Enhance Customer Relationship Research Articles

Impact of Days to Receivable on the Profitability of Exporting Coal Companies in Indonesia

This study examines the effects of exchange rates, export intensity, and days to receivable on the profitability of Indonesian coal exporting companies, using panel data from 2015 to 2023. The objective is to find if export intensity and exchange rate impedes the effect of days to receivable on profitability. Interaction analyses reveal that the positive impact of receivables on profitability diminishes with increasing exchange rates. Conversely, the interaction between days to receivable and export intensity does not significantly affect profitability. Therefore, it might be beneficial for Indonesian coal companies should explore implementing more flexible trade credit policies to enhance customer relationships, particularly during periods of rupiah appreciation.

The Impact of Sports Background on Sales Performance of Sporting Goods: Evidence from Companies in Pingdingshan

Sales performance is an important indicator of business operations, especially in the field of sporting goods sales, but less attention has been paid to the role of sales staff’s professional background on performance. This article focuses on the impact of sports professional background on sales performance. The results show that sports professional background significantly improves the subjective and objective performance of salespeople. Further analysis found that this influence mechanism is mainly reflected in three aspects: first, sports expertise provides unique advantages for sales; second, good interpersonal skills enhance customer relationship management; third, strong sports enthusiasm stimulates sales enthusiasm. Finally, this article puts forward corresponding management suggestions, such as giving priority to sales staff with a sports professional background, strengthening targeted training and incentives, etc., in order to provide reference for the construction of sales teams of sports brand enterprises.

Big data analytics capabilities and leadership: catalysts of firm performance in telecommunications

PurposeThe proliferation of big data analytics (BDA)-enabled tools and technologies has endowed organizations with the capacity to augment decision-making processes, optimize operational endeavors and foster innovation across diverse business domains. Consequently, BDA has been posited as a catalyst for enhanced customer relationship management, improved risk mitigation strategies and heightened operational efficiencies, all of which converge to augment overall firm performance. Thus, the purpose of this research is to introduce a conceptual framework aimed at explaining the influence of BDA capabilities on the performance of telecommunications firms in Pakistan. Additionally, it examines the potential mediating effect of talent capabilities and moderating effect of top management attitude on firm performance.Design/methodology/approachData from a sample comprising 520 participants were collected via survey questionnaires. The study employed Partial Least Squares-Structural Equation Modeling to empirically evaluate the proposed model.FindingsResults reveal a positive association between BDA technology and information capabilities with both BDA talent capabilities and firm performance. Furthermore, the analysis suggests that BDA talent capabilities mediate the relationship between BDA dynamic capabilities and firm performance, while top management attitude acts as a moderator, enhancing the relationship between BDA talent capabilities and firm performance.Originality/valueThere is a scarcity of research that has examined the relationship of BDA capabilities, top management attitude and firm performance. This study attempts to examine their interrelationships. First, it enhances the extant literature by elucidating the mediating role of BDA talent capabilities in the relationship between BDA technology and information capabilities and firm performance. Second, the study introduces a novel dimension by incorporating top management attitude as a moderator variable. This augmentation adds layers of complexity to comprehending BDA implementation dynamics, emphasizing leadership’s role in fostering an enabling environment for effective utilization of BDA capabilities.

Enhancing Customer Relationships Through Social Media by Transitioning from Traditional to Social CRM

Enhancing Customer Relationships Through Social Media by Transitioning from Traditional to Social CRM

Evaluating the Impact of App Quality, Product Attributes, and Customer Relationships on Hotel-Booking App Purchases: Insights from Malaysian Users

Traditional hotel bookings have declined due to changes in travel patterns and evolving consumer preferences. Understanding factors driving the surge in booking-app usage is crucial for hotel businesses to adapt and capitalise on the growing demand for digital booking platforms. This study investigates the factors influencing consumer purchase decisions on these apps, utilising an online questionnaire as a primary method of data collection, which conveniently inviting Malaysian hotel-app users to respond. The findings indicate that app quality, product-related attributes, and customer relationships significantly impact customer purchase decisions. Inferentially, Consumer-Relationship Factor (CRF) shows a medium, positive correlation, indicating that Malaysians highly value customer relationships and past experiences within the app, which have significantly impacted their purchase decisions. Simultaneously, this study also indicates a strong influence of customer relationships on hotel-booking app usage. Hence, the study may suggest that hotel operators enhance customer relationships by conducting satisfaction surveys, providing employee training, and utilising customer-relationship management tools to foster business growth. This is because prioritising customer relationships can lead to increased loyalty, repeat business, and positive reviews, which contribute to success in the hospitality industry.

