Customer Satisfaction Metrics Research Articles

The Impact of Demand Forecasting Accuracy on Customer Satisfaction

Purpose: The aim of the study was to analyze the impact of demand forecasting accuracy on customer satisfaction Methodology: 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 field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Accurate demand forecasting significantly enhances customer satisfaction by reducing stockouts, improving delivery reliability, and aligning inventory with consumer needs. Advanced predictive models, such as AI and machine learning, enable businesses to adapt to market dynamics, fostering trust and loyalty. Studies show improved repeat purchase rates and customer retention, highlighting forecasting precision as crucial for competitive advantage. Unique Contribution to Theory, Practice and Policy: Resource-Based View (RBV) Theory, Expectancy-Disconfirmation Theory (EDT) and Systems Theory in supply chain and management maybe used to anchor future studies on the impact of demand forecasting accuracy on customer satisfaction. Research should focus on developing integrative frameworks that combine demand forecasting accuracy with customer satisfaction metrics, emphasizing the role of advanced technologies like AI and machine learning. Companies should also invest in training programs to equip supply chain professionals with the skills to utilize advanced forecasting tools effectively. Governments and regulatory bodies should establish guidelines to encourage secure data sharing between supply chain stakeholders, enhancing forecasting accuracy

Customer Perception Towards Customer-Centric Strategies of Private Sector Banks

Introduction: In the Indian financial market, banking plays a very vital role. Banking is the biggest dealer of credit and most of the people invest in banks as savings. Global economic progress depends on banking. The reason for this is that a well-developed financial sector is seen as essential and acts as a gauge for a nation's overall economic health. The banking sector is the most robust and prosperous in our nation. In addition to fierce competition among banking firms for customers, the Indian banking industry is engaged in a profit war. Try to learn about the tactics used by private sector banks in this article to draw in and keep consumers. It also makes an effort to comprehend how clients view banks in the private sector. Objectives: This study aims to explore the strategies employed by private sector banks in India to attract and retain customers within a highly competitive financial market. It seeks to understand customer perceptions of private sector banks and evaluate the role of the banking sector in supporting economic growth and financial stability. Methods: The study examines various marketing and customer service strategies used by private sector banks to attract and engage clients. It includes a review of competitive tactics within the banking sector, with a focus on customer acquisition, loyalty programs, and personalized services. Additionally, the research assesses customer perceptions through surveys and analysis of customer satisfaction metrics in relation to private sector banks. Results: The findings reveal that private sector banks employ a variety of strategies, including personalized services, digital banking options, and loyalty programs, to attract and retain customers. Customer perception of private sector banks is generally positive, with clients valuing the convenience, technological advancements, and customer-focused services offered by these institutions. The competitive nature of the banking industry has led to innovation and improved customer experience, contributing to the sector's robust growth. Conclusion: The study highlights the essential role of private sector banks in supporting the Indian economy by promoting financial inclusion and innovation. Effective strategies for customer attraction and retention, coupled with a focus on customer satisfaction, have strengthened the position of private sector banks within the competitive banking landscape. These insights can guide future efforts to enhance customer engagement and sustain growth within India's financial sector.

Non-Financial KPIs: The Relationship Between BUMDes Personnel Performance and Financial KPIs

This research aims to examine the influence of BUMDes personnel performance on financial KPIs, with non-financial KPIs serving as a mediator. The sample for this research consists of BUMDes in Bengkulu Province, with a total of 60 observations. The data used is primary data collected through questionnaires. The independent variable is BUMDes personnel performance, which is measured using four indicators: work quality, quantity of work, timeliness of work, and work effectiveness. The mediating variable is the non-financial KPI, measured by three indicators: customer satisfaction metrics, the ratio of repeat customers to new customers, and market share. The dependent variable in this research is the financial KPI, which is measured using three indicators: Gross Profit Margin, Net Profit Margin, and Current Assets. The results prove that: (1) BUMDes personnel performance has a positive and significant effect on financial KPIs; (2) BUMDes personnel performance has a positive and significant effect on non-financial KPIs; (3) financial KPIs have a positive and significant effect on financial KPIs; and (4) non-financial KPIs fully mediate the influence of BUMDes personnel performance on financial KPIs. The findings of this research strengthen stewardship theory and the balanced scorecard approach. Therefore, it is hoped that the follow-up to the results of this research will provide important insights for BUMDes managers in Bengkulu Province in designing strategies to improve organizational performance.

