consumer behavior in social shopping - empirical insights 2013
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Association Rules in Web Usage
Logfile Data – Empirical Insights into
the Use of User-Generated
Web Site Features
International Conference on Electronic Commerce 2013
Turku, Finland
Aug. 13, 2013
Dr. Christian Holsing and Dr. Carsten D. Schultz
Chair of Marketing, University of Hagen, Germany
Research supported by
SAS Institute Germany
Overview
2
1. Relevance and Basics of Business Model SSC
2. Literature Review
3. Research Question/Methodology
4. Empirical Results (Logfile Analysis)
5. Conclusion and Outlook
University of Hagen
3
Largest university in German-speaking countries
> 80,000 students
Distance Learning System
50 study centres in Germany, Austria, Switzerland, and Central and Eastern Europe
Faculties:
Cultural and Social Sciences
Mathematics and Computer Science
Business Administration and Economics
Law
www.fernuni-hagen.de/marketing
Relevance of Business Model SSC
4
Web 2.0 provides consumers with many methods of
creating and sharing user-generated content (UGC)
Social media are growing rapidly
Social Networking + Online-Shopping = Social Shopping
Social Shopping is about connecting consumers and
shopping together
Business model Social Shopping Community (SSC)
becomes more relevant
polyvore.com: more than 21 Mio. Unique Visitors/Month;
22 Mio. $ Venture Capital
SSC: Definition
5
OLBRICH/HOLSING 2011, p. 15:
A SSC is an online-shopping service that connects
consumers and lets them discover, share, recommend,
rate, and purchase products.
In contrast to traditional e-commerce channels, such as
online-shops, and shopbots, SSCs additionally offer user-
generated social-shopping features, as well as potential
interaction, so as to initiate or simplify purchase decisions.
SSC: Features
6
Product Detail-Site
at smatch.com:
SSC: Example of a Style on polyvore.com
7
Literature Review: Social Shopping
Research in Social Shopping is just at the beginning
Only few aspects are analyzed, e.g., impact of user-
generated content on economic outcomes
(GODES/MAYZLIN 2004; CHEVALIER/MAYZLIN 2006; LIU
2006; MOE/TRUSOV 2011)
Some recent studies are analyzing Social Shopping/
SSCs more detailed:
KANG/PARK 2009: Acceptance Factors of Social Shopping
SHEN/EDER 2011: An Examination of Factors Associated
with User Acceptance of Social Shopping Websites
8
Literature Review: Logfile Analysis/E-Commerce
9
Authors Country, Data Focus Sessions
BUCKLIN/SISMEIRO 2003 n. a., 10/1999 Car website 6,630 sessions
HUANG/LURIE/MITRA 2009 USA, 01 - 07/2004 comScore panel: websites in 6 product categories 210 sessions
JOHNSON/MOE/FADER/BELLMAN/LOHSE 2004 USA, 07/1997 - 06/1998 Media Metrix panel: 51 websites (books, CDs, flights) 33,452 unique visits
MOE 2003 n. a., 5/18 - 7/05/2000 Online shop for nutrition products 5,730 users; 7,143 sessions
MONTGOMERY/LI/SRINIVAN/LIECHTY 2004 USA, 4/01 - 4/30/2002 Media Metrix panel: barnesnoble.com, books.com,
bn.com
1,160 users; 1,659 sessions
PARK/CHUNG 2009 USA, 07 - 12/2004 comScore panel: travel websites (Expedia, etc.) Sessions of 1,190 panelists
PARK/FADER 2004 USA, 10/1997 - 05/1998 Media Metrix panel: online shops for books, and CDs 7,377 panelists; 18,027
sessions
VAN DEN POEL/BUCKINX 2005 n. a., 5/25 - 4/18/2002 Online shop for wine 1,382 visitors; 10,173
sessions
ZHANG/FANG/SHENG 2006 USA, 07 - 12/2002 comScore panel: 69 websites (CDs, computer hardware,
flight tickets)
104,416 sessions
This study Germany, Austria,
Switzerland, 5/01 -
10/31/2009
SSC focussing on fashion, living, and lifestyle 2.9 million sessions
Literature Review: Clickstream Studies
Clickstream data are a powerful source of information
Using clickstream data confronts researchers with a number of
difficulties, e.g.:
Capturing the purchasing environment of consumers
Associated data pre-processing
Accordingly, relatively few studies in fact use such data
PADMANABHAN/ZHENG/KIMBROUGH 2001; MOE/FADER 2004;
SISMEIRO/BUCKLIN 2004; VAN DEN POEL/BUCKINX 2005, PARK/CHUNG 2009
Research gap:
Analyzing consumer behavior in SSC‘s
Analyzing impact of more than just one kind of user-generated content,
e.g., ratings
Focus on categories of fashion, living, and lifestyle
10
Research Question/Methodology
Which shopping features, especially user-
generated features, of a SSC are used
together within user sessions?