Role of Machine Learning Algorithms in Enhancing Customer Relationship Management Systems in Rwanda

Purpose: The aim of the study was to assess the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Materials and Methods: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Algorithms analyze vast amounts of data, allowing businesses to personalize interactions and anticipate customer needs more effectively. For instance, predictive analytics can forecast customer churn, prompting proactive retention strategies. Moreover, sentiment analysis tools gauge customer emotions from feedback, enabling tailored responses and improved service delivery. Machine learning also automates routine tasks like email marketing segmentation, optimizing resource allocation and enhancing overall efficiency. Consequently, CRM systems empowered by machine learning algorithms foster stronger customer relationships, driving business growth through enhanced customer satisfaction and loyalty. Implications to Theory, Practice and Policy: Technology acceptance model (TAM), resource-based view and diffusion of innovations theory may be used to anchor future studies on assessing the role of machine learning algorithms in enhancing customer relationship management systems in Rwanda. Businesses should adopt advanced ML techniques, such as Long Short-Term Memory (LSTM) models and ensemble learning, to enhance the accuracy of customer segmentation and churn prediction, leading to more personalized marketing strategies and improved customer retention. Policymakers should develop and enforce stringent data privacy and security regulations to protect customer data used in ML-enhanced CRM systems.

Managing Rapport on TripAdvisor: Correlation of Negative Reviews and Response Voices on Online Business Platforms

This study explores the impact of negative reviews and responses on rapport management, synthesizing current research on this topic. Employing formulated research questions and statistical methods like chi-square tests, this study systematically analyzed collected data both quantitatively and qualitatively. Results indicate significant differences in response voices—accommodative, formalistic, and defensive—among various interpersonal relationship management styles when addressing negative reviews. These findings emphasize the complexity of managing interpersonal relationships amidst criticism, offering theoretical insights and practical guidance for improving customer rapport management in industries like hospitality. By examining how different management voices respond to negative reviews, this study contributes a fresh perspective to enhancing customer relationship practices in service sectors.

Enhancing Customer Relationship Management with Data Analytics: Insights from Retail Survey

In the contemporary digital era, understanding the role of Data Analytics Capabilities (DAC) in Customer Relationship Management (CRM) systems is essential for effective marketing decisions. DAC refers to the skills and knowledge in using technological tools, techniques, and methodologies to extract meaningful and actionable insights from various data sources. The originality of the study lies in providing an external perspective from customers on the benefits of effective DAC to improve the performance of CRM systems in the retail industry. Specifically, it evaluates the roles of technological CRM system quality, employee interaction-management capabilities empowered with the CRM system, and the DAC outcomes in enhancing the overall customer satisfaction for both online and in-store experiences. The methodology employs a quantitative approach that includes a survey for data collection of 380 retail customers, an exploratory factor analysis and a multi-linear regression analysis. Theoretically, findings advance our understanding of retail CRM systems by providing empirical evidence quantifying the positive impact of DAC outcomes on customer satisfaction. Practically, results offer managerial insights for leveraging technology and human capabilities to enhance in-store and online customer relationships. Acknowledging geographical and industry limitations, future research could explore the research model across various contexts.

Enhancing Customer Relationships: A Case Study of CRM Practices at City Square Mart in Ahmedabad city of Gujarat State

This study paper provides an in-depth examination of customer relationship management (CRM) operations conducted at City Square Mart, a well-known retail business specializing in supermarket trends. This research seeks to provide insights that will improve the development and implementation of CRM strategies in the retail industry by conducting interviews with store managers and analyzing their CRM methods. This study analyzes key elements of consumer engagement channels, such as in-store experiences and online platforms, to understand how City Square Mart effectively combines these channels to provide a seamless and cohesive customer experience. This research also examines consumer feedback methods, loyalty programs, complaint management processes, and initiatives aimed at personalization and customization of goods. The results of this research provide important insights for retail managers who want to improve their CRM strategies and for academics who want to increase their understanding of customer relationship management in the retail industry. This research aims to improve customer interactions and drive company success in the changing retail market by analyzing the unique CRM tactics implemented by City Square Mart

Golden Arches of Success: Enhancing Customer Relationship Management at McDonald's, Wide Angle, Ahmedabad

