Voice Of Customer Research Articles

The Mediating Role of Customer Commitment in the Relationship Between Customer-Company Fit and Customer Voice: An Empirical Study on Restaurant Customers

Objective: This study discusses value congruence (person-organization fit) and voice behaviors, which are frequently discussed in the field of organizational behavior, in the context of marketing. The study aims to examine the effect of customer-company fit (CC fit) on customer voice behavior as well as the role that customer commitment plays in this effect. Design/methodology: Research data was obtained from 390 restaurant customers in a province in Turkey through the convenience sampling method using the survey technique. The SmartPLS program (PLS-SEM) was used to conduct the analyses. The research model was analysed using partial least squares path analysis (PLS-SEM) and bootstrapping (5,000 bootstrap samples). Findings: The findings indicate that CC fit directly affects both customer commitment and customer voice behavior. Furthermore, the findings revealed that customer commitment plays a mediating role CC fit’s effect on customer voice behavior. Theoretical and Practical implications: The present study is the first in the literature to discuss the various relationships between these variables (CC fit, customer commitment and customer voice) together. Another distinctive feature of the study is that the sample consists of restaurant customers. The high-contact nature of restaurant service makes service failures inevitable. For this reason, it is much more important to reveal customer voice behaviors in restaurant services than in other sectors. In this respect, the results of the study may provide guiding information to the sector.

ECOBRICK SOFA DESIGN IS ENVIRONMENTALLY FRIENDLY USING THE HOUSE OF QUALITY METHOD AND THE BOSTON CONSULTING GROUP (BCG) METHOD

Plastic is one of the longest decomposing types of waste with a decomposition time of up to 10 centuries. Plastic waste that is difficult to decompose can be minimized by using ecobrick techniques. This ecobrick technique aims to turn plastic waste that was initially worthless (zero value) into a product that has added value. This study uses four methods, namely the House of Quality (HOQ), Quality Function Deployment (QFD), Slovin, and Boston Consulting Group (BCG) methods. These methods are used to determine product planning and development, consumer desires, product position in market share, and systematic evaluation of its ability to produce products to satisfy consumers. This study involved 100 respondents obtained from the slovin method. Where the respondents are positioned as research subjects. The respondent data was obtained from distributing questionnaires through google forms. The data collected is calculated and analyzed for unique and minimalist sofa House of Quality (HOQ). The results of this study inform that customer feedback on the product or Voice of Customer can affect quality control and improvement by using the statistical method or Statistical Quality Control. Furthermore, unique and minimalist sofa companies each have different product development focuses. Following up on the findings in this study, the company can adjust the needs and desires of these consumers to the design and service standards as well as the obstacles that may occur.

Amplifying the Customer Voice: Unleashing the Power of Influencer Partnerships and Co-creation to Drive Viral Growth

Amplifying the Customer Voice: Unleashing the Power of Influencer Partnerships and Co-creation to Drive Viral Growth

Decision-Making Model Supporting Eco-Innovation in Energy Production Based on Quality, Cost and Life Cycle Assessment (LCA)

Currently, the development of renewable energy products (RES) encourages the search for innovative solutions that take into account key criteria from the point of view of their sustainable development. Despite efforts in this area, there is a lack of approaches and tools to support this process. Therefore, the aim of the research was to develop a decision-making model supporting eco-innovation in products based on the key criteria of sustainable development: quality (customer satisfaction with use), environmental impact in the life cycle (LCA), and the cost of investment incurred in the product development. The functioning of the model was based on the following factors: (i) obtaining the voice of customers (VoC) and processing it into product criteria as part of the development of alternative production solutions (prototypes), (ii) prospective quality assessment and subsequent life cycle assessment of prototypes, (iii) cost analysis taking into account both quality and environmental criteria, (iv) interpretation of results and search for eco-innovative product solutions. Development decision-making is additionally supported by techniques implemented in the model, e.g., the CRITIC method, the LCA method with Ecoinvent database in OpenLCA, the CEA method, and the morphology method. The model was illustrated and tested for photovoltaic (PV) panels, after which a global sensitivity analysis was performed in Statistica. The test results showed that the main factor that influenced the PV development decisions was the investment cost, followed by quality (customer satisfaction) and then environmental impact in LCA.

