie in reviewing the “one-fits-all strategy” in the development of program, education and socialisation. Based on the findings, this study proposes three scenarios of cash waqf participation: as wakif only (waqf donor), investor only (capital provider) and hybrid participation (waqf donor and capital provider).

The limitation of this study is the location and object of the sample are only Muslims in Indonesia who are categorised as upper-middle class in terms of their monthly income. Based on this study’s findings, other Muslim-majority countries worldwide have the potential to develop a cash waqf model that is integrated with financial instruments and involves the role of Islamic banking and other Islamic commercial institutions in future research development. Researchers can also attempt to include a simulation or experiment method to construct and validate the proposed cash waqf model based on this study’s findings and to explore other factors that have not been addressed.

The findings of this study can contribute as a foundation for the development of a cash waqf model and business-marketing strategy to increase the participation of upper-middle-class Muslims.

The findings of this study will support the acceleration of cash waqf collection for investment initiatives, which in turn will have a broader social and economic impact nationally.

To the best of the authors’ knowledge, this study constitutes the first attempt to specifically investigate upper-middle-class Muslim segmentation toward cash waqf participation for productive purposes. This study’s knowledge is helpful for various stakeholders such as academia, the Islamic banking industry, regulators and the Muslim community about customer segmentation to Islamic banking products and services related to cash waqf.

Internet of things (IoT) and big data (BD) could change how the societies function. This paper explores the role of IoT and BD and their impact on customer relationship management (CRM) investments in modern customer service. The purpose of this paper is to develop an analytic hierarchy planning framework to establish criteria weights and to develop a general self-assessment model for determining the most important factors influencing the IoT and BD investment in CRM. The authors found that most studies have focused on conceptualizing the impact of IoT without BD and with limited empirical studies and analytical models. This paper sheds further light on the topic by presenting both IoT and BD aspects of future CRM.

The analytic hierarchy process (AHP) methodology is used to weight and prioritize the factors influencing the IoT and BD investment in modern CRM in the service industry. The AHP framework resulted in a ranking of 21 sustainability sub-factors based on evaluations by experienced information technology and customer service professionals.

The paper provides significant insight on the new frontier of CRM, focusing on the use of IoT and BD and the respective solutions to address them were identified. This study primarily contributes in providing the process of effectively managing and implementing IoT and BD in big businesses by identifying the connecting link between firms and customers.

The understanding of new frontier of CRM connective via IoT and BD can solve the dilemmas and challenges linked to the practice of implement IoT and BD in the information systems field. The study provides valuable information and critical analysis of IoT and BD with regard to the integration of CRM. Finally, this study further provides directions for future researchers.

IoT and BD are a growing phenomenon, which business decision-makers and information professionals need to consider seriously to properly ascertain the modern CRM dimensions in the digital economies. They also should embrace the proper CRM innovation, which is powered by IoT and BD, and discover how IoT and BD can bring the next level of maturity to CRM “CRM of everything.”

Strategic analysis of customer profitability for assessing market segmentation and reconfiguring customer relationship management (CRM) activities remains the key factor for achieving high return on CRM investment. The purpose of this paper is to map the profit-based ranking of corporate customers into the current market segments, with a view of determining the relative profitability of each market segment.

This study develops a novel model that combines activity-based costing (ABC), CRM, fuzzy analytic hierarchy process (AHP), and technique for order preference by similarity to ideal solution (TOPSIS) methods to evaluate strategically customer profitability and prioritizing corporate accounts. This case study airline company has invested heavily in CRM over the past seven years on integrating multi-functional departments that touch customers. The airline operations management and marketing functions provide key inputs. Results of the hybrid model validate feasibility of the proposed model.

The airline management makes use of the ranking results to optimize customer profitability by reconfiguring marketing programs, integrated schedule design, fleet assignment, maintenance routing, crew scheduling, and real-time optimization of schedule recovery in the aftermath of disruptions or irregularities. The proposed model also directs the marketing function to customize service offerings and introduce appropriate service levels to engage customers of different segments for the purpose of maximizing corporate profitability.

Significant amount of investment is necessary to design and implement the extensive CRM database and systems to assure customer data quality and availability so as to bear fruits in the proposed hybrid model. These data requirements can especially be a critical barrier for small to medium-sized companies.

This hybrid model is able to capitalize on the benefits of the ABC, CRM, fuzzy AHP, and TOPSIS methods and offset their deficiencies. Most importantly, it can be applied to various industries without complex modification.

This study represents the first move to adopt the fuzzy AHP and TOPSIS methods to analyze the ABC and CRM data inputs of an airline company. In mapping the profit-based ranking of corporate customers into the current market segments, the relative profitability of each market segment can be determined.

The purpose of this paper is to explore how tracking of products by the use of radio frequency identification (RFID) technology may describe customer behaviour in real-time.

The study was conducted as a field experiment, where a commercially available RFID platform was deployed in the fitting rooms of a fashion retail store.

