1. Boire Filler Group Desired Outcomes: Data Mining 1. Explain the fundamental concepts and business uses of data mining 2. Describe the critical aspects of customer data for marketing analytics 3. Understand the role of predictive modelling in business 4. Build a predictive model 5. Demonstrate the ability to select appropriate techniques for solving business problems 6. Understand the importance of customer segmentation

2. Boire Filler Group What this course will NOT do Teach you all the statistics you need to do data mining Teach you all the statistics you need to do data mining Replace real-world experience analyzing databases Replace real-world experience analyzing databases  Turn you into an immediate data mining practitioner

3. Boire Filler Group What this course will do Help you understand how and when to use data mining Help you understand how and when to use data mining Assist you in talking to data miners (internally or externally) Assist you in talking to data miners (internally or externally) Begin your training as a data miner Begin your training as a data miner

4. Intro to Data Mining MARK2039 Spring 2005 George Brown College

5. Boire Filler Group What is Data Mining? What is Data Mining? The process of exploration and analysis, by automatic means, of large quantities of data to discover meaningful patterns and rules The process of exploration and analysