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As e-commerce has expanded rapidly, online shopping platforms have become widespread in India and throughout the world. Product return, which has a negative effect on the E-Commerce Industry's economic and ecological sustainability, is one of the E-Commerce Industry's greatest challenges in light of the substantial increase in online transactions. The authors have analyzed the purchasing patterns of the customers to better comprehend their product purchase and return patterns.

The authors utilized digital transformation techniques-based recency, frequency and monetary models to better understand and segment potential customers in order to address personalized strategies to increase sales, and the authors performed seller clustering using k-means and hierarchical clustering to determine why some sellers have the most sales and what products they offer that entice customers to purchase.

The authors discovered, through the application of digital transformation models to customer segmentation, that over 61.15% of consumers are likely to purchase, loyal customers and utilize firm service, whereas approximately 35% of customers have either stopped purchasing or have relatively low spending. To retain these consumer segments, special consideration and an enticing offer are required. As the authors dug deeper into the seller clustering, we discovered that the maximum number of clusters is six, while certain clusters indicate that prompt delivery of the goods plays a crucial role in customer feedback and high sales volume.

This is one of the rare study that develops a seller segmentation strategy by utilizing digital transformation-based methods in order to achieve seller group division.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

In 2007, Best Buy was the leading electronics retailer in the United States with more than 941 stores, revenue totaling $31 billion, and a market cap of $21 billion. In 2005, Best Buy had adopted a new business model, culture, and customer-segmentation template called Customer Centricity. This move created volatility in the price of Best Buy stock because of the higher-than-expected employee costs that went with this new way of doing business and the difficulty of executing the old and the new business models simultaneously while the new model was rolled out. Best Buy responded to Wall Street's short-term focus in a myriad of ways. It first asked for investor patience, and stressed the strong operating results achieved in Best Buy stores operating under the new model. But in June 2007, after the stock dropped again, the CEO knew he had to decide whether to open more Best Buy stores, increase the company's dividend, or increase the stock-repurchase program.

The versatility of customer relationship management (CRM) systems has kept these technologies popular over the years. These solutions have been integrated into organizations of all sizes, from large corporations to small- and medium-sized enterprises. Similarly, CRM systems have also found applications in all types of industries and business sectors. All this has been the driving force behind the proliferation of CRM solutions around the world. In this chapter, the author not only reflects on the impact and democratization of CRM systems on business management and marketing strategies but also explores how these technologies can determine the company's income. In particular, the author presents an experiment that analyzes the extent to which the volume of annual investment in CRM solutions can be used to predict annual net income in a sample of companies. Using time series analysis and applying the autoregressive integrated moving average modeling technique, the researcher examines a sample of 10 companies from different industries, and countries, over a 20-year period. The results show the efficiency of the predictive models developed in nine of the 10 companies analyzed. The findings of this study allow us to conclude that there seems to be an association between the investments made in CRM solutions and the income of the companies that invest in these technologies.

The primary aim of this study is to gain insight into the entrepreneurial marketing strategy (EMS) decisions made by microenterprises in the craft sector and draw comparisons between the marketing strategy formulation and implementation of conventional businesses and those of craft-based microenterprises with a specific focus on the context of emerging markets, particularly India.

This paper follows a qualitative interpretivist paradigm using a multiple-case methodology approach. It tracks craft-based microenterprises that make furniture or home décor products in India. The study participants were the founders, principal designers, studio managers or craftspersons.

This study’s findings reveal that craft-based microenterprises implement an EMS that adopts a hybrid form of market orientation strategy. In this approach, the product or creative concept is at the centre of the decision-making, and the customer needs are factored in at a later stage for customisation. These microenterprises prioritise product positioning over segmentation and targeting strategies.

This study tries to understand marketing strategy decision-making processes among craft-based microenterprises in India. Given that study participants came from only two-product-based craft businesses, this limits the generalisability of the findings to similar or related contexts. This study provides a framework and methodology for replication in other contexts and industries to formulate a nuanced understanding of micro, context-specific, craft-based businesses.

This study uses qualitative analysis to understand EMS in craft-based businesses in India. This study contributes to this fledgling stream of literature at the interface of marketing and entrepreneurship to understand entrepreneurial marketing. This study analyses the marketing strategy of craft-based businesses using the framework of Morgan et al. (2019).

This study aims to investigate the engagement gap between Metaverse and in-person travel, the influence of Metaverse tourism on tourists and the industry and the challenges and responses associated with Metaverse technology. The study presents practical cases and highlights the implications of this research for practice, society and future research.

This study uses a literature review to explore concerns about Metaverse technology in tourism. It analyzes the difference between in-person travel and Metaverse tourism, the impact on tourists and the industry and challenges and responses to Metaverse. The review shows a rising trend in Metaverse tourism research.

