Location: Capital Tower, Tanjong Pagar
Date: 9 July 2019
Time: 12:00pm – 2:00pm
Institution: Singapore University of Social Sciences (SUSS)

WHAT IS BROWN BAG MASTERCLASS SERIES?

The Brown Bag Series is a lunch and learn series by HeadHunt, which aims to inspire working professionals to develop a passion for lifelong learning.

In this Brown Bag @ Tanjong Pagar Series, Singapore University of Social Sciences (SUSS) kickstarted the 4-part lunch and learn event on 9th July 2019, Tuesday, where Professor David Lee shared on the topic of Technology Transformation: What Are You Missing Out?, followed by Understanding Customer Product Reviews and Ratings: A Case at Amazon by Dr. Jiang Zhiying.

Session 1Technology Transformation: What Are You Missing Out? – Professor David Lee – School of Business, SUSS

Brown Bag Masterclass – 9th July
Technology Transformation: What Are You Missing Out?

Speaker:
Professor David Lee
School of Business, SUSS

Synopsis:
Prof Lee will give an overview of the change in global business environment and corporate strategy driven by technology changes. In particular, the talk will focus on the global changes brought about by the accelerated adoption and commercialization of new tech LASIC business models in China. Business education in universities will also now be focusing in ABCDE so as to prepare the students to harness technology for sustainable business. Not familiar with acronyms such as LASIC and ABCDE? Listen to this talk to find out more on how your company and your future will be affected.


Session 2 – Understanding Customer Product Reviews and Ratings: A Case at Amazon – Dr. Jiang Zhiying – School of Business, SUSS

Brown Bag Masterclass – 9th July
Understanding Customer Product Reviews and Ratings: A Case at Amazon

Speaker:
Dr. Jiang Zhiying
Head (Graduate Certificate & Diploma in Digital Marketing Programmes)
School of Business, SUSS

Synopsis:
Brands need to leverage the enormous volumes of feedback that consumers leave on social media. Existing methods for understanding free-text based consumer feedback data (e.g. online reviews) are predominantly qualitative (e.g. sentiment analysis). Qualitative approaches, however, cannot provide quantitative predictions of a potential rating increase following a product improvement. This talk will discuss a novel method that converts reviews and ratings into statistical data that can be used to forecast rating performance. This is achieved by assigning quantitative values of importance to the various features of a given product based on each feature’s percentage contribution to the product rating. With such information, marketing and innovation teams can optimise their investment decisions to address consumer needs accurately and therefore maximise return on investment.

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