Efforts to Improve Service Quality at Skenario Kopi Cafe Based on Consumer Needs Using Quality Function Deployment

Faced with fierce competition, coffee shops need to stay in tune with evolving customer preferences. This research was conducted the Quality Function Deployment (QFD) method, specifically the House of Quality (HoQ) tool, to analyze feedback from subject consist of 100 customers of Skenario Kopi Cafe. The HoQ is a structured approach that translates customer desires (Voice of Customer or VoC) into actionable technical improvements. The sampling technique is based on purposive sampling technique. Analysis of Validity Test and Reliability Test data using SPSS 24 software. The research identified 15 key customer needs, categorized by their level of importance. They then compared these needs to 5 potential technical responses that Skenario Kopi Cafe could implement. Based on this analysis, the result showed such as prioritizing communication training for staff, followed by facility upgrades, and lastly, implementing regular performance evaluations. By analyzing the relationships between these factors, the researchers were able to prioritize the actions that would have the greatest impact on customer satisfaction.

Implementation of Digital Marketing through Social Media to Increase The Number of Visits at Ujung Suso Beach Tourism, East Luwu District

This research aims to understand how to improve the number of visitors to the Ujung Suso beach tourist destination after implementing digital marketing. The research method used is qualitative descriptive research. Data collection was carried out through observation, interviews and documentation techniques as well as using Voice of Customer (VOC) analysis. The data collected is processed by data reduction, data presentation, and drawing conclusions. Based on the research results, it shows that the implementation of digital marketing via social media Facebook and Instagram from February to March succeeded in increasing the number of visits to Ujung Suso beach tourism. This is proven by the increase in the number of visitors during the research period compared to the period before the implementation of digital marketing. So it can be said that digital marketing through social media is an effective strategy to increase tourist visits to Ujung Suso beach.

The application of the KANO model in education: A systematic literature review

As markets become more competitive, customer satisfaction becomes one of the most critical factors of success for any organization, especially in the service industry, where the opportunity for customer delight can be limited. Customer requirements and their perceptions of quality can differ significantly from organizations' views. Therefore, employing tools to bridge this gap and gaining insights into the voice of customers is imperative for organizations. The Kano model provides a framework for understanding the impact of quality attributes on customer satisfaction. The Kano model has been used in many different industries over the years. Multiple case studies have applied the Kano model to various service industry sectors from healthcare to tourism. owever, the use of the Kano model in education remains limited. This study provides a systematic literature review of the use of the Kano model in education. This study aims to examine the extent to which the Kano model is applied as a quality improvement tool in the education sector and to identify research gaps in the literature for future research.

고객 서비스 개선을 위한 비정형 텍스트 데이터 자동 분류 모델 비교 - 고객 불만 데이터를 대상으로

With the advancement of information technology and the proliferation of digital communication, there is an increasing need for effective management and processing of the growing volume of customer complaints and feedback. Particularly, when most of the data is unstructured, it is crucial to quickly process and classify this data to improve customer service. In this study, we examine traditional classification models such as Naive Bayes, SVM, and Random Forest, as well as deep learning models like CNN and LSTM. These models were applied to Boston's traffic complaint data to compare the performance of automatic classification of customer complaint data. The results showed that CNN and LSTM achieved high classification accuracies of 81% and 97%, respectively, confirming their effectiveness in handling complex and diverse patterns of unstructured data, such as customer complaints. These findings demonstrate that deep learning models are better at analyzing the characteristics of customer complaint data and detecting contextual connections. This study provides a methodology for companies to quickly and accurately understand and respond to customer voices, thereby enhancing corporate competitiveness.

Cultural values and voice as determinants of customers' marketing research cooperation: A fuzzy set perspective

AbstractFirms and their managers have long been concerned with encouraging customers to take on an active role in their marketing research activities. The critical question for these firms is how to foster collaboration with customers in marketing research to enhance the firm's marketing effectiveness and simultaneously benefit the customers. This study focuses on the role that individuals' cultural values and customer voice potentially play in shaping how customers perceive marketing research cooperation with the firm. We analyzed data from a sample of 270 customers of apparel store brands in an African country. Utilizing fuzzy‐set qualitative comparative analysis, our research reveals that the combinations of antecedent conditions that enhance marketing research cooperation significantly differ from those that impede it. Theoretically and managerially, our study expands the understanding of how marketing research cooperation is contingent upon the combination of customers' cultural values and their voice.

Roles of Natural Language Processing in New Product Development Process: Literature Review

Customer satisfaction is a key success factor for a business. To provide products that meet customer satisfaction, companies must be able to understand the customers’ needs and desires. Technological developments nowadays have helped companies to understand customer desires more easily so that companies can provide products that satisfy their customer. Natural Language Processing (NLP) is a technology that allows computers to process human language. NLP is also commonly referred as text-mining. NLP has been utilized in the New Product Development (NPD) process. We compiled studies related to NLP and NPD and conducted a literature review to map out how far NLP has been utilized in NPD processes. We found that in this era of Big Data, current NLP studies most often have the goal to process text data from online reviews on e-commerce and from social media. By using NLP, large amounts of data can produce valuable Voice of Customer (VOC) information for product development. We also found that NLP technology also has been utilized in other NPD processes that do not involve VOC, such as the design stage, document processing, and extraction of requirements in the NPD process.

