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Spatial-temporal data analytics and ...

Yan, Ping.

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Spatial-temporal data analytics and consumer shopping behavior modeling.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Spatial-temporal data analytics and consumer shopping behavior modeling./
Author:	Yan, Ping.
Description:	151 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-03, Section: A, page: 1009.
Contained By:	Dissertation Abstracts International72-03A.
Subject:	Business Administration, Marketing. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3438790
ISBN:	9781124431017

Spatial-temporal data analytics and consumer shopping behavior modeling.
Yan, Ping.

Spatial-temporal data analytics and consumer shopping behavior modeling. - 151 p.

Source: Dissertation Abstracts International, Volume: 72-03, Section: A, page: 1009.

Thesis (Ph.D.)--The University of Arizona, 2010.

RFID technologies are being recently adopted in the retail space tracking consumer in-store movements. The RFID-collected data are location sensitive and constantly updated as a consumer moves inside a store. By capturing the entire shopping process including the movement path rather than analyzing merely the shopping basket at check-out, the RFID-collected data provide unique and exciting opportunities to study consumer purchase behavior and thus lead to actionable marketing applications.

ISBN: 9781124431017Subjects--Topical Terms:

1017573
Business Administration, Marketing.

Spatial-temporal data analytics and consumer shopping behavior modeling.
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100 1 $a Yan, Ping. $3 1032387
245 1 0 $a Spatial-temporal data analytics and consumer shopping behavior modeling.
300 $a 151 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-03, Section: A, page: 1009.
500 $a Adviser: Daniel Zeng.
502 $a Thesis (Ph.D.)--The University of Arizona, 2010.
520 $a RFID technologies are being recently adopted in the retail space tracking consumer in-store movements. The RFID-collected data are location sensitive and constantly updated as a consumer moves inside a store. By capturing the entire shopping process including the movement path rather than analyzing merely the shopping basket at check-out, the RFID-collected data provide unique and exciting opportunities to study consumer purchase behavior and thus lead to actionable marketing applications.
520 $a This dissertation research focuses on (a) advancing the representation and management of the RFID-collected shopping path data; (b) analyzing, modeling and predicting customer shopping activities with a spatial pattern discovery approach and a dynamic probabilistic modeling based methodology to enable advanced spatial business intelligence. The spatial pattern discovery approach identifies similar consumers based on a similarity metric between consumer shopping paths. The direct applications of this approach include a novel consumer segmentation methodology and an in-store real-time product recommendation algorithm. A hierarchical decision-theoretic model based on dynamic Bayesian networks (DBN) is developed to model consumer in-store shopping activities. This model can be used to predict a shopper's purchase goal in real time, infer her shopping actions, and estimate the exact product she is viewing at a time. We develop an approximate inference algorithm based on particle filters and a learning procedure based on the Expectation-Maximization (EM) algorithm to perform filtering and prediction for the network model. The developed models are tested on a real RFID-collected shopping trip dataset with promising results in terms of prediction accuracies of consumer purchase interests.
520 $a This dissertation contributes to the marketing and information systems literature in several areas. First, it provides empirical insights about the correlation between spatial movement patterns and consumer purchase interests. Such correlation is demonstrated with in-store shopping data, but can be generalized to other marketing contexts such as store visit decisions by consumers and location and category management decisions by a retailer. Second, our study shows the possibility of utilizing consumer in-store movement to predict consumer purchase. The predictive models we developed have the potential to become the base of an intelligent shopping environment where store managers customize marketing efforts to provide location-aware recommendations to consumers as they travel through the store.
590 $a School code: 0009.
650 4 $a Business Administration, Marketing. $3 1017573
650 4 $a Business Administration, Management. $3 626628
650 4 $a Information Technology. $3 1030799
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690 $a 0454
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710 2 $a The University of Arizona. $b Management Information Systems. $3 1026782
773 0 $t Dissertation Abstracts International $g 72-03A.
790 1 0 $a Zeng, Daniel, $e advisor
790 1 0 $a Ram, Sudha $e committee member
790 1 0 $a Liu, Yong $e committee member
790 1 0 $a Szidarovszky, Ferenc $e committee member
790 $a 0009
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3438790