These findings suggest differences between Metaverse tourism and in-person travel. By providing personalized travel options, social interaction, immersive experiences and soliciting visitor feedback, it is possible to enhance the tourist experience. Additionally, the study highlights the opportunities and challenges that Metaverse tourism presents to the tourism industry. The study provides practical cases in the tourism industry and implications for practice, society and future research.

The study’s implications for Metaverse tourism are practical, societal and future research-related. Metaverse technology can enhance the tourist experience through personalized options, social interaction, immersive experiences and feedback. This inclusivity can promote social equity and cultural exchange. Further research is needed to explore the social effects of Metaverse tourism and its long-term impacts on local communities, economies and the environment.

This study contributes by exploring the impact of Metaverse tourism, supporting academic research and practice. It fills a knowledge gap by analyzing the application of Metaverse technology in tourism, providing insights for researchers and practitioners. It offers practical guidance by identifying opportunities and challenges in Metaverse tourism, fostering industry innovation. Additionally, it informs policymakers about the impact of Metaverse tourism on development.

This study aims to capture how the association between a multichannel relational communication strategy (MRCS) and customer performance is contingent upon such customer performance (low- versus high-performance customers) and to reconcile past contradictory results in this marketing-related topic. To this end, the authors propose and validate the method of quantile regression as an unconventional, yet effective, means to proceed to that reconciliation.

This study collected data from 4,934 customers of a private pension fund firm and accounted for both firm- and customer-initiated relational communication channels (RCCs) and for customer lifetime value (CLV). This study estimated a generalized linear model and then a quantile regression model was used to account for customer performance heterogeneity.

This study finds that specific RCCs present different levels of association with performance for low- versus high-performance customers, where outcome customer performance is the dependent variable. For example, the relation between firm-initiated communication (FIC) and performance is stronger for low-CLV customers, whereas the relation between customer-initiated communication (CIC) and performance is increasingly stronger for high-CLV customers but not for low-CLV ones. This study also finds that combining different forms of FIC can result in a negative association with customer performance, especially for low-CLV customers.

The authors tested the conceptual model in one single firm in the specific context of financial services and with cross-sectional data, so there should be caution when extrapolating this study’s findings.

This study offers nuanced and precise managerial insights on recommended resource allocation along with relational communication efforts, showing how managers can benefit from adopting a differentiated-customer performance approach when designing their MRCS.

This study provides an overview of the state of the art of MRCS, proposes a contingency analysis of the relationship between MRCS and performance based on customer performance heterogeneity and suggests the quantile method to perform such analysis and help reconcile past contradictory findings. This study shows how the association between RCCs and CLV varies across the conditional quantiles of the distribution of customer performance. This study also addresses a recent call for a more holistic perspective on the relationships between independent and dependent variables.

This case reviews the development of Dianping. After seeing Zagat's unique business model in the United States, founder Zhang Tao found that he could bring it to China and bring about local innovation. At the beginning of its establishment, the collection and promotion of comment content was the major challenge for Dianping. At the same time, Dianping faced legal issues. To solve these problems, the review mechanism of Dianping was designed to a certain extent to ensure the fairness of the review. With the advent of the mobile Internet era, Dianping began to develop a new business model. Relying on its high-quality “word-of-mouth” content and mass basis, Dianping launched group buying, online restaurant ordering, and other businesses. Dianping has always been open to strategic partners. Since 2015, Dianping has undergone historical changes, merging with Meituan. Since then, Dianping has continuously adjusted its business and organizational structure to maintain its competitiveness. Gradually, Dianping has changed from an independent business entity into a business unit of Meituan.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

This study aims to address the critical need for innovative financing solutions in the global housing sector, focusing specifically on Malaysia’s distinct housing finance system encompassing both conventional and Islamic loans. The primary objective is to develop a transformative housing finance model that addresses affordability challenges and reshapes the Malaysian housing landscape.

The study presents an alternate housing finance model for Malaysia, integrating lower monthly payments and reduced household debt. Key variables include house price appreciation rates, interest rates, initial guarantee fees and loan-to-value ratios. Inspired by the Help to Buy (HTB) scheme, the model aligns with proven global initiatives for enhanced affordability, balancing payment amounts, loan interest rates and acceptable price thresholds.

The study’s findings promise to address affordability disparities and reshape Malaysia’s housing finance landscape. The emphasis is on introducing a structured repayment plan that offers a sustainable path to homeownership, particularly for low-income families. Incorporating the future value adaptation concept, inspired by reverse mortgages and Islamic finance, enhances adaptability, ensuring long-term sustainability despite economic shifts.

The proposed model promotes widespread access to homeownership, offering practical solutions for policymakers to improve affordability, prompting adaptable risk management strategies for financial institutions and empowering potential homebuyers with increased flexibility.

The study introduces a transformative housing finance model for Malaysia, merging elements from reverse mortgages, Islamic finance and the HTB scheme, offering potential applicability to similar systems globally.