Online Review Analysis from a Customer Behavior Observation Perspective for Product Development

Observing customers is one of the methods to uncover their needs. By closely observing how customers use products, we can indirectly experience their interactions and gain a deep understanding of their feelings and preferences. Through this process, companies can design new products that have the potential to succeed on the market. However, traditional methods of customer observation are time-consuming and labor-intensive. In this study, we propose a method that leverages the analysis of online customer reviews as a substitute for direct customer observations. By correlating a customer journey map (CJM) with online reviews, this research establishes a verb-centric analysis that produces a CJM based on online review data. Various text analysis techniques were utilized in this process. When applying online retail site review data, our method of customer observation required one week. This proved to be more efficient in comparison with traditional customer observation methods, which typically need at least one month to complete. Additionally, we observed that the customer behavior-based VOC (voice of customer) identified during the CJM mapping process offers broad insights that are distinct from traditional product feature-centric review analyses. This behavior VOC can be effectively utilized for product improvement, new product development, and product marketing. To verify the usefulness of the behavior VOC, we asked product development experts to evaluate the quantitative analysis results of the same reviews. The experts evaluated the CJM as useful for product conceptualization and selecting technology priorities.

Redesain Bioreaktor Guna Mempercepat Proses Fermentasi Pembuatan Pupuk Organik di Desa Minggirsari Kecamatan Kanigoro Kabupaten Blitar

Alongside the increasing potential of the agriculture and plantation sectors in Minggirsari Village, Blitar Regency, there have been concerns among farmers regarding the decreasing supply of both subsidized and non-subsidized chemical fertilizers. However, these concerns were validated with the invention of an automatic anaerobic bioreactor by Nawang Sonia in 2023. The device is capable of reducing the fermentation process time from the initial 14-21 days to just 3-5 days. Although the designed device still had some functional limitations hindering its performance, a redesign was deemed necessary. The redesign was conducted using the Quality Function Deployment method through the distribution of questionnaires or Voice of Customer. Subsequently, a House of Quality was obtained, which served as the basic concept for the device's redesign. From this research, eight features were added to the device. The results showed that the device was more optimal, flexible, and effective than the previous anaerobic bioreactor design.

Integrating object detection and natural language processing models to build a personalized attraction recommendation agent in a smart product service system

Product Service System (PSS) is a new business model that integrates tangible products and intangible services to better meet customer needs and expectations. In recent years, scholars had some efforts to enhance the capability of PSS with the support of artificial intelligence (AI) which is known as Smart PSS(SPSS). So far, most previous studies adopted a single model which cannot handle multiple tasks simultaneously that results in unsatisfactory services to customers. In addition, customer preference cannot be fully addressed to achieve personalization. Therefore, this study proposes a method that integrates multiple models into an SPSS with three steps: (1) Collect data and construct an appropriate object detection model. (2) Develop smart PSS solutions. Then, (3) Optimize the system based on feedback through Natural Language Processing (NLP) to provide customers with personalized services. An attraction recommendation case study with experiment is designed to verify the proposed method. The results show that proposed SPSS can optimize the system in time according to the feedback of users, and provide better personalized services. This research is the first research that utilizes applied both text and image data to extract customer characteristics to better capture the voice of customers.

A Framework for Mining Speech-to-Text Transcripts of the Customer for Automated Problem Remediation

Technical support services get several thousand voice calls every year. These calls vary across a range of technical issues or maintenance requests for a suite of hardware and software products. On receiving the call, a support agent creates a ser- vice request artifact that contains her interpretation of the customer’s problem. This service request goes through the life cycle of the problem remediation process with the resolution also being recorded as part of the service request. It has been empirically observed that the actual complaint voiced by the customer is often different from the recorded interpretation in the service request. The service request created by sup- port agents runs the risk of missing key information elements present in the customer voice records. In this paper, we build a framework that taps into voice calls and uses unsupervised and supervised learning methods to enrich the service requests with additional information. The enriched data is then used for automated problem resolution.

