Does WEB Log Data Reveal Consumer Behavior?

1. Does WEB Log Data Reveal Consumer Behavior? Faculty of Commerce, Kansai University Daigo Naito , Kohei Yamamoto , Katsutoshi Yada , Naohiro Matsumura , Kosuke Ohno and Hiroshi Tamura

2. The purpose of this research • Combining various data mining technology ,and discovering the new knowledge from Web log data • from the knowledge that has been gained, planning useful sales strategies for future use

3. Background • Competition between the various shops doing business on the Internet is becoming more severe -It is necessary to plan the effective sales strategies • Each customers have different purposes and actions -planning strategies for every different purposes and action • The Web log data that has been accumulated on the servers -Data mining

4. Explanation of the data

5. Detail of Web log data • Definition • Cart – It means “kosik”, and it is also defined as purchase. • Session – a session is used as a unit of study of a customer. • PATH – it is a procedure of following a route of a click made within each site during a given session.

6. The data that was subjected to analysis • Remove the PATH data including only a limited numbers and very large numbers of clicks. A ratio of number of clicks per a session ‐A single and 2-4 clicks data does not constitute enough information for analysis of consumer behavior. ‐Session data that included 100 or more clicks comprised less than 1% of total sessions and thus, it can be surmised that their overall importance is not greater. clicks • The session data included over 5 clicks and under 100 clicks (4,220 visitors who made purchase and 140,327 persons who did not) will be used for the analysis.

7. Basic analysis

8. PATH to the purchase # of clicks to the purchase • PATH to the purchase ‐The number of customers who reached a purchase in 7 clicks is the largest. In addition, such a customers visit a site and purchase it during 2-6 minutes. # of customers # of clicks Distribution of length of time spent to the purchase # of customers Staying time for session (sec)

9. The customer behavior at every each shop site 25～ 20-24 15-19 10-14 5-9 0-4 Differences in Average clicks per session by each shop • Differences of customers action ‐The upper figure shows that differences of average clicks between every each shop. ‐The lower figure indicates that there are the customers who buy some product categories with a low number of clicks. But the purchasing visitors of other product categories use a high number of clicks. ‐It is depending on the shop and product category, customer behavior tends to vary. # of clicks Differences in number of clicks by product category # of clicks

10. Strategy for the each shops

11. strategic suggestion for the Characteristics of each shops Shop1 camera Shop2 audio Shop3 TV Shop4 electrical appliances Shop5 MP3 Shop6 mobile Shop7 PC • It would be divided into 3 groups by purchasing possibility Positioning of the shop sites shop５（MP3） • Because purchase probability is high, It is surmised that the visitors of shop5 have already decided what they intend to purchase before they visit the shop. • The strategy that the shop use banners advertising to actively induce visitors to come to the site can be effective. Purchasing possibility # of customers shop3（TV）,shop6（mobile） • Because purchase probability is low, it is thought that the visitors of these shops make their purchase at regular shops. • The strategy that involves a joint effort of the “Click and Mortar” strategy type (real shop and Net mall shop cooperation) can be recommended.

12. Purchasing possibility # of customers Shop1 camera Shop2 audio Shop3 TV Shop4 electrical appliances Shop5 MP3 Shop6 mobile Shop7 PC Defining the target shop Positioning of the shop sites shop1・2・4・7 • The purchasing possibility level is about in the middle. • It is possible to raise sales of shop by giving purchasing possibility. • It is necessary to analysis of customer level. • The number of Shop4’s sessions is large. • We will focus on Shop 4.

13. Extraction the rule of target customers

14. Defining the analysis of the objective variables • the customers that were wondering which product to buy.（ the customers who go to the cart after 12 clicks or more） we extracted the characteristics from among the customers who were wavering concerning whether to purchase a product or not ‐Target Data The visitors of Shop4, among the visitors to the site that used 12 clicks or more and also read the page of refrigerators-freezers and also read the page of refrigerators-freezers ‐the analysis of the objective variables ‐purchase a product（166sessions） or did not（346sessions） E-BONSAI Ｗｅ extract a rule every customer group.

15. E-BONSAI E-BONSAI was originally developed to analyze DNA code. Since then, E-BONSAI has been improved and by expressing consumer behavior patterns as character strings, it can beused for extracting patterns from time-series category patterns as a data mining tool. DNA T CANCER A C A GAGGCACAGA … B GAGTGACAGA … C GAGTGACAGA … G

16. the click PATH data convert into character strings A flow of time ct ls dt / ct popup Customer A Mapping Table Each page is made character string. As follow as Mapping Table 2 4 5 4 5 1 Customer A • Characters from the internal site pages can be converted into different • characters and the click PATH data (the data they referred to) for all • visitors that were part of the project can be converted into character • strings.

17. The result of E-BONSAI 1*7*7* Searching by functional specification as popup and findp were used Mapping Table Yes No purchase! 1*5*5*5 *5*7* ls (product list) or dt(product explanation) were seen (hit/sup)=(300/400) Yes purchase! (hit/sup)=(28/42) There are the characteristic such as 2 mentioned above was seen throughout. →The factor that we paid special attention was the multiple searches they made by keying in terms concerning the functions specifications.

18. Implications for Business Why do they repeat searching by keying in terms concerning the functions specifications? There are two possible reasons for this behavior. • The page design was bad and it is difficult to use the searching function ⇒There may be a need to improve the design of the search function page. • The visitor can’t decide that which product matches to them ⇒They need the choice standard for purchase. Because they don’t know what they really want to purchase.

19. Implications for Business Information from a maker An instruction from a site We suggest that to add a word of mouth reporting function to a site With word of mouth information Evaluationfrom the user who really bought the product

20. A Japanese word of mouth bulletin board site http://www.kakaku.com/ 1 A figure of Point count of word of mouth information 2 A text search of word of mouthinformation 3 A word of mouth bulletin board

21. Count and comparison of word of mouth information • Word of mouth‐Product comparison by count information‐Ｅａｓｙ　ｔｏ　 understand!! The graph of product evaluation by the existing user

22. Implications for Business Point count of word of mouth information A text search of word of mouth information An at a loss customer Decision-making support by word of mouth information Ｘ Ｘ purchase！