Unveiling Customer Contentment: A Study on Customer Satisfaction with Reference to Amazon

This research delves into the realm of customer satisfaction, focusing on the e-commerce giant Amazon. Recognizing the significance of customer satisfaction in the online retail landscape, this study employs a mixed-methods approach, incorporating qualitative insights and quantitative analyses, to comprehensively explore the factors influencing customer satisfaction and their overall shopping experiences with Amazon. The qualitative aspect of the study involves interviews, surveys, and customer testimonials to capture nuanced perspectives on what contributes to customer satisfaction during their interactions with Amazon. Simultaneously, quantitative data analysis will be utilized to identify trends, correlations, and statistical patterns associated with customer satisfaction metrics. Various dimensions of customer satisfaction will be investigated, including the impact of service quality, delivery efficiency, website usability, customer support, and product quality on overall satisfaction levels. The study aims to provide insights into how Amazon maintains high levels of customer satisfaction and navigates challenges in the highly competitive e-commerce market. His findings from this research are anticipated to offer valuable insights for Amazon, e-commerce professionals, and businesses seeking to enhance customer satisfaction. By understanding the intricacies of customer satisfaction with reference to Amazon, stakeholders can refine strategies, improve service offerings, and contribute to the ongoing evolution of customer-centric practices in the online retail industry. This study serves as a foundation for informed decision-making and future research within the realm of customer satisfaction in the context of e-commerce.

Marketing Strategy of Nykaa Case Study

In this in-depth case study, we examine the strategic endeavour carefully organized by Nykaa, a renowned and significant participant in the world of Indian beauty and cosmetics e-commerce. This investigation's main focus is on Nykaa's coordinated efforts to improve the user experience and strengthen customer engagement levels on their advanced digital platform. This study conducts an in-depth analysis of Nykaa's multifaceted marketing strategy, which was carefully planned, executed, and integrated with a wide range of interactive features. These factors have all worked together to significantly increase customer satisfaction metrics and the length of time that discerning customers spend on the website. The extraordinary achievements of Nykaa are due to their skilful application of cutting-edge technologies that are smoothly incorporated with a base of datadriven insights. Nykaa has been able to painstakingly refine the entire inline shopping experience thanks to this perfect mix. This actually improves the entire online purchasing experience. Higher client retention rates and, concurrently, a discernible and respectable uptick in total business growth are the tangible results of this optimization.

Analysis of the application effect of ecological adaptability concept in rural landscape design based on TACSI-IPA evaluation

Abstract In this research, we apply the innovative TACSI-IPA system to study how ecological adaptability can transform rural landscape design, focusing on elevating design excellence and customer contentment. We innovatively blend the TACSI model—centered on capturing tourist attraction nuances—with a sophisticated latent variable approach for a deeper dive into customer satisfaction metrics. The introduction of IPA analysis allows us to intricately map out tourists’ satisfaction levels and interests, leading to more targeted evaluation outcomes. Our practical work in Zhangzhou Wuqiao Village has shown remarkable improvements in the appreciation of both its natural and crafted landscapes. Specifically, the plant and water landscapes achieved coordination scores of 2.25 and 4.46, respectively, with the crafted amenity landscape receiving widespread acclaim and an impressive satisfaction average above 4. A comparative lens against traditional villages underscores Wuqiao Village’s standout visual appeal.

