Date of Award
Doctor of Philosophy (PhD)
Operations & Information Management
Consumers are increasingly spending more time and money online. Business
to consumer e-commerce is growing on average of 20 percent each year and
has reached 1.5 trillion dollars globally in 2014. Given the scale and growth
of consumer online purchase and usage data, firms' ability to understand
and utilize this data is becoming an essential competitive strategy.
But, large-scale data analytics in e-commerce is still at its nascent stage and there
is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure
(from unstructured data such as text, photo, and video) large-scale data
and econometric analyses to truly understand and assign causality to interesting
patterns. In my dissertation, I study how firms can better utilize big data
analytics and specific applications of machine learning techniques for improved
e-commerce using theory-driven econometrical and experimental studies. I
show that e-commerce managers can now formulate data-driven strategies for
many aspect of business including cross-selling via recommenders on sales
sites to increasing brand awareness and leads via social media content-engineered-marketing.
These results are readily actionable with far-reaching economical consequences.
Lee, Dokyun, "Three Essays on Big Data Consumer Analytics in E-Commerce" (2015). Publicly Accessible Penn Dissertations. 1830.