Identification of key customer requirements based on online reviews

2020 ◽  
Vol 39 (3) ◽  
pp. 3957-3970
Author(s):  
Nailiang Li ◽  
Xiao Jin ◽  
Yupeng Li
Keyword(s):  
Customer Satisfaction ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Customer Requirements ◽  
Kano Model ◽  
Engineering Characteristics ◽  
Quantitative Calculation ◽  
Product Engineering ◽  
Key Customer

Customer requirements are the essential driving force for successful product development. They can be grouped into several categories, including basic requirements, indifferent requirements, reverse requirements, expected requirements, and attractive requirements. Among these, the latter two are crucial for improving customer satisfaction and can be classified as key requirements. However, the literature on identifying key requirements suffers from issues related to subjective interference and the lack of a specific quantitative calculation process. Thus, this study proposes a model for identifying critical customer requirements. First, use Python to run the web crawler for extracting online customer reviews. Second, extract product engineering characteristics using the relevant text mining technology and latent Dirichlet allocation topic clustering algorithm. Third, we combine sentiment analysis and other factors that influence customer satisfaction with the product engineering characteristics to conduct the conjoint analysis and calculate utility values for the product engineering characteristics. Finally, integrate Kano model to formulate the requirements hierarchy rules, determine the final key requirements index, and identify the key customer requirements. And a case study implemented the key customer requirements identification problem for a smartphone to demonstrate the feasibility and effectiveness of the proposed methodology.

scholarly journals Business intelligence using the fuzzy-Kano model

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Soumaya Lamrharia ◽  
Hamid Elghazi ◽  
Abdellatif El Faker
Keyword(s):  
Customer Satisfaction ◽  
Business Intelligence ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Customer Requirements ◽  
Kano Model ◽  
The Voice ◽  
Allocation Approach ◽  
Voice Of The Customer ◽  
Dirichlet Allocation

Today, understanding customer satisfaction is becoming a difficult and complex task for companies due to the explosive growth of the voice of the customer in online reviews. This has pushed companies to rethink their business strategies and resort to business intelligence techniques in order to help them in analyzing customer requirements and market trends. This paper proposes a decision support framework for dynamically transforming the voice of the customer data into actionable insight. The framework measures the customer satisfaction by extracting key products’ aspects along with customers’ sentiments from online reviews using a text mining technique: the latent Dirichlet allocation approach. We apply the Fuzzy-Kano model to classify the real customer requirements, then, map them dynamically to the SWOT matrix. The proposed approach is extensively tested on an empirical dataset based on several performance metrics including accuracy, precision, recall, and F-score. The reported results showed that latent Dirichlet allocation approach has correctly extracted aspects with 97.4% accuracy and 92.4 % precision.

A systematic estimation approach for the importance of engineering characteristics based on online reviews

2020 ◽  
Vol 234 (11) ◽  
pp. 1433-1447
Author(s):  
Tao Xing ◽  
Guan Wang ◽  
Lin Yuan ◽  
Yusheng Liu ◽  
Xiaoping Ye ◽  
...  
Keyword(s):  
Opinion Mining ◽  
Online Reviews ◽  
Customer Requirements ◽  
Engineering Characteristics ◽  
Market Competitiveness ◽  
Product Engineering ◽  
Degree Of Satisfaction ◽  
Quality Function Development ◽  
Work First

Online reviews are a new source for the valuable voice of customers. By identifying the customer’s opinion, designers can comprehend the important features of a product to satisfy customer demand, thus enhancing the market competitiveness of the product. Customers have opinions on multiple aspects of products hidden in reviews, and sentiment divergence may exist. Moreover, there is a gap between customer requirements and the product’s system requirements. How to effectively analyze a large number of reviews to extract the aspect-level customer opinion and thus determine the most important product engineering characteristics in design are the critical challenges for market-driven design. A systematic requirement analysis framework is proposed in this work. First, a convolutional neural network and sentiment analysis are used for opinion mining of online reviews. Then, based on fuzzy logic, the customer sentiment divergence (which is quantified by controversy indexes) and the average sentiment of a requirement are used to determine the degree of satisfaction. Finally, based on the product’s quality function development matrix, the satisfaction and frequency of the customer requirements are used to estimate the importance of the product’s engineering characteristics, which identifies the focus of product design. A case study of a hair dryer is given to demonstrate the effectiveness of the proposed methods.

