1. Data mining for shopping centres - customer knowledge-management framework 授課教師 : 許素華博士 學生 : S92660005 黃永智 S92660014 呂曉康 S92660017 李峻賢 日期 : 2004/03/29 Dennis, C., Marsland, D., Cockett, T.(2001) Journal of Knowledge Management.. Vol. 5, Iss. 4; pp. 368-374

2. 1 Agenda Introduction Exploratory Study Results Models of Relative Spend Discussion and Conclusion About K-Means

3. 2 Introduction(1/2) Knowledge is a fundamental factor behind an enterprise's success Management using knowledge-based computer systems and networks Management using knowledge-based computer systems and networks Management of intellectual (human) capital Management of intellectual (human) capital All knowledge activities affecting success All knowledge activities affecting success Richards et al. (1998) argue that success is founded on "a continuous dialogue with users, leading to a real understanding". For retailers the key... is to establish data warehouses to improve and manage customer relationships (Teresko, 1999)

4. 3 Introduction(2/2) Incorporating data mining and customer database aspects within a framework of knowledge management can help increase knowledge value. Sharing information Loyalty schemes The objective of retail data mining schemes has been to identify subgroups Shopping and Service motivations Shopping and Service motivations

5. 4 Exploratory Study The results are from a survey of 287 respondents at six shopping centres Determine which specific attributes of shopping centres were most associated with spend for subgroups of shoppers Convenience sample –(weekdays, 10.30am to 3.30pm ) Unstructured Interviews Unstructured Interviews Least squares regression

6. Results

7. 6 Conventional demographics(1/6) Females vs males The significant attributes for females were grouped around two factors: The significant attributes for females were grouped around two factors: Shopping: "selection of merchandise" Shopping: "selection of merchandise" Experience: "friendly atmosphere" Experience: "friendly atmosphere"

8. 7 Conventional demographics(2/6) Upper vs Lower socio-economic groups ABC1 (managerial, administrative, professional, supervisory and clerical) ABC1 (managerial, administrative, professional, supervisory and clerical) C2DE (manual workers and pensioners) C2DE (manual workers and pensioners)

9. 8 Conventional demographics(3/6) Higher vs Lower income groups

10. 9 Conventional demographics(4/6) Older vs Younger shoppers

11. 10 Conventional demographics(5/6) Shoppers travelling by car vs Public transport

12. 11 Conventional demographics(6/6) Service importance vs Shops importance

13. 12 Cluster analysis Shoppers motivated by the "importance" of "shops" vs "service“ A cluster analysis (SPSS K-means) based on "importance" scores has identified distinct subgroups sharing particular needs or wants. A cluster analysis (SPSS K-means) based on "importance" scores has identified distinct subgroups sharing particular needs or wants.

14. 13 Compare Service & Shops Groups “Service" shoppers were in a slightly higher characters then “shops” shoppers gropus Socio-economic group (63 % ABC1s vs. 59 %) Socio-economic group (63 % ABC1s vs. 59 %) Income (60 % ￡ 20,000 per year + vs. 53 %) Income (60 % ￡ 20,000 per year + vs. 53 %) Age (42 percent 45 + vs. 33 percent) Age (42 percent 45 + vs. 33 percent) Traveled by car (90 percent vs. 52 percent) Traveled by car (90 percent vs. 52 percent)

15. 14 Models of Relative Spend for "shops": 11 Spend = 19.4 + 0.70 X Attractiveness -0.21 X Distance.

16. 15 Relationship Between Attractiveness & Sales Turnover

17. 16 Discussion and Conclusion Target high-spending "service" shoppers. Increase their spend by 10%, equivalent to over a 3% rise in total sales. Increase their spend by 10%, equivalent to over a 3% rise in total sales. Local loyalty cards are applicable and cost-effective for cities and regional shopping centres Car park membership scheme for in-town centres Car park membership scheme for in-town centres Knowledge management network between retailers and the centre would be a further stage Most successful shopping centres are those where “Active marketing" and “Proactive management" are a feature

18. 17 About K-Means

19. The K-Means Algorithm 1. Choose a value for K, the total number of clusters. 2. Randomly choose K points as cluster centers. 3. Assign the remaining instances to their closest cluster center. 4. Calculate a new cluster center for each cluster. 5. Repeat steps 3-5 until the cluster centers do not change.

20. An Example Using K-Means

22. Figure 3.6 A coordinate mapping of the data in Table 3.6

24. Figure 3.7 A K-Means clustering of the data in Table 3.6 (K = 2)

25. General Considerations Requires real-valued data. We must select the number of clusters present in the data. Works best when the clusters in the data are of approximately equal size. Attribute significance cannot be determined. Lacks explanation capabilities.

26. 25 簡報完畢 敬請指導