The role of marketing metrics in social media: A comprehensive analysis

In the digital era, social media has emerged as a powerful marketing tool, revolutionizing the way businesses interact with consumers. As social media platforms continue to evolve, it is crucial for marketers to effectively measure and analyze their efforts. This article explores the significance of marketing metrics in the realm of social media, highlighting the key metrics used to evaluate marketing campaigns and their impact on business success. Through an in-depth analysis of various marketing metrics, this article provides insights into optimizing social media strategies for enhanced customer engagement, brand awareness, and return on investment. The primary objective of this article is to delve into the significance of marketing metrics in the realm of social media. By examining various marketing metrics and their relevance in evaluating social media campaigns, this article aims to provide marketers with insights on how to optimize their strategies and achieve desired outcomes. The scope of this article encompasses a comprehensive analysis of key marketing metrics, including reach and impressions, engagement metrics, conversion metrics, customer satisfaction metrics, brand awareness metrics, and influence and authority metrics. This article is based on a thorough review of existing literature, industry reports, case studies, and expert opinions on marketing metrics in social media. Primary and secondary sources were consulted to gather information on the various metrics used in social media marketing and their impact on business success. The analysis and insights presented in this article are derived from a synthesis of the available knowledge and experiences shared by marketing professionals and researchers in the field of social media marketing. By exploring the importance of marketing metrics in the context of social media, this article aims to equip marketers with the necessary knowledge and tools to make data-driven decisions, optimize their social media strategies, and achieve meaningful results.

Impact of Food and Beverage Quality and Service Quality Towards Customer Satisfaction and Customer Retention, A Study of Five-Star Hotels

Purpose: The objective of this study is to examine how customer happiness at five-star hotels is impacted by the quality of the F&B they receive and the services they receive, which in turn affects retention. Design/methodology/approach: A convenience sampling strategy was utilized to distribute a self-administrated survey to 386 guests who used the F&B services of five-star hotels in Delhi-NCR. The model and study's hypotheses were evaluated in this study using a structural assessment modelling methodology, with AMOS 20.0 serving as the evaluation tool. Findings: The outcomes of the study show that CS and its impact on customer retention are significantly and favorably influenced by quality of the cuisine and beverages, as good as the service. Theoretical framework: This study recognizes the magnitude of numerous customer satisfaction metrics from a managerial perspective to predict customer retention or re- purchase plans in the standpoint of a five-star eatery in Delhi. Managers should use this to assess their performance and gain a better grasp of the standard of F&B services. Research Implications: Since the current study was restricted to the capital city of India and because the responses may have been subjective, it cannot be said to be completely conclusive. The study shows, from a practitioner's perspective, that the F&B services departments of five-star hotels in New Delhi, India, have performed notably well in terms of the quality of their services and to categorize the regions of difference in provision and F&B product quality. Originality/Value: This study fills a research gap on customers' opinions on the provision of food and beverages in the food and beverage division of five-star hotels. It also advances knowledge of visitors' expectations and perceptions of the value of the F&B services.

Does proactively asking about allergens before ordering improve customer outcomes? An in-business randomised controlled trial.

Does proactively asking about allergens before ordering improve customer outcomes? An in-business randomised controlled trial.

Factors That Attract Customers to Online Food Delivery Applications During The Pandemic in Malaysia

Digital technology is the science or engineering knowledge branch that deals with creating and practically using digital or computerised devices, methods, and systems to enhance human life. During the Virus-19 pandemic, many business sectors were affected economically because people could not go shopping, travelling, etc. One of the affected business sectors was the food business. Nevertheless, this digital technology helps to sustain the business sector affected by the covid-19 virus. Many online food services and delivery systems were grown to provide buying and selling food services to customers. This study selected eight Online Food Delivery Application (OFDA) platforms operated in Malaysia and further surveyed them to measure their customers' demography and reasons for OFDA preference. Customer demography includes gender, race, age, occupation, and state of residence. While the factors used to measure customer preference for the OFDA are based on standard criteria of customer satisfaction metrics such as user friendly, accessibility, compatibility, and response time. Finally, the customers ranked the OFDA based on additional metrics such as rider service, food quality, price of food, and food arrival time.

Customer Satisfaction Research based on Customer Service Dialogue Corpus

Recently, consumers are increasingly inclined to contact customer service for help when they encounter problems and have high demands for remote support. A high-quality customer service connects the company with its customers and establishes a positive image. Applying customer satisfaction metrics to measure the quality and efficiency of customer service is widely used, yet most of the existing customer service evaluation systems rely on manual processes, which are clearly unsustainable and costly. We introduce an ERNIE-based customer satisfaction analysis model that automatically analyses the text of customer service dialogues and scores them from four perspectives (i.e., product, service, process and overall) without human involvement. Furthermore, we construct a corpus containing around 1500 entries of dialogues texts transcribed from customer service consultation and scale it up to 9 times in the training phase. Results show that our model performs better compared to the baseline model and demonstrates a good generalization ability as well.

