Hsiu-Yuan Tsao is a Professor of Marketing at National Chung Hsing University in Taiwan (2013-Now). He holds a Master’s in Computer Science from the University of Southern California (1995, GPA: 3.79) and a PhD in Marketing from Curtin University in Western Australia (2003). Before pursuing his PhD in Marketing, he was an IT specialist at IBM Taiwan (1996-1999).
He is a visiting scholar at the University of Sydney Business School where he collaborates with the Consumer Insights Research Group in Marketing (2017).
The author of more than 20 articles published in peer-reviewed journals over the past decade, his work has been accepted for publication by such journals as Industrial Marketing Management, Journal of Service Management, Marketing Letters (lead article), European Journal of Marketing , Business Horizons, International Journal of Market Research, Online Information Review, Journal of the Consumer Behaviour (lead article), Journal of Marketing Analytics(lead article), Omega, Journal of The Operations Research Society, Journal of Brand Management, Asia Pacific Journal of Marketing and Logistics, International Journal of Information and Management Sciences, Journal of Information Technology Research, and Journal of Information Science and Technology.
One of his representative works entitled “Budget Allocation for Customer Acquisition and Retention While Balancing Market Share Growth and Customer Equity” and published by Marketing Letters received the following commentary: ”This paper adds another interesting aspect to the growing literature on CLV and CE, which has branched into many directions over the last decade. It presents a bit of a different angle that is worth noting.” Additionally, a paper entitled “Moderating Effects of the Brand Concept on the Relationship Between Brand Personality and Perceived Quality” and published by Journal of Brand Management is a highly cited paper.
Dr. Tsao has won many awards for best papers as presented at International Conferences, including the Annual Meeting of the Association of Management/International Association of Management, Australia and New Zealand Academy of Marketing, and the Chinese Commerce and Technology Management where he was awarded for the most distinguished paper for advising a Master’s thesis entitled the “Perceptual Map of Brand Concept and Brand Personality Using Text Mining” and “The influence of Brand Personality on the success of Crowdfunding --Application of Logistic Regression and Neural Networks”.
Dr. Tsao has core knowledge and key technology in marketing and computer science. Indeed, he combined the disciplines of computer science and marketing to implement the Automated Textual Data Analytics System (testing site for English version please refer to https://www2.sidrlab.net/upload_jeiba_b5E.php), Measuring Survey Scale via Text Mining Online (MSSTMO) and develop a very innovated and unique methodology that demonstrated the potential of this approach to complement more traditional survey approaches used for assessing marketingconstructs via text mining, machine learning, and sentiment analysis. Based on this methodology,a paper was recently published in the Journal Service, Management, Online Information Review in 2018. Another paper entitled,” A Machine-Learning Based Approach to Measuring Constructs Through Text Analysis “, had been published European Journal of Marketing. This paper particularly developed a novel and generalizable machine-learning- based method for measuring established marketing constructs through the passive analysis of consumer-generated textual data. Further, the newly published paper entitled,” From mining to meaning: How B2B marketers can leverage text to inform strategy “, had been published Industrial Marketing Management. This paper particularly further developed a novel and generalizable machine-learning- based method for measuring established marketing constructs through the passive analysis of consumer-generated textual data.
He tested the method on two brands, Starbucks and McDonald’s, using both brand experience and brand personality constructs. Through a training dataset and a publicly available thesaurus of semantically linked words, the method first developed a dictionary of words related to the specific dimensions of the construct. Once developed, this dictionary can assess consumer-generated textual data from any source, such as publicly posted consumer comments or reviews, for their most specific meaning. Whenever calculating the results for the specific dimensions of a construct, the method explicitly recognizes both specific words and the strength of their underlying sentiments. The results calculated using this new approach are statistically equivalent to the responses for more traditional marketing scale item boxs. This result demonstrates the validity of this methodology and shows its valuable potential for complementing traditional survey approaches for assessing marketing constructs.
Furthermore, our research attempt to persuade marketers to assess what, if any, hindrances crucial issues present whenever they consider adopting textual data to solve marketing problems. Based on the IPA (Importance Performance Analysis), we adopt textual measures, i.e., Attention (Term Frequency) and Affection (Score of Sentiment) to generate an IPA grid and explicitly reveal the options for actionable strategies marketers have, keeping in mind the various consumer insights into those measures. Finally, we present related cases of Shopify and Salesforce reviews to demonstrate how to employ textual data for marketing action.
Technical and Professional Expertise
First are my programming skills, for even as I developed my second domain knowledge of Marketing, programming skills have remained a strong personal interest. I kept up with the updated and popular R and Python languages to develop the MSSTMO system. I also have experience with COBOL, C, Visual Basic, ASP, PHP, and currently use both R and Python.
Second, is database management. I have experience using the Middle Computer IBM AS/400 DB2, Microsoft SQL Server, and the currently popular MySQL as well as MongoDB as a non-structural database.
Third, I am proficiy in R language with a prominent Machine Learning Package of CARET to implement a deep learning function of MLP (Multilayer Perceptron) to examine the determinant factor that affects the success of crowd sourcing projects, and a unique Textual Data Analytics Package of tidyverse to implement an innovated automatically textual data analytics system based on Importance-Performance Analysis (IPA). I proactive to adopt the Frequency and Sentiment of textual data from social media to replace the numerical data of the Importance and Performance obtained from questionnaire.
Fourth is key domain knowledge of Marketing. Since 2001, I have developed second domain knowledge in Marketing, and obtained my PhD in Marketing in Australia. The majority of the papers I have published are on Marketing, Operational Research, and Information System include publication in the journal Marketing Letters, one of the top journals in Marketing. Thus, I fully understand the process involved in publishing academic papers successfully.
Fifth is data analytics. Without a doubt, the training in the discipline of marking traditional statistics, such as Regression, ANOVA, Factor Analysis, even Multidimensional Scaling has given me both basic skills and considerable knowledge. In addition, due to the proficiency in usage and knowledge of information technology, I developed new skills that originated in the discipline of Data Mining. Therefore, the association rule, decision tree, even text mining and sentiment analysis are all heavily used current techniques of mine.
After accruing nearly twenty years of experience in both industry and academics and combining the two disciplines of computer science and marketing, I’m confident of my experience and my ability to contribute to and undergo a prominent co-research based on my skill and knowledge of Data Scientist specialized in Textual Data Analytics and Machine Learning.