語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Spatial-temporal data analytics and ...
~
Yan, Ping.
FindBook
Google Book
Amazon
博客來
Spatial-temporal data analytics and consumer shopping behavior modeling.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Spatial-temporal data analytics and consumer shopping behavior modeling./
作者:
Yan, Ping.
面頁冊數:
151 p.
附註:
Source: Dissertation Abstracts International, Volume: 72-03, Section: A, page: 1009.
Contained By:
Dissertation Abstracts International72-03A.
標題:
Business Administration, Marketing. -
電子資源:
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.
LDR
:03677nam 2200349 4500
001
1405351
005
20111205110038.5
008
130515s2010 ||||||||||||||||| ||eng d
020
$a
9781124431017
035
$a
(UMI)AAI3438790
035
$a
AAI3438790
040
$a
UMI
$c
UMI
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
690
$a
0338
690
$a
0454
690
$a
0489
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
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9168490
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入