Konsumentenverhalten im Social Shopping - empirische Analyse mittels Logfiles. Methode: Assoziationsregeln.

1. 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

2. 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

3. 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

4. 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

5. 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.

6. SSC: Features
6
Product Detail-Site
at smatch.com:

7. SSC: Example of a Style on polyvore.com
7

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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)

14. 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

15. 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}

16. Measures of Association Rules
16
 Significance measure
 Quality measure
 Interestingness measure
S
sBASs
BA
})(|{
)sup(


})(|{
})(|{
)(
sASs
sBASs
BAconf



)sup(
)(
)(
B
BAconf
BAlift



17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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