The study demonstrates an application of in-store RFID tracking to describe customer behaviour, and some practical challenges of utilising such technology. An example typology of four fitting room traits was constructed based on the data collected.

Different customer types most likely require and respond differently to attention from the personnel operating the fitting room area. By identifying customer behaviour in real-time, it is possible to deliver “best practice” shop stewardship and create a more personalised retail experience.

The study is based on real-life retail settings, rather than anecdotal management observations or economic and demographic indicators. To the best of the authors’ knowledge, few contributions combine RFID and consumer behaviour outside conceptual work or laboratory experiments.

One of the case writers worked as a student in the summer of 2018 in EnoLight, which provided the inspiration for the case. The first author is not tied to the company and provides an unbiased perspective. The information presented in the case and the quotes were sourced from an interview with Farzad Moghiman in the Fall of 2018, email and phone follow-up with him, and information the second author remembered from his time at EnoLight, which was approved later by Farzad. Supplementary information was obtained from online sources, as cited in the case.

Farzad Moghiman, president of EnoLight, has a vision to revolutionize the use of light and bring it to the forefront of artistic designs. The company was founded in late 2016. Over a year was spent developing the business plan, finding additional partners, establishing the company as a limited partnership, finding and negotiating with suppliers and beta-testing its products. It is now time for Farzad to start selling as his funds, which were his lifetime savings, is running out. He knew the first decision to make was the target market. Identifying the first segment to target would help him select the distribution channel and other marketing plan elements.

The main objective of this case is to segment the market and identify the most attractive segment to target. This case offers an opportunity for students to segment both consumer and business markets and experience the significant impact of selecting the target market on other marketing mix elements. Furthermore, students are exposed to the difficulties of a start-up environment, resource constraints and a lack of market credibility – bearing these factors in mind while generating realistic alternatives. This case can be used in an introductory marketing course.

1. Apply segmentation variables to segment both business and consumer markets and understand how segmentation and targeting impact other marketing decisions.2. Evaluate different customer segments to select the target market.3. Develop the best positioning strategy for a new startup company.4. Recommend an segmentation, targeting and positioning (STP) plan that meets the company’s financial objective.

Introduction: Big data is that disruptive force that affects businesses, industries, and the economy. In 2021, insurance analytics will include more than simply analysing statistics. According to current trends, new insurance big data analytics (BDA) methods will enable firms to do more with their data. The insurance business has traditionally been conservative, but adopting new technology is no longer only a current trend; it must be competitive. Big data technologies aid in processing a huge amount of data, improve workflow efficiency, and lower operating costs.

Purpose: Some of the most recent developments in big data for insurance and how insurers may use the information to stay ahead of their competitors are discussed in this chapter. This chapter’s prime purpose is to analyse how artificial intelligence (AI), blockchain, and mobile technology change the outlook and working of the insurance sector.

Methodology: To achieve our research purpose, we analyse case studies and literature that emphasise how BDA revolutionises the insurance market. For this purpose, various articles and studies on BDA in the insurance market will be selected and studied.

Findings: From the analysis, we find that the use of big data in the insurance business is growing. The development of BDA has proven to be a game-changing technology in insurance, with a slew of benefits. The insurance sector is now grappling with the risks and opportunities that modern technology presents. Big data offers opportunities that every company must avail of. We can safely argue that big data has transformed the insurance sector for the better. The BDA’s consequences have enabled insurers to target clients more accurately. This chapter highlights that new tools and technologies of big data in the insurance market are increasing. AI is emerging as a powerful technology that can alter the entire insurance value stream. The transmission of any type of digital proof for underwriting, including the use of digital health data, might be a blockchain use case (electronic health record (EHR)). As digital forensics becomes easier to include in underwriting, it must expect price and product design changes in the future. In the future, the internet of things (IoT) and AI will combine to automate insurance processes, causing our sector to transform dramatically. We highlight that these technologies transformed insurance practices and revolutionalised the insurance market.

Customer lifetime value (CLV) is one of the key indicators to measure the success or health of an organization. How can an organization assess the organization's customers' lifetime value (LTV) and offer relevant strategies to retain prospective and profitable customers? This study offers an integrated view of different methods for calculating CLVs for both loyalty members and non-membership customers.

This study outlines eleven methods for calculating CLV considering (1) the deterministic aspect of NPV (Net present value) models in both finite and infinite timespans, (2) the geometric pattern and (3) the probabilistic aspect of parameter estimates through simulation modeling along with (4) the migration models for including “the probability that customers will return in the future” as a key input for CLV calculation.

The CLV models are validated in the context of complementary and alternative medicine (CAM)in the healthcare industry. The results show that understanding CLV can help the organization develop strategies to retain valuable customers while maintaining profit margins.

The integrated CLV models provide an overview of the mathematical estimation of LTVs depending on the nature of the customers and the business circumstances and can be applied to other business settings.