1) The document proposes a new method to analyze relationships between substitute products using user browsing and purchase behavior data from e-commerce sites. It examines which products are superior to others in attractiveness. 2) The method was tested on wedding venue data from a Japanese wedding planning site. It accurately identified competitive and win-lose relationships between venues based on correlations with user survey data. 3) The method also extracts keywords explaining why one product is superior by analyzing reviews from users who chose that product over others. This provided more accurate superiority factors than a simple baseline method.

1. Inferring Win–Lose Product Network
from User Behavior
Shuhei Iitsuka†
, Kazuya Kawakami‡
, Seigen Hagiwara*
,
Takayoshi Kawakami**
, Takayuki Hamada***
, Yutaka Matsuo†
1
† The University of Tokyo, Japan
‡ University of Oxford, UK
* Recruit Marketing Partners Co., Ltd., Japan
** Industrial Growth Platform, Inc., Japan
*** IGPI Business Analytics & Intelligence, Inc., Japan

2. Background
● E-commerce is expanding, and various data
mining methods have been proposed.
● However, few data mining techniques have been
proposed to provide:
○ superiority relations of product
attractiveness.
○ why that superiority is formed.
→ Understanding competitive advantages is
important for product marketers.
INTRODUCTION
2
Data mining is playing an important
role in e-commerce marketing.
https://www.amazon.com/dp/B005EOWBHC/

3. Objective
● We propose a new method to examine
win–lose relation.
○ Superiority relation among substitute
products in terms of attractiveness.
● We also propose a review mining method to
extract why that superiority is formed.
● Our method uses the difference between users’
browsing and purchasing behavior.
3
INTRODUCTION
browse browse
purchasepurchase
SUPERIORITY SUPERIORITY
・・・
USER #1 USER #N
stylish,
modern
compact,
light
win–lose network

4. Product Relation
Substitute or Complementary
● Common means of perceiving product relations
in consumer theory.
● Have been used in e-commerce mining to
understand product relations.
Network Analysis
● Network analysis methods have been imported
to e-commerce marketing.
→ Few studies have examined directed relation of
products.
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RELATED WORKS
SUBSTITUTE COMPLEMENTARY

5. Win–Lose Product Relation
● Competitive relation
Item A and B are browsed by the same user.
= Item A and B are in competition. (A ↔ B)
● Win–lose relation
Item A is purchased after item A and B are browsed.
= Item A is superior to item B. (A ← B)
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PROPOSED METHOD
User Browsed (Purchased)
1 B, C
2 A, B, C
3 B, C
Competitive network Win–lose network
Access Log

6. Superiority Factor Analysis
● Examines why the superiority is formed in a form of keywords (superiority factors).
● Item A’s superiority factors to item B comes from the reviews of products purchased by
patrons who prefer item A to B.
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PROPOSED METHOD
Users who
supports
A > B
superiority

7. Zexy http://zexy.net/
● Japanese largest wedding portal website.
● Browse = browse a venue page
Purchase = reserve a venue tour
● Used log data
○ Jan 1, 2012 — Oct 31, 2012
○ User ID, URL, Flag for tour reservation
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ANALYSIS RESULTS
Tour reservation made!
BROWSE
PURCHASE
VENUE PAGE
LIST PAGE

8. Product Network
Competitive network of wedding venues in Japan
The color segments match well with the competition
cluster. → Competition takes place per region.
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ANALYSIS RESULTS
Win–lose network of selected venues in Tokyo
Directed relation of attractiveness is shown.
→ E-commerce owners can expect users’ tendency
to make conversion actions.

9. Superiority Factor Analysis
9
ANALYSIS RESULTS
● ceremony
● garden
● banquet
● solemnity
● photograph
● Japanese dish
● Japanese-style room
● ceremony
● garden
● bus
Venue H
Venue J Venue A

10. Experimental Setup
● Evaluate how much our method can estimate
the actual product relations.
● Conducted a user survey of couples to observe
actual user perceptions.
○ Jan 23, 2012 — Dec 14, 2013
○ Couples who used Zexy for tour reservation
and held a ceremony
○ Selected 10 venues in Tokyo
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EVALUATION EXPERIMENT
Log data
User survey
USER (N=202)
PRODUCT
USER (N=173)
PRODUCT
BROWSE A VENUE
RESERVE A TOUR
ATTEND A TOUR
HOLD A CEREMONY

11. Results
Experiment #1: Correlation of network weights
● Correlation between the weights of the product
network: user survey VS log data.
● Significant correlation was found between them
for both of competition and win–lose network.
→ Log data can be a good alternative of the user
survey.
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EVALUATION EXPERIMENT
Correlation of competitive relation
0.685 (p < 0.01)
Correlation of win–lose relation
0.648 (p < 0.01)

12. Results
Experiment #2: Superiority factor analysis
● Actual factor: Responses to the question
“Reason for selection”.
● Baseline method: Regards the winner product’s
review as the superiority factors.
● Across all venues, proposed method estimated
more actual factor words significantly (p < 0.05).
● Proposed method shows interesting findings
while Baseline method only shows general and
well-known property of the product.
EVALUATION EXPERIMENT
ATTENDED
A TOUR
HELD A
CEREMONY
Reason for selection Actual factor words
Method Factor Words of product D against G
Proposed
(13/20)
chapel, hospitality, ceremony, guest,
feeling, day, impression, staff,
stained glass, banquet, dish,
atmosphere, church, location, San,
photograph, lovely, venue,
Omotesando, weddings
Baseline
(3/20)
map, cathedral, forbidden, she, Akka,
order, impression, problem, standard,
cloud, stained glass, church,
European, minute, overall, exchange,
movie, ring, Omotesando, bringing 12

13. Discussion & Conclusion
● Our proposed method is useful to estimate the superiority relation of products and why that
superiority is formed.
● Our text mining method does not consider polarity of the sentence.
→ Our method captures aspects which users care.
● Analysis needs to be done in the same category of products and only between substitutes.
Contribution
● Proposed a new data mining method to analyze superiority product relation.
● Proposed a text mining method to analyze superiority factors.
→ E-commerce owners can plan effective marketing or promotion strategies.
● Evaluated if log data can be a good alternative of user survey.
→ Huge costs (distribution, data input etc.) can be saved.
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14. Thank you for listening.
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https://tushuhei.com
iitsuka@weblab.t.u-tokyo.ac.jp