BA_EM Electronic Marketing

1. BA_EM Electronic Marketing 22. 10. 2013 – Pavel Kotyza @VŠFS

2. Agenda • Effective data mining as a source of relevant data about customer needs

3. What is data mining? • Absolut • Unknown • Useful

4. What is data?

5. Data-mining traditional uses

6. Data-mining traditional uses

7. Data-mining traditional uses

8. Data-mining traditional uses

9. Data-mining traditional uses

10. Question • Say an example of data mining?

11. My example

12. What is data mining? • Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. • Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. • Data mining is also known as • Knowledge Discovery (KD) in Data (KDD).

13. The key properties of data mining are • Automatic discovery of patterns • Prediction of likely outcomes • Creation of actionable information • Focus on large data sets and databases

14. Video http://www.youtube.com/watch?v=BjznLJcgSFI

15. Why to use • Data mining can answer questions that cannot be addressed through simple query and reporting techniques.

16. Video example • http://www.ted.com/playlists/56/making_sense_of_too_much_data.html

17. Data Mining types

18. Automatic Discovery • Data mining is accomplished by building models. A model uses an algorithm to act on a set of data. The notion of automatic discovery refers to the execution of data mining models. • Data mining models can be used to mine the data on which they are built, but most types of models are generalizable to new data. The process of applying a model to new data is known as scoring.

19. Prediction • Many forms of data mining are predictive. • E.g. A model might predict income based on education • Predictionshave an associated probability (How likely is this prediction to be true?). Prediction probabilities are also known as confidence • How confident can I be of this prediction? • Some forms of predictive data mining generate rules, which are conditions that imply a given outcome. • E.g. A rule might specify that a person who has a bachelor's degree and lives in a certain neighborhood is likely to have an income greater than the regional average. Rules have an associated support • What percentage of the population satisfies the rule?

20. Grouping • Other forms of data mining identify natural groupings in the data. • E.g. A model might identify the segment of the population that has an income within a specified range, that has a good driving record, and that leases a new car on a yearly basis.

21. Actionable Information • Data mining can derive actionable information from large volumes of data. • For example, a town planner might use a model that predicts income based on demographics to develop a plan for low-income housing. • A car leasing agency might a use model that identifies customer segments to design a promotion targeting high-value customers.

22. Why is it important now • Data all around us • Social networks • Search in e-shops • Targeting Advertising • Information overload

23. Social Insight & Personal Advantage • Rent prices • Blogs and News • Movie data • Fashion • Product Prices • Hotties / Adult content categories

24. The beauty of data visualization http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization.html

25. Data Mining Process

26. Problem definition

27. Problem definition

28. Problem definition

29. Data Gathering & Preparation • Data Access • Data Sampling • Data Transformation

30. Model Building & Evaluation • Create Model • Test Model • Evaluate & Interpret Model

31. Knowledge Deployment • Model Apply • Custom Reports • ExternalApplications

32. How predictable are you? http://www.youtube.com/watch?v=DaWcL3oOd-E

33. The End! • Are there in your company/school any assholes?Solution: of the problem: • D-Fenz Tie Test - Extreme example