Customer Relationship Management (CRM) is crucial in the retail business, especially in the highly competitive fast-food sector. This research examines the customer relationship management (CRM) strategies used by McDonald's, Wide Angle in Ahmedabad. The objective is to get insights into their methods of managing customer relationships and improving corporate performance. The goals include evaluating current CRM strategies, identifying strengths and areas for improvement, and offering practical suggestions to increase CRM performance. The study used a qualitative research methodology, including semi-structured interviews with branch managers to get in-depth understanding of McDonald's customer relationship management (CRM) activities. The key results include McDonald's tactics for managing data, techniques for segmenting customers, avenues for contact, and programmes for fostering loyalty. The investigation demonstrates a significant focus on customisation, the use of technology, and the incorporation of client feedback methods. The strengths that have been highlighted include the implementation of comprehensive communication strategies and the successful implementation of loyalty programmes. On the other hand, the problems that need to be addressed are the integration of feedback into practical and effective initiatives.

Calculating Customer Lifetime Value through Data Analysis

Customer Lifetime Value (CLV) is a crucial metric for businesses to understand the long-term value of their customers. This research paper delves into the significance of CLV, explores various methods for calculating it through data analysis, and discusses the implications of accurate CLV estimation on business strategies. By leveraging data analytics techniques, businesses can gain valuable insights into customer behaviour, preferences, and profitability, ultimately leading to improved decision-making and enhanced customer relationships. Customer Lifetime Value (CLV) is a vital metric for businesses to comprehend the long-term value of their customers. The research paper highlights several key points regarding CLV: Calculation Factors: CLV calculations should consider indirect costs like administrative, marketing, and legal costs, not just direct expenses. Limitations: CLV based solely on spending may not fully represent a customer's value, as factors like brand advocacy, referrals, and feedback also contribute. Historical Data: CLV is often calculated using past customer data, which may not accurately predict future customer behaviours due to changing trends and customer preferences. Pareto's Principle: This principle, known as the 80/20 rule, can be applied to CLV to identify high-value customers and key factors influencing CLV. Overall, the paper emphasizes the importance of understanding CLV beyond mere spending, considering historical data limitations, and utilizing tools like Pareto's Principle to optimize customer value and business strategies.

Messenger Marketing Platforms and Performance of Tour Operating Companies in Kenya

Messenger marketing is part of global digital trend that acts as a new avenue for tour firms to adopt based on convenience in interaction, ability to reach more audiences and collaboration with customers. The study aimed to examine the effect of messenger marketing on performance of tour operating companies in Kenya. The study was anchored on diffusion innovation theory. Cross-sectional and correlational research design were utilized. The study applied census approach to a target population of 212 general managers of tour operating companies. Online and physical questionnaires were used to collect primary data. The supervisors and experts were used to examine validity of the instrument while Cronbach Alpha was adopted to test reliability was 0.783 which was above a threshold of 0.7, hence reliable. Descriptive statistics consisting of mean and standard deviations were used to summarize data while simple linear regression was used to test significance at 5%. The results revealed that messenger marketing platform was practiced by the tour operating companies through using mobile services platform for sending messages, chatting and soliciting sales. It also assisted in obtaining customer feedback, blogging and chatting with customers. However, the Chatbots were hardly used by firms which adopted artificial intelligence in communicating, sending random messages and could be integrated to existing online platforms. The results indicated that messenger marketing had positive significant effect on the performance of tour operating companies (β1=0.428, P = 0.000<0.05). The study concludes that messenger marketing platform marketing significantly influenced performance of tour operating companies. The study recommends tour operating companies should apply Chatbots, mobile service, mobile commerce and conversation commerce to enhance customer relationship and improve performance

Exploring Sentiment Analysis in Indian Regional Languages: Methods, Challenges, and Future Directions

Sentiment Analysis, pivotal in natural language processing, extends its reach beyond English to Indian regional languages like Hindi, Marathi, Kannada, Konkani, Bengali, Khandeshi, and Urdu. This paper presents a comprehensive survey of 32 research papers in this domain, examining methodologies, datasets, and techniques while emphasizing the significance of sentiment analysis in diverse linguistic contexts for enhancing customer relationship management functionalities. It underscores the necessity for future research and highlights the efficacy of machine learning techniques. By elucidating on computational challenges and outlining various sentiment analysis methods, this paper serves as a critical resource for researchers and practitioners, fostering advancements in sentiment analysis tailored to regional linguistic nuances. KEYWORDS Bag Of Words, Hindi, Kannada, RNN, Konkani, Malayalam, Marathi, Maximum Entropy, Naive Bayes, Sentiment Analysis, SVM, TF-IDF, Urdu.