Looking beyond the stars: A description of text mining technique to extract latent dimensions from online product reviews

2019 ◽  
Vol 62 (2) ◽  
pp. 195-215
Author(s):  
Frederik Situmeang ◽  
Nelleke de Boer ◽  
Austin Zhang
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Research Methodology ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Product Reviews ◽  
Marketing Literature ◽  
Mining Technique ◽  
Online Product Reviews ◽  
The Relationship

The purpose of this study is to contribute to the marketing literature and practice by describing a research methodology to identify latent dimensions of customer satisfaction in product reviews, and examining the relationship between these attributes and customer satisfaction. Previous research in product reviews has largely relied only on quantitative ratings, either stars or review score. Advanced techniques for text mining provide the opportunity to extract meaning from customer online reviews. By analyzing 51,110 online reviews for 1,610 restaurants via latent Dirichlet allocation, this study uncovers 30 latent dimensions that are determinants of customer satisfaction. Furthermore, this study developed measurements of sentiment and innovativeness as moderators of the effect of these latent attributes to satisfaction.

scholarly journals A Novel Approach Based on Kano Model, Interval 2-Tuple Linguistic Representation Model, and Prospect Theory for Apperceiving Key Customer Requirements

2018 ◽  
Vol 2018 ◽  
pp. 1-23
Author(s):  
Aijun Liu ◽  
Qiuyun Zhu ◽  
Haiyang Liu ◽  
Hui Lu ◽  
Sang-Bing Tsai
Keyword(s):  
Prospect Theory ◽  
Product Innovation ◽  
Customer Requirements ◽  
Kano Model ◽  
Linguistic Representation ◽  
Numerical Example ◽  
Representation Model ◽  
Novel Approach ◽  
Product Function ◽  
Key Customer

The precisely perception of key customer requirements (CRs) is critically important for customer collaborative product innovation (CCPI) design. A novel approach is proposed based on the Kano model, interval 2-tuple linguistic representation model, and prospect theory. First of all, a Kano model is constructed to preliminarily screen the relatively important product function attributes. For the uncertain and vague information of CRs, an interval 2-tuple linguistic representation model is proposed to determine the weight of CRs. Then, the comprehensive prospects value is utilized for sorting the innovative programs based on the prospect theory. Finally, a numerical example is given to verify the scientific and validity of the proposed method.

scholarly journals Quality Function Deployment Implementation Based on Kano Model An Application on Mobile Vendor Product Development

2011 ◽  
Vol 9 (1) ◽  
pp. 73-77
Author(s):  
Rusdiyantoro Rusdiyantoro ◽  
Yunia Dwie Nurcahyanie
Keyword(s):  
Customer Satisfaction ◽  
Product Development ◽  
Quality Function Deployment ◽  
Effective Communication ◽  
Customer Requirements ◽  
Kano Model ◽  
Quality Function ◽  
Customer Needs ◽  
Business System ◽  
Access Information

The severe competition in the market has driven enterprises to produce a wider variety of products to meet consumers’ needs, a strategic business system allows more effective communication among different  groups at dispersed locations to share ideas and access information needed for developing new productsand executing innovative processes. The mainfunction of mobile vendor product development is to develop an attractive system which ensures customer satisfaction. Therefore, one of the important topics of the system developments is to take customer requirements into consideration.Quality function deployment (QFD) has beenwidely used for numerous years; it is one ofthe structured methodologies that are usedto translate customer needs into specific qualitydevelopment. However, in the traditional QFD approach, each element’s interdependence and customer requirements are usually not systematically treated. Additionally, the Kanomodel can effectively classify customer demandattributes, but to make Kano model moreobjective in the course of weighing.

scholarly journals Topic Modeling of Online Accommodation Reviews via Latent Dirichlet Allocation

2020 ◽  
Vol 12 (5) ◽  
pp. 1821 ◽  
Author(s):  
Ian Sutherland ◽  
Youngseok Sim ◽  
Seul Ki Lee ◽  
Jaemun Byun ◽  
Kiattipoom Kiatkawsin
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Bayesian Hierarchical ◽  
Extant Literature ◽  
Guest Satisfaction ◽  
Hierarchical Statistical Model ◽  
Different Characteristics ◽  
Dirichlet Allocation

There is a lot of attention given to the determinants of guest satisfaction and consumer behavior in the tourism literature. While much extant literature uses a deductive approach for identifying guest satisfaction dimensions, we apply an inductive approach by utilizing large unstructured text data of 104,161 online reviews of Korean accommodation customers to frame which topics of interest guests find important. Using latent Dirichlet allocation, a generative, Bayesian, hierarchical statistical model, we extract and validate topics of interest in the dataset. The results corroborate extant literature in that dimensions, such as location and service quality, are important. However, we extend existing dimensions of importance by more precisely distinguishing aspects of location and service quality. Furthermore, by comparing the characteristics of the accommodations in terms of metropolitan versus rural and the type of accommodation, we reveal differences in topics of importance between different characteristics of the accommodations. Specifically, we find a higher importance for points of competition and points of uniqueness among the accommodation characteristics. This has implications for how managers can improve customer satisfaction and how researchers can more precisely measure customer satisfaction in the hospitality industry.

