The document discusses several topics related to data mining and analytics of web data: 1. It introduces common categories of web mining including web usage mining, web content mining, and web structure mining. 2. Example goals of web analytics are improving website design, measuring marketing strategy effectiveness, and personalization. 3. Challenges of web data analysis include messy and imperfect data that requires extensive preprocessing, focusing on actionable metrics, and new types of event-driven data. 4. Advanced data mining applications discussed include navigation analysis, multivariate testing, semi-supervised learning, and recommender systems.

1. Annual Conference 2010

3. Data Mining Track WIFI ibahn_conference CODE: 01A3D9

4. Walking In Circles

5. IT’S GREAT BEING A MODELER However…

6. SO, WHAT DO YOU DO FOR A LIVING? Something with data Some stuff with statistics and numbers It’s complicated… (do you have an hour?)

7. SO, WHAT DO YOU DO FOR A LIVING? However…

8. THE CRM CIRCLE

16. Modeling Automation

17. MODEL AUTOMATION

18. MODEL FARMERS ?

19. MODEL ARCHITECTS

20. IS AUTOMATION A THREAT?

21. OR AN OPPORTUNITY?

24. Data Mining Track WIFI ibahn_conference CODE: 01A3D9

25. Web Analytics 2.0: state of the art, challenges and opportunities Prof. dr. Bart Baesens Department of Decision Sciences and Information Management K.U.Leuven (Belgium) Vlerick Leuven Ghent Management School (Belgium) School of Management University of Southampton (United Kingdom) [email_address] www.dataminingapps.com Twitter: DataMiningApps Facebook: Data Mining with Bart

33. Dashboards

36. SAS Web Analytics: funnel example

37. SAS Web Analytics: site overlay

41. Example Recommender System

43. Example Bayesian Network

47. Example ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?

50. Annual Conference 2010