Enhancing Customer Relationship Management in ERP Systems through AI: Personalized Interactions, Predictive Modeling, and Service Automation

Enhancing Customer Relationship Management in ERP Systems through AI: Personalized Interactions, Predictive Modeling, and Service Automation

The Influence of Green Marketing on Repurchase Intention and Customer Loylaty Through Customer Satisfaction as a Mediation Variable

This research aims to determine the direct influence of green marketing on repurchase intention and customer loyalty and how much customer satisfaction is a mediating variable between Green Marketing on Repurchase Intention and Customer Loyalty on Uniqlo products. The research method used uses a quantitative approach with the explanatory research type. The sample used was 270 respondents, and data collection was done using a questionnaire. The dependent variable in this research is Green Marketing (X). In contrast, the independent variables are Repurchase Intention and Customer Loyalty (Y), while the Customer Satisfaction variable is a Mediation variable (Z). Data analysis used in this research uses the Smart Partial Least Square (PLS) method. The study's hypothesis testing yielded significant findings. Firstly, green marketing positively impacts customer satisfaction, loyalty, and repurchase intention for Uniqlo products, supported by prior research. Secondly, customer satisfaction and loyalty are interlinked, as validated by existing literature. Thirdly, the influence of green marketing through customer satisfaction and repurchase intention significantly affects customer loyalty. These findings emphasize the importance of green marketing strategies in enhancing customer relationships and brand loyalty for Uniqlo products.

Customer Relationship Management as a Firm-level Factor and its role in Firm Performance: A Study on the Firms Listed at Nairobi Securities Exchange

The study sought to examine the relationship between customer relationship management and performance of firms listed at Nairobi Securities Exchange. While the listed firms remain integral in the country’s economy, their continued performance has been declining over the years. This has seen some of the firms get delisted and others suspended from trading due to subsequent years of loss-making. Customer relationship management on the other hand has been found to be instrumental in determining the performance of modern firms, in the midst of growing competition and dynamic operating environment. The motivation of this study was therefore the limited literature on the relationship between customer relationship management and performance of listed firms. The study utilized a descriptive-exploratory research design, where 240 senior management personnel drawn from the 64 listed firms were surveyed using a structured questionnaire. The data was analysed using SPSS and Amos software for descriptive statistics, regression model and structural equation modelling respectively. The findings revealed that customer relationship management had a significant influence on the performance of firms listed at the NSE (R2 = .265; β = 0.353; P=0.0001<0.05). It was therefore concluded that customer relationship management as a frim-level factor is instrumental in determining the performance of the listed firms at the NSE. The study recommended the need for the management of the listed firms to be at the forefront of pushing for enhanced customer relationship management so as to strengthen the firms performance.

Impact of Electronic Customer Relationship Management and Quality Moderated by Sharia Compliance on Loyalty (Study on Islamic banks in Jakarta)

In the rapidly digitizing landscape of Islamic banking, innovation is imperative for competitiveness and delivering top-tier services to customers. Electronic Customer Relationship Management (E-CRM) emerges as a key solution, streamlining interactions between banks and customers in the digital realm. This study delves into how E-CRM and Service Quality affect Customer Loyalty within Islamic banks, with a focus on Shariah Compliance as a moderating factor. The aim is to decipher how the Islamic banking sector can cultivate and enhance customer relationships in the digital age. Employing a quantitative approach, the research collects survey data from 212 Islamic bank customers through random sampling. Structural Equation Modeling (SEM), executed using IBM AMOS V.22 software, scrutinizes the collected data. Findings indicate a significant, positive contribution of E-CRM to Customer Loyalty. Interestingly, Service Quality doesn't wield a significant influence on Customer Loyalty, suggesting the presence of other influential factors in the Islamic banking arena. Notably, Shariah Compliance emerges as an important moderating element, bolstering the connection between Service Quality and Customer Loyalty, though it doesn't moderate the impact of E-CRM on Customer Loyalty.

