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Three essays on big data consumer an...

Lee, Dokyun.

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Three essays on big data consumer analytics in e-commerce.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Three essays on big data consumer analytics in e-commerce./
Author:	Lee, Dokyun.
Description:	147 p.
Notes:	Source: Dissertation Abstracts International, Volume: 77-01(E), Section: A.
Contained By:	Dissertation Abstracts International77-01A(E).
Subject:	Commerce-Business. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3721617
ISBN:	9781339032627

Three essays on big data consumer analytics in e-commerce.
Lee, Dokyun.

Three essays on big data consumer analytics in e-commerce. - 147 p.

Source: Dissertation Abstracts International, Volume: 77-01(E), Section: A.

Thesis (Ph.D.)--University of Pennsylvania, 2015.

This item is not available from ProQuest Dissertations & Theses.

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.

ISBN: 9781339032627Subjects--Topical Terms:

3168423
Commerce-Business.

Three essays on big data consumer analytics in e-commerce.
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245 1 0 $a Three essays on big data consumer analytics in e-commerce.
300 $a 147 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-01(E), Section: A.
500 $a Adviser: Kartik Hosanagar.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2015.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a 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.
590 $a School code: 0175.
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650 4 $a Marketing. $3 536353
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710 2 $a University of Pennsylvania. $b Operations and Information Management. $3 2101627
773 0 $t Dissertation Abstracts International $g 77-01A(E).
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792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3721617