Proceedings of the 15th annual conference on Computers and industrial engineering
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Confirmation-guided discovery of first-order rules with tertius
Machine Learning
Finding Association Rules That Trade Support Optimally against Confidence
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The exploration of customer satisfaction model from a comprehensive perspective
Expert Systems with Applications: An International Journal
Using Online Conversations to Study Word-of-Mouth Communication
Marketing Science
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Intelligent service quality management system based on analysis and forecast of VOC
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A conceptual model for proactive-interactive customer complaint management systems
International Journal of Business Information Systems
Hi-index | 12.05 |
Voice of the customer (VOC) is a critical analysis procedure that provides precise information regarding customer input requirements for a product/service output. The ability to conduct a voice of the customer analysis, which could be gained through direct and indirect questioning, will enable engineers and other decision makers to successfully understand customer needs, wants, perceptions, and preferences. The information obtained from the customers is then translated into critical targets that will be used to ultimately satisfy the customer requirements. During this research project, different forms of customer input, including qualitative and quantitative data, were transformed to a common data format to develop a correlation between design input requirements and product/service outputs. We have developed a new method for measuring customer satisfaction ratio (CSR) by considering the following: mining both textual and quantitative data, multiple design parameters, mapping output on a scale of 0-1, and a decision template for means of measure. Previous measures of CSR fail to incorporate the cost implication of fixing customer complaints/issues; however, we include this important and unique measure in our research. The implication of this research will reduce Things Gone Wrong (TGW's) and engineering development time and will achieve improvements in JD Power ratings, quality perception, marketing tools, and customer satisfaction.