Guest Editorial: Unleasing the Power of Customer Advisory Boards Combined With a Customer Value-Based Strategy

_ In today’s rapidly evolving business landscape, innovation has become the key to success. Companies that can adapt, innovate, and stay ahead of the curve not only survive, but thrive. To achieve this, a fresh approach to innovation is required: one that harnesses the collective wisdom of customer advisory boards and prioritizes a strategy based on customer value. In this article, we delve into the value of co-creating innovation through these two critical elements. The Power of Customer Advisory Boards Customer advisory boards are invaluable assets for any forward-thinking, innovative organization. They are not just a forum for feedback, but a mechanism for co-creating value and fostering innovation. They serve as a strategic tool for an organization to gain valuable insight, enhance customer relationships, and drive innovation. The facilitated collaboration leads to improved products, market-driven innovation, stronger customer relationships, and market success. The diverse backgrounds and expertise of the customers who make up an advisory board provide a holistic perspective that internal teams might lack. Here’s how advisory boards contribute to innovation. Unbiased Insights and Feedback. Customer advisory board members bring fresh eyes to a company’s products and challenges and provide a structured platform for customers to voice their opinions, share experiences, and offer feedback on products and services as well as the overall customer journey. They offer impartial insights and constructive criticism, allowing for a more accurate assessment of innovation efforts. Market Validation. These boards typically comprise individuals with deep industry knowledge. Their expertise helps identify emerging trends and opportunities, ensuring that innovation efforts are aligned with industry advancements and customer expectations; thus, avoiding costly misinnovation from misalignment with the market. This ensures that when a product is developed, there is market demand for the product. Enhanced Customer Relationships. Customer advisory boards can open doors to new partnerships, collaborations, and resources. They connect organizations to a broader ecosystem, fostering innovation through collaboration. Co-Creation of Solutions. Customer advisory boards enable collaborative problem solving and solutions. Customers, as experts in their fields, actively participate in ideation and development of new products and features to solve their challenges and address future business requirements. This co-creation ensures solutions meet specific customer needs driven by current challenges. A collective voice of the board ensures solutions and products are not too customized for a specific customer but fit the needs of the broader market experiencing the same challenges.

Structural Model for Analyzing the Impact of Social CRM on Customer Relationship Performance in Automotive Manufacturing Company

Social Customer Relationship Management (S-CRM) has been widely adopted by companies, including Indonesia's Automotive Manufacturing Companies (AMC). The adoption of S-CRM is aimed at enhancing customer relationship performance. However, there is limited research on implementing S-CRM can effectively support Customer Relationship Performance (CRP) in AMC. To address this gap, this research describes the conceptual model that illustrates the impact of S-CRM and other related factors on supporting CRP in AMC using Partial Least Square Modeling (PLS-SEM) method. The research's results reveal seven variables, eight hypotheses, and 45 indicators that can be used to increase CRP in AMC by implementing S-CRM. Result showed that only seven out of eight hypotheses are supported. This research can be used to provide recommendations for future studies on CRP in AMC, contributes to developing knowledge regarding integrating S-CRM in AMC and provides a basis for further research to enhance CRP in the company.

Unraveling The Influence of Factors on Micro Small and Medium-Sized Enterprises’ Adoption of Social Media Marketing and Its Business Impact

The continuously growing opportunities in the digital economy have prompted the Indonesian government to encourage Micro, Small, and Medium-sized Enterprises (MSMEs) to undergo digital transformation. While there are various approaches available, marketing products/services through social media is believed to be one of the most effective methods. To maximize the benefits of social media marketing adoption for MSMEs, it is indeed crucial to identify the influential factors related to its adoption. Therefore, this study aims to quantitatively identify the factors influencing social media adoption among MSMEs in Indonesia. In this study, attention is directed toward five factors: performance expectancy, social influence, effort expectancy, facilitating conditions, and operating costs. The study incorporated 136 actively participating MSMEs that employed social media marketing. We opted to employ Partial Least Squares Structural Equation Modeling (PLS-SEM) due to its suitability for our sample size and potential data characteristics. As we were unsure about the normality of our data and the presence of measurement issues, PLS-SEM provided a robust and flexible approach. It allows us to analyze complex models and handle potential non-normal data and measurement concerns, ensuring reliable results even if such issues arise. Data is processed using the SmartPLS 3.0 software. The research findings indicate that the usage of social media marketing is influenced by performance expectancy and social influence. However, factors such as effort expectancy, facilitating conditions, and costs do not have a significant impact on social media adoption among MSMEs. Furthermore, the results of the study highlight the positive impact of utilizing social media for marketing on the performance of MSMEs, particularly in terms of increasing sales, enhancing customer relationships, improving productivity, and fostering creativity. These findings suggest that MSMEs can leverage social media platforms to achieve tangible benefits and enhance various aspects of their business operations.