scholarly journals A deeper look at the collective intelligence phenomenon

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Klaus Solberg Söilen
Keyword(s):  
Business Intelligence ◽  
Latent Dirichlet Allocation ◽  
Collective Intelligence ◽  
Online Reviews ◽  
Competitive Intelligence ◽  
The Internet ◽  
Kano Model ◽  
Intelligence Studies ◽  
Search Data ◽  
Google Search

For the upcoming conference on Intelligence Studies at ICI 2020 in Bad Nauheim, Germany the focus of this issue of JISIB is on collective intelligence and foresight. The first two papers by Søilen and Almedia and Lesca deal with collective intelligence from an intelligence studies perspective. It may be said that the Internet itself is a gigantic collective intelligence effort, the largest in human history. Open source is a prerequisite for this system to work for everyone. The article by Černý et al. is on open source. All other contributions are on the connection between the Internet, software and intelligence. This issue consists of seven articles to compensate for two articles that were taken out by editors in the last issue. The first article by Søilen entitled “Making sense of the collective intelligence field: a review” is a historical review of the field of collective intelligence. The paper shows how collective intelligence is an interdisciplinary field and argues there is a flaw in the notion of “wisdom of crowds”. Collective intelligence can be understood in terms of social systems theory and as such this approach has been fruitful for the social sciences, although so far not very popular. It also bares relevance for the study of business and economics. The second article by Almeida and Lesca is entitled “Collective intelligence process to interpret weak signals and early warnings”. Early warning and the detection of weak signals is a vital topic for any intelligence organization. Two aspects are discussed in the paper, the importance of new technology and collective sense making or interpretation The third article by Shaikh and Singhal entitled “Study on the various intellectual property management strategies used and implemented by ICT firms for business intelligence” deals with intellectual property rights and patenting strategies. The authors identify a number of defensive and offensive IP strategies applied to ICT companies. The results have a bearing on patent acquisitions. The fourth article by Lamrhari et al. is entitled “Web intelligence for understanding customer satisfaction: application of Latent Dirichlet Allocation (LDA) and the Kano model”. Customer satisfaction today is mostly measured with data from the internet, using different business intelligence techniques. The Kano model is still valuablei,ii, but the way we gather information to assess the different levels in the model has changed. The authors use Latent Dirichlet Allocation to analyze the voice of customer (VOC) in online reviews. They suggest that BI techniques and a fuzzy-Kano model can enable companies to better understand their customers’ online reviews. The fifth article by Nahili et al. is entitled “A new corpus-based convolutional neutral network for big data text analysis”. Companies need efficient ways to analyze everything that is said about them on the internet (reviews, comments). The paper suggests a convolutional neural network (CNN) as it has been successfully used for text classification. IMDB movie reviews and Reuters datasets were used for the experiment. The sixth article by Černý et al. is entitled “Using open data and google search data for competitive intelligence analysis”. Taking the Czech antidepressant market as an example, the authors show how competitive intelligence can be obtained using Google Search data, Google Trend and other OSINT sources. The seventh article by Dadkhah et al. is entitled “The potential of business intelligence tools for expert findings”. The paper suggests a way for researchers to find experts using business intelligence tools. The same method may also be used by any business or person looking for experts on a specific topic. As always, we would above all like to thank the authors for their contributions to this issue of JISIB. Thanks to Dr. Allison Perrigo for reviewing English grammar and helping with layout design for all articles and to the Swedish Research Council for continuous financial support. We hope to see you all at the ICI 2020 on the 16-17 March, 2020. The deadline for the two-page abstract submission is March 1st, 2020.

Customer satisfaction in purchasing mobile phones: An explorative study

Think India ◽  
2019 ◽  
Vol 22 (3) ◽  
pp. 1751-1757
Author(s):  
Rohith Raja M ◽  
Ida David
Keyword(s):  
Customer Satisfaction ◽  
Mobile Phones ◽  
Current Trend ◽  
Essential Element ◽  
Manufacturing Companies ◽  
Customer Requirements ◽  
Business World ◽  
Large Market ◽  
Explorative Study

Customers hold the key role in determining the market of any material. The demand and requirements of the customers are taken into account while the manufacturers produce any product. Mobile phones, being an essential element in today’s world, occupy a large market in today’s business world. Customer satisfaction is the prime motive of the manufacturing companies. Based on a survey conducted, we study the current trend of phone purchase in India and thus analyze the customer’s responses towards different brands and their products. The results depict which brands satisfy the customer requirements.

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