Customer Preference Analysis on Fashion Online Shops using the Kano Model and Conjoint Analysis

2015 ◽  
Vol 6 (5) ◽  
pp. 881 ◽  
Author(s):  
Amalia Suzianti ◽  
Nurza Dwi Prisca Faradilla ◽  
Shabila Anjani
Keyword(s):  
Conjoint Analysis ◽  
Kano Model ◽  
Preference Analysis ◽  
Customer Preference ◽  
Online Shops

CONJOINT ANALYSIS VS PREFERENCE ANALYSIS: A COMPARISON

1987 ◽  
Vol 60 (3c) ◽  
pp. 1063-1068
Author(s):  
THOMAS R. SCHORP ◽  
H. LEE MEADOW
Keyword(s):  
Conjoint Analysis ◽  
Preference Analysis

scholarly journals Preference analysis of determining jobs using conjoint analysis

2020 ◽  
Vol 1442 ◽  
pp. 012040
Author(s):  
R D Karima ◽  
R Setiadi ◽  
T Siswantining
Keyword(s):  
Conjoint Analysis ◽  
Preference Analysis

scholarly journals Mining Changes in User Expectation Over Time From Online Reviews

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Tianjun Hou ◽  
Bernard Yannou ◽  
Yann Leroy ◽  
Emilie Poirson
Keyword(s):  
Conjoint Analysis ◽  
Language Processing ◽  
Online Reviews ◽  
Kano Model ◽  
Improvement Strategies ◽  
Two Generations ◽  
Product Improvement ◽  
Set Up ◽  
Successive Generations ◽  
Flow Of Information

Customers post online reviews at any time. With the timestamp of online reviews, they can be regarded as a flow of information. With this characteristic, designers can capture the changes in customer feedback to help set up product improvement strategies. Here, we propose an approach for capturing changes in user expectation on product affordances based on the online reviews for two generations of products. First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text. Then, inspired by the Kano model which classifies preferences of product attributes in five categories, conjoint analysis is used to quantitatively categorize the structured affordances. Finally, changes in user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products. A case study based on the online reviews of Kindle e-readers downloaded from amazon.com shows that designers can use our proposed approach to evaluate their product improvement strategies for previous products and develop new product improvement strategies for future products.

Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement

1997 ◽  
Vol 34 (2) ◽  
pp. 286-291 ◽  
Author(s):  
V. Srinivasan ◽  
Chan Su Park
Keyword(s):  
Utility Function ◽  
Conjoint Analysis ◽  
Predictive Validity ◽  
The Self ◽  
Preference Structure ◽  
Combined Approach ◽  
Customer Preference ◽  
Full Profile ◽  
Structure Measurement

The authors introduce customized conjoint analysis, which combines self-explicated preference structure measurement with full-profile conjoint analysis. The more important attributes for each respondent are identified first using the self-explicated approach. Full-profile conjoint analysis customized to the respondent's most important attributes then is administered. The conjoint utility function on the limited set of attributes then is combined with the self-explicated utility function on the full set of attributes. Surprisingly, the authors find that the self-explicated approach by itself yields a slightly (but not statistically significantly) higher predictive validity than does the combined approach.

An Approach to Modeling Customer Preference Uncertainty by Applying Bootstrap to Choice-Based Conjoint Analysis Data

2010 ◽  
Author(s):  
Swithin S. Razu ◽  
Shun Takai
Keyword(s):  
Product Development ◽  
Conjoint Analysis ◽  
New Product Development ◽  
Market Share ◽  
New Product ◽  
Analysis Data ◽  
Product Development Process ◽  
Product Concept ◽  
Customer Preference ◽  
Market Shares

Analysis of customer preferences is among the most important tasks in a new product development. How customers come to appreciate and decide to purchase a new product affects the products market share and therefore its success or failure. Unfortunately, when designers select a product concept early in the product development process, customer preference response to the new product is unknown. Conjoint analysis is a statistical marketing tool that has been used to estimate market shares of new product concepts by analyzing data on the product ratings, rankings or concept choices of customers. This paper proposes an alternative to traditional conjoint analysis methods that provide point estimates of market shares. It proposes two approaches to model market share uncertainty; bootstrap and binomial inference applied to choice-based conjoint analysis data. The proposed approaches are demonstrated and compared using an illustrative example.