Data: Web usage logfiles of a SSC
Method: Association Rule Learning
we will identify strong rules, and thus structural
relations between user-generated and direct
shopping features
using different measures of interestingness
11
Logfile Analysis: Data and Process
12
Logfiles of a high-traffic SSC
Categories of fashion, living, and lifestyle
> 600 participating online shops
Product data base > 1.5 million products
Period from May 1st, 2009 to October 31st, 2009
Number of sessions: 2.9 million
4 variable categories: general, direct shopping,
social shopping, and transactional
Software: SAS Enterprise Miner 6.2
Variables (4 Categories)
13
General Home (number of home page visits)
Product (number of product-detail sites visited)
Direct-Shopping Filter mechansims (brand, category, gender, price, sale, shop)
Search field
Social-Shopping (user-generated Web site features) List
Style
Profile
Tag
Transactional Click out (number of visits to participating online shops)
Descriptive Statistics
14
Variable Min Max Mean SD
General:
HOME 0 130 .09 .450
PRODUCT 0 664 .91 2.032
Direct-Shopping:
SEARCH_BRAND 0 369 .31 2.492
SEARCH_CAT 0 557 1.48 6.669
SEARCH_FIELD 0 520 1.15 2.548
SEARCH_GENDER 0 430 .73 4.016
SEARCH_PRICE 0 220 .12 1.693
SEARCH_SALES 0 234 .05 .960
SEARCH_SHOP 0 178 .12 .905
Social Shopping:
LIST 0 112 .02 .227
STYLE 0 95 .01 .164
PROFILE 0 72 .01 .148
TAG 0 183 .03 .565
Transactional:
CLICK_OUTS 0 471 .81 1.878
Method of Association Rules Learning
15
Set of user sessions S = {s1, s2, …, sn}
A user session is a sequence of interactions
I = {i1, i2, …, im}
Association rule is
an implication of A B
where A, B I and A B = Ø
{HOME, PRODUCT} {CLICK_OUT}
Measures of Association Rules
16
Significance measure
Quality measure
Interestingness measure
S
sBASsBA
})(|{)sup(
})(|{
})(|{)(
sASs
sBASsBAconf
)sup(
)()(
B
BAconfBAlift
Summary of Association Rules
17
Conclusion min.
support
min.
confident
max.
antecedents
number of
assoc. rules
CLICK_OUT .01 .05 3 32
PRODUCT .01 .05 3 34
LIST .007 .03 3 3
PROFILE .007 .03 3 3
STYLE .007 .03 3 4
TAG .01 .05 3 19
Results of Association Rules Learning (1)
18
Conclusion Antecedent No. sup conf lift
{CLICK_OUT} {HOME, PRODUCT} 84,103 .0289 .5812 1.41
{CLICK_OUT} {PRODUCT, SEARCH_GENDER} 115,478 .0397 .5423 1.32
{CLICK_OUT} {PRODUCT, SEARCH_GENDER,
SEARCH_CAT} 61,287 .0211 .5284 1.28
{PRODUCT} {HOME, CLICK_OUT} 59,140 .0200 .8407 1.94
{PRODUCT} {TAG} 31,722 .0110 .7654 1.77
{PRODUCT} {SEARCH_CAT,
SEARCH_GENDER, CLICK_OUT} 41,702 .0143 .7238 1.67
Results of Association Rules Learning (2)
19
Conclusion Antecedent No. sup conf lift
{LIST} {STYLE} 23,842 .0082 .0927 8.31
{LIST} {HOME, PRODUCT, SEARCH_FIELD} 24,530 .0084 .0456 4.09
{LIST} {HOME, PRODUCT, SEARCH_CAT} 26,548 .0091 .0326 2.92
{PROFILE} {STYLE} 23,842 .0082 .1088 28.01
{PROFILE} {LIST} 32,486 .0112 .0783 20.17
{PROFILE} {PRODUCT, LIST} 22,198 .0076 .0692 7.81
{STYLE} {PRODUCT, LIST} 22,198 .0076 .0711 8.69
{STYLE} {LIST} 32,486 .0112 .0680 8.31
{STYLE} {HOME, PRODUCT, SEARCH_FIELD} 24,530 .0084 .0419 5.11
{TAG} {PRODUCT, SEARCH_BRAND} 47,696 .0165 .3275 30.05
{TAG} {SEARCH_BRAND, SEARCH_CAT} 46,771 .0161 .2700 24.78
{TAG} {SEARCH_BRAND, SEARCH_FIELD,
CLICK_OUT} 29,553 .0102 .1709 15.68
Implic@tions
20
Association rules provide insights into structural relationships in user sessions
recommendations can be derived to improve the use and usability, e.g., linking certain shopping features
Identifying features that support main economic aim: click-out Social shopping features: no strong relationships with click-out
Potential strategy: adjust features, e.g., by integrating a direct click-out into styles and lists, instead of having product-detail sites as an intermediate step
Social shopping features: highly associated to each other Way of increasing click-outs: loosen the linkage between these features
However, one important user motive may be to browse and participate in the community manage specific user groups
Implic@tions
21
Provide different features to various user types e.g., to community-orientated users, browers, buyers, etc.
specific cluster analysis or self-organizing maps (SOM)
Split testing could evaluate such a solution before implementation
Provide sales promotions within lists, profiles, and styles increase click-out rate
Search results may also include direct links to online shops e.g., by miniature previews, in addition to product-detail sites
Management needs to monitor consumer confusion or reactance
Overall, association rules provide evidence enabling the management to reduce user navigation and search effort increase usability
Limitations and Future Research
22
Future research should confirm results and extend the focus to other features and to different types of online services
As user-generated features continue to evolve dynamically, more recent data can incorporate the latest developments
Method of Association Rules Learning
does not consider the order of interactions within a session
Rules simply consider request for an interaction, not frequency
good starting point to identify interesting relations
further inspection: order (clickstream) and frequency of interactions
Distinguish between different user groups to analyze potential differences between these segments
Conclusion and Outlook!
23
We enhance the research in Social Shopping
It seems likely that Social Shopping will become
more and more important
Use of social media increases
New business models arise, e.g., Pinterest (online
pinboard)
New technologies will be established rapidly (mobile,
tablets, etc.)
Booz&Co forecast: social commerce revenues will hit
$30bn by 2015
Thank You
For Your Attention!
Dr. Christian Holsing and Dr. Carsten D. Schultz
Contact:
Dr. Christian Holsing: http://social-commerce.net, www.lynx-ecommerce.de
Dr. Carsten Schultz: www.fernuni-hagen.de/marketing
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