Harnessing customized AI to create voice of customer via GPT3.5

The integration of customer feedback is universally acknowledged as crucial in the product development process. Yet, traditional feedback collection methods employed by companies, such as interviews and surveys, have remained mainly unchanged and come with limitations. Interviews often fail to accurately capture customers' needs due to communication barriers, while surveys prompt only incremental changes instead of inspiring innovation. This challenge is compounded in the service industry, where feedback is intangible and more difficult to quantify. Text analysis presents a promising solution to delve into customer preferences more deeply, providing insights that can guide the development of new products and services. Our research advances the use of generative AI, specifically the GPT engine, beyond its conventional role as a chatbot. We innovate by adapting it to extract actionable insights from customer-service interactions, offering real-time, valuable data for decision-making and representing a significant leap forward in Voice of the Customer (VoC) analysis.

An integrated decision framework for evaluating and recommending health care services

Quality management techniques such as the quality function deployment model can help hospitals assess and improve the quality of their services by integrating the voice of customers. The different quality parameters of this model are usually determined and assessed by experts; nonetheless, obtaining such experts is not always easy or inexpensive. Moreover, in this method, patient opinions are not usually considered directly, although they are the real users of the services and those who can best assess those services. Nevertheless, these opinions are easily accessible today, owing to the development of medical social networks where patients directly convey their opinions about the different services and features of a hospital. Therefore, it is feasible to replace expert knowledge with the information provided by these opinions. Based on this idea, this study proposes a novel fuzzy recommendation model based on the quality function deployment method to rank hospitals depending on patient opinions and preferences regarding hospital services. This model integrates a topic modeling strategy for determining hospital requirements, customer needs, and the relationship between them as well as a sentiment analysis algorithm for assessing customer satisfaction regarding hospital services. To demonstrate the usefulness of the proposed method, several experiments were conducted using patient reviews from real hospitals, and the method was compared against other recommendation models. The results prove that this approach represents a step toward more personalized and effective health care system selection considering patient preferences and opinions.

How to deeply understand the voice of the customer? A proposal for a synthesis of techniques for analyzing online reviews in the hospitality industry

PurposeThis study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.Design/methodology/approachThis study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.FindingsExperimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.Originality/valueThe novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.

Custom Voice Cloner

The Custom Voice Cloner is based on voice signal speech synthesizer. It is a technology that converts text into audible speech, simulating human speech characteristics like pitch and tone. It finds applications in virtual assistants, navigation systems, and accessibility tools. Building one in Python typically involves Text-to-Speech (TTS) libraries such as gTTS, pyttsx3, or platform-specific options for Windows and macOS, offering easy text-to-speech conversion.However, TTS libraries might lack customization and voice quality needed for advanced projects. For more sophisticated applications, custom voice synthesizers can be built using deep learning techniques like Tacotron and WaveNet. These models learn speech nuances for more natural output.Creating a custom voice synthesizer is challenging, requiring high-quality training data, machine learning expertise, and substantial computational resources. It goes beyond generating speech to convey emotions and nuances in pronunciation for natural and expressive voices. Key Word: Voice signal speech synthesizer,text-to-speech conversion, deep learning,TTS, gTTS, pyttsx3,etc.

Development of Traditional Market Garbage Bin

A garbage bin is a tool for handling waste in the Demangan and Sentul markets. Garbage leachate is terrible for the environment and humans and is a location for Aedes aegypti mosquitoes to breed due to ammonia compounds in leachate. This research aims to redesign the garbage bin so that it is optimal for controlling and managing waste and minimizing its impact. The garbage bin is designed using the Quality Function Deployment method. The garbage bin design has nine attributes from the customer's voice and sixteen technical response attributes. The technical response attribute becomes the part deployment input, resulting in twenty one part specification attributes. The priority attribute divides the shelter into two parts: the organic and non-organic waste has CW=662.16. The developed garbage bin design advantages are storage space according to type, wastewater storage, and minimizing waste's destructive impact.

Formula Optimal dalam Penentuan Aspek Penting pada Desain Alat Pemotong Ring AMDK Gelas Plastik Menggunakan Metode Quality Function Deployment (QFD)

Drinking water waste in plastic cups is increasing every year, which has an impact on environmental sustainability, so there is a need for equipment solutions for processing this waste. The aim of this research is a formulation to determine important aspects in the design of ring-cutting tools for plastic cup bottled drinking water. The method used is Quality Function Deployment (QFD) by considering user aspects through Voice of Customers (VoC) with 10 needs. This data is converted into 9 technical data which is analyzed with the House of Quality (HoQ). The results show priority improvements for the development of these tools, namely: manual cutting tool design (46,318), intuitive cutting tool design (45,227), adjustable tool holders and cutters (43,636), portable cutting tool design (33,591), components using standard parts (25,136 ), steel material (25,091), affordable tool manufacturing costs (20,455), non-corrosive material (17,955) and cylindrical and non-slip handle shape (12,273). This data is used as a basis for further research to design this tool. Keywords: Plastic Cups, QFD, Ring Cutting