scholarly journals CLUSTER AND CONJOINT ANALYSIS FOR DETERMINING ONLINE SHOP SHOPEE CUSTOMERS PREFERENCE BASED ON E-SERVICE QUALITY

2021 ◽  
Vol 15 (2) ◽  
pp. 361-372
Author(s):  
Dimas Nurwinata Rinaldi ◽  
Fahriza Nurul Azizah ◽  
Candra Galang Gemilang Putra
Keyword(s):  
Cluster Analysis ◽  
Service Quality ◽  
Conjoint Analysis ◽  
Business Strategy ◽  
Quality Attributes ◽  
Sociodemographic Characteristics ◽  
Customer Preference ◽  
Gender Education ◽  
Education Profession ◽  
Cluster 2

The use of e-commerce as a means of shopping is a trend that is very much in demand by many Indonesians. This makes e-service quality very important in a transaction. Customer preference is one of the main factors in shaping a business strategy. This research discusses the use of cluster analysis to segment Shopee's e-commerce customers based on sociodemographic characteristics with k-means algorithm and conjoint analysis to determine which e-service quality attributes are most important to each cluster. The sociodemographic characteristics to be analyzed are gender, education, profession, e-commerce visit, income, and last purchase from the e-commerce. The result from k-means algorithm is there are 2 groupings of customers based on their sociodemographic characteristics, cluster 1 with the majority of women members with frequent visits, while for cluster 2 with the majority of male members with frequent visits. With the result of cluster analysis, conjoint analysis help this research to find which e-service quality attributes are most important. The results are members in cluster 1 prioritize Full payment payment methods when shopping online, while members in cluster 2 prioritize star seller types when shopping online. The aspect that doesn't matter most when shopping online is fulfillment in cluster 1 and security in cluster 2.

scholarly journals Integrating Kano Model, AHP, and QFD Methods for New Product Development Based on Text Mining, Intuitionistic Fuzzy Sets, and Customers Satisfaction

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ming Li ◽  
Jie Zhang
Keyword(s):  
Decision Making ◽  
Product Development ◽  
New Product Development ◽  
Quality Function Deployment ◽  
New Product ◽  
Kano Model ◽  
Analytic Hierarchy ◽  
Customer Preference ◽  
Intuitionistic Fuzzy ◽  
Hierarchy Process

Online reviews are crucial to any online business that wants to increase sales on the Internet. Customer reviews have information about product attributes, customer requirements (CRs), and shopping experience; mining reviews provide the direction of decision-making for new product development and design (NPDD). Besides, the information of customer preference has vagueness and uncertainty, and the accuracy of decision-making information directly affects the success of NPDD. This paper proposed a methodology that integrates the Kano model (KM), analytic hierarchy process (AHP), and quality function deployment (QFD) methods with intuitionistic fuzzy set (IFS) to solve decision-making problems in NPDD. By the new method, the web crawler technology was first applied to e-commerce web sites to collect raw data, and the representative CRs were extracted through combining LDA model with Apriori algorithm. Second, the intuitionistic fuzzy Kano model (IFKM) is proposed to evaluate adjustment coefficient of CRs and Kano categories via customer preference membership functions. Thirdly, overall weights which contained emotional needs (ENs) and functional needs (FNs) are obtained via intuitionistic fuzzy analytic hierarchy process (IFAHP); thus, the adjusted weights are calculated from IFKM and IFAHP. Next, the intuitionistic fuzzy quality function deployment (IFQFD) is proposed to acquire engineering characteristics (ECs) of weights through combining competition benchmarks and based on technical benchmarks to make goals for a company’s NPDD. Finally, the method was applied to study vertical-configured air conditioner (VAC) as an example. The results showed that the application of text mining and IFS to improve CS is both reliable and scientific.

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