A Performance Evaluation of Attribute Extraction Technique for the Analysis of Sentiment

Sentiment Analysis is the process of computation identifying and categories the thought expressed by people in order to determine whether the writer’s attitude towards a particular product is positive or negative. The results of this analysis can be used in computing customer satisfaction metrics, marketing, contextual advertising, suggestion systems based on the user likes and rating, recommendation systems etc. It can be done on reviews, blogs, tweets, forums etc. Term Weighting is the process of assigning weights to the terms in the documents. Different term weighting schemes like Term Frequency, Term Frequency – Inverse Document Frequency and Binary Weighting methods are used on the reviews to weigh the sentiments words present in the reviews. It is done considering the terms as unigrams, bigrams, trigrams and Syntactic n-grams. The huge data presented to extract sentiments are unstructured in nature and they require processing like classification or clustering to get some meaningful information out of it. Supervised machine learning approaches like Naive Bayes are used to classify the human sentiments present in the reviews as positive and negative.

Quantile regression metamodeling: Toward improved responsiveness in the high-tech electronics manufacturing industry

Both technology and market demands within the high-tech electronics manufacturing industry change rapidly. Accurate and efficient estimation of cycle-time (CT) distribution remains a critical driver of on-time delivery and associated customer satisfaction metrics in these complex manufacturing systems. Simulation models are often used to emulate these systems in order to estimate parameters of the CT distribution. However, execution time of such simulation models can be excessively long limiting the number of simulation runs that can be executed for quantifying the impact of potential future operational changes. One solution is the use of simulation metamodeling which is to build a closed-form mathematical expression to approximate the input–output relationship implied by the simulation model based on simulation experiments run at selected design points in advance. Metamodels can be easily evaluated in a spreadsheet environment “on demand” to answer what-if questions without needing to run lengthy simulations. The majority of previous simulation metamodeling approaches have focused on estimating mean CT as a function of a single input variable (i.e., throughput). In this paper, we demonstrate the feasibility of a quantile regression based metamodeling approach. This method allows estimation of CT quantiles as a function of multiple input variables (e.g., throughput, product mix, and various distributional parameters of time-between-failures, repair time, setup time, loading and unloading times). Empirical results are provided to demonstrate the efficacy of the approach in a realistic simulation model representative of a semiconductor manufacturing system.

Antecedents of word of mouth behaviour among female grocery shoppers in Iran

PurposeThe purpose of this paper is to report on research on the antecedents of word of mouth (WOM) behaviour among Iranian women in the context of grocery shopping.Design/methodology/approachUsing a survey instrument, the authors empirically test the conceptual model of De Matos and Rossi.FindingsThis paper finds support for the model and finds satisfaction, commitment, loyalty, trust, perceived value and quality to be significant antecedents of WOM. Commitment is found to be the most significant predictor of WOM.Research limitations/implicationsThis paper examines only factors affecting the initiation of positive WOM; future research should examine also drivers of negative WOM. The study sample consists of female shoppers at Tehran branches of one grocery retailer. There is scope to extend the research to shoppers of other grocery retailers and to other parts of Iran.Practical implicationsThe findings provide useful insight for practitioners in identifying the relative importance of drivers of WOM. This research suggests that satisfaction is the weakest predictor of WOM, and therefore, it is dangerous for grocery retail managers to rely on customer satisfaction metrics as indicators of WOM initiation.Originality/valueThis research appears to be the first study of WOM in the context of Iranian grocery retailing, a sector which is both fast growing and undergoing structural change.

Critical Review on the Customer Satisfaction Metrics for Project Success in Construction

Critical Review on the Customer Satisfaction Metrics for Project Success in Construction

The duality of decisions and the case for impulsiveness metrics

Customer metrics help firms manage their performance and predict financial outcomes. While many firms focus on customer satisfaction metrics for this purpose, dual-process theories in psychology and neuroscience show that customer decisions are based on two processes. This suggests that metrics which measure the impulsiveness of purchase decisions might effectively complement customer satisfaction metrics. In a series of experiments we demonstrate that satisfaction and impulsiveness metrics make distinct but strong predictions of consumer choices. Satisfaction and impulsiveness influence choice in different ways. While impulsiveness relates to choice directly, the satisfaction-choice path is mediated by loyalty intention. Moreover this relationship is moderated by product involvement such that impulsiveness metrics provide a better prediction for low-involvement than for high-involvement situations. Finally, a field study of 750 customers of 101 firms demonstrates these relationships at a firm level, indicating that satisfaction and impulsiveness metrics have equally strong but distinct relationships with shareholder value. Therefore firms may be able to benefit from complementing customer satisfaction metrics with customer impulsiveness metrics.

