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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.

In today’s dynamic business environment, it’s crucial for companies to employ robust strategies and decision-making tools to remain competitive. This chapter explains the key comprehensive policies that can be applied by any company to maximise the effectiveness of the APPNIE framework. These policies include the following key elements for effective use of the APPNIE model: appointing an APPNIE manager responsible for maintaining and updating the model, collaborating with top executives, and formulating action plans; raising awareness of the APPNIE manager’s role across the organisation to encourage sharing valuable information (establishing clear procedures and channels for data collection); encouraging the communication of valuable data by offering recognition or rewards, ensuring a steady flow of filtered information; monitoring the eight quadrants using qualitative and quantitative data for up-to-date assessments of key APPNIE’s factors; organising regular meetings between APPNIE managers and directors from different functions to share perspectives, discuss action plans, and address challenges and opportunities. The chapter shows that by adopting these practices, companies can navigate ‘complexity’, make informed decisions, and enhance their overall success and competitiveness in the market.

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.

Food delivery applications (FDAs) are becoming more and more well-liked across Generations X, Y and Z, with Asia experiencing the biggest growth. These three generations of consumers have distinct views toward using FDAs because they were influenced by various social environments, cultures and experiences. Therefore, marketers ought to present customers with various values. The purpose of this study is to investigate how the theory of consumption values (TCV) affects the intention of various generational cohorts to continued usage intentions.

Online surveys were distributed to 745 Thai customers who had previously ordered meals from FDAs to collect information. The data were analyzed using partial least squares structural equation modeling and multigroup analysis.

Results reveal that there are significant differences among Generations X, Y and Z, while emotional value is insignificant. Generation X is concerned about functional and conditional value, whereas Generation Y expresses the highest concern on epistemic and social value. Meanwhile, Generation Z pays attention to epistemic, functional and social value.

The novelty of this study contributes to TCV affecting the different FDAs consumption value among Generations X, Y and Z, while the previous research only focused on gender and age difference, and this study firstly combines FDAs with TCV to predict the consumers’ intention to continuously use FDAs in the post-COVID-19 outbreak.

The study’s objective is to measure the importance consumers attach to AI-based attributes, namely, chatbots, face recognition, virtual fitting room, smart parking and cashier-free station in retail stores. The study also examines the specific purpose of using these attributes for shopping.

A conjoint experiment was conducted using fractional factorial design. Consumers were given 14 profiles (AI attributes and its levels) to rank according to their visiting preferences.

The results revealed that the retail chatbot was considered the most important attribute, followed by face recognition, virtual fitting room, smart parking system and cashier-free station. Moreover, consumers prefer to use chatbots for in-store shopping assistance over alerts and updates, customer support and feedback. Similarly, consumers wish a face recognition facility for greetings while entering the store over other services. In addition, cluster analyses revealed that customer groups significantly differ in their preferences for AI-based attributes.

The study guides retail managers to invest in AI technologies to provide consumers with a technology-oriented shopping experience.

Our results provide an insight into the receptivity of AI technologies that consumers would like to experience in their favorite retail stores. The present study contributes to the literature by investigating consumer preferences for various AI technologies and their specific uses for shopping.

With rapidly changing marketing landscape, nowadays, the formulation of various marketing strategies is increasingly focused on how consumers tend to make decisions. To meet the highly demanding consumer expectations, market segmentation can be used as an important marketing strategy. Due to gender marketing concept familiarity in the contemporary world, gender difference is one of the reference features in the process of market segmentation for marketers. This research is aimed to examine various determining factors that foster consumer purchase decision-making and the differences between consumers of different genders while making shopping and purchase decisions with special reference to an emerging economy, i.e. Pakistan.

Based on a cross-sectional sample of 367 consumers, the study adapted Sproles and Kendall's (1986) Consumer Style Inventory (CSI) to scrutinize the decision-making of both genders in Pakistan. For data analysis, the exploratory and confirmatory factor analysis in addition to the structural equation modeling has been used.

The study emphasized that, with the exception of quality awareness, brand consciousness, fashion consciousness, option overload and price consciousness greatly affect buying decisions. In addition, when it comes to consumer purchase decision-making, significant gender variations were discovered for both fashion consciousness and price consciousness.

Drawing upon the distinctive cultural characteristics of Pakistan and its people, in-depth research was conducted on purchasing behaviors of Pakistani consumers and the decision-making characteristics of customers of different genders were summarized. The outcomes are expected to make a significant contribution to the field of gender marketing by organizations.

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.

Considering the speedy growth of Islamic finance and limited research work on Muslim behavior regarding Islamic Banking, this study aims to investigate to comprehend the stimulus of religiosity on customer’s behavior.

A conceptual model is developed on existing literature. The key dimensions of religiosity in the model include practice, knowledge, experience and consequences to capture the whole religiosity of customers. Model of the study investigates the impact of customer’s religiosity on their behavior in decision-making about selection of Islamic bank. Analysis of the study is based on the sample of 370 customers of Islamic banks from District Nowshera Khyber Pakhtunkhwa, Pakistan. The data for the study collected through random sampling by a comprehensive survey questionnaire. Binary logistic model is used to test the data for statistical analysis.

The key findings of the study suggest that religiosity influence customer behavior positively in decision-making regarding Islamic finance. Service standards of Islamic banking has also significant impact on customer perception, while the financial education of the customers has insignificant impact on customer behavior.

This study mainly focused on the curiosity of the customer religious commitment, so religiosity is a vast phenomenon; there are deep sections in each dimension of religiosity, so further study is suggested for the comprehensive capture of each dimension of religiosity.

The results of the study have a great importance for the managers of Islamic finance industry to identify and detect the potential customers and divide the target market of banking industry on the base of religiosity. Furthermore, the study may bring significant managerial suggestions for marketing planners and can help them in market segmentation strategies.

The study examined the association between Muslim religiosity and Islamic banking customer’s selection behavior. This study spread the understanding of religiosity and its impact on Islamic banking customer’s behavior. Furthermore, the study is valuable to discover the level to which religiosity determines the inclinations of customers. This study helps marketing practitioners and researchers to grow their knowledge about customer’s motives in terms of religious commitment.

The objective of this study is to analyze the experiences in wine tourism according to narratives shared by the visitors themselves. Furthermore, this study aims to examine the levels of intensity associated with these experiences within a wine destination, considering the segmentation of visitors.

This study uses a mixed-methods approach to analyze data, incorporating a deductive process followed by content analysis. Data collection procedures include a bibliographic review and a data survey conducted through netnography research to analyze 954 visitor reviews on TripAdvisor shared by visitors to Vale dos Vinhedos. Also, statistical analysis is performed to assess whether there are any significant variations in attribute citations among different market segmentation profiles.

The study’s discoveries indicate that there are no significant differences in intensity among profile segments for the dimensions of entertainment, aesthetics, educational and interactions, unlike escapism. The findings reveal that attributes such as “winery,” “wine,” “products and services” and “landscape” are essential for all visitors. In addition, the study shows that social interactions in the wine tourism destination are not as significant as previously assumed.

This research study constitutes a methodological advancement in the field of market segmentation using electronic word-of-mouth data. It provides crucial insights into the experiential nuances of the research locus and the varying degrees of these experiences in relation to visitor segmentation. Additionally, the contributions of this study are not only of theoretical importance but also hold practical implications for market segmentation strategies.