CLOUD computing

AbstractCLOUD computing (Grid or utility computing, computing on‐demand) which was the talk of the computing circles at the end of 1990s has become once again a relevant computational topic. CLOUD computing, also considered as a fifth utility after water, electric power, gas, and telephony, is on the basis of the hosting of services on clusters of computers housed in server farms. This article reviews CLOUD computing fundamentals in general, its operational modeling and quantitative (statistical) risk assessment of its much neglected service quality issues. As an example of a CLOUD, a set of distributed parallel computers is considered to be working independently or dependently, but additively to serve the cumulative needs of a large number of customers requiring service. Quantitative methods of statistical inference on the quality of service (QoS) or conversely, loss of service (LoS), as commonly used customer satisfaction metrics of system reliability and security performance are reviewed. The goal of those methods is to optimize what must be planned about how to improve the quality of a CLOUD operation and what countermeasures to take. Also, a discrete event simulation is reviewed to estimate the risk indices in a large CLOUD computing environment favorably compared to the intractable and lengthy theoretical Markov solutions. WIREs Comp Stat 2011 3 47–68 DOI: 10.1002/wics.139This article is categorized under: Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control Statistical Models > Simulation Models Software for Computational Statistics > Software/Statistical Software

Center to Advance Palliative Care Palliative Care Clinical Care and Customer Satisfaction Metrics Consensus Recommendations

Data collection and analysis are vital for strategic planning, quality improvement, and demonstration of palliative care program impact to hospital administrators, private funders and policymakers. Since 2000, the Center to Advance Palliative Care (CAPC) has provided technical assistance to hospitals, health systems and hospices working to start, sustain, and grow nonhospice palliative care programs. CAPC convened a consensus panel in 2008 to develop recommendations for specific clinical and customer metrics that programs should track. The panel agreed on four key domains of clinical metrics and two domains of customer metrics. Clinical metrics include: daily assessment of physical/psychological/spiritual symptoms by a symptom assessment tool; establishment of patient-centered goals of care; support to patient/family caregivers; and management of transitions across care sites. For customer metrics, consensus was reached on two domains that should be tracked to assess satisfaction: patient/family satisfaction, and referring clinician satisfaction. In an effort to ensure access to reliably high-quality palliative care data throughout the nation, hospital palliative care programs are encouraged to collect and report outcomes for each of the metric domains described here.

Linking account portfolio management to customer information: Using customer satisfaction metrics for portfolio analysis

When appropriate variables are used, account portfolio analyses engender a convenient framework for the relationship composition of companies and allow management to reconsider which customers and relationship dimensions need attention. Based on an industrial company's key account relationships, the portfolio approach considered in this study employs Customer Satisfaction (CS) metrics as a portfolio dimension and suggests a new and more customer oriented approach to account portfolio analysis. Proposed portfolio matrices provide insights into the strength and stability of customer relationships. Furthermore, the matrices force managers to adopt a future perspective on customer relationships by evaluating the business potential of customers along with CS information and allow prioritization with respect to resource allocation. The study attempts to put forward customer heterogeneity in industrial markets and offers a managerial guideline embracing customer specific marketing actions. In addition, the paper proposes a new use for CS information in strategic decision making.

The Growing Divide in the Patterns World

At Microsoft, the patterns & practices group has been employing software patterns to distill, reuse, and communicate guidance about software design and development. This effort made a number of contributions to the patterns world, including several books and a community site (patternshare.org) aimed at bringing pattern authors and users together. Since then, patterns have also became a common ingredient in some of our other products, such as reusable application blocks and software factories. Our sustained investment in patterns, large user community, customer-driven development approach, and usage and customer satisfaction metrics provide us with unique insights into how practitioners use patterns. They also give us opportunities to identify and address some of their key challenges. Bridging the gap between the patterns expert community and the typical pattern user is critical to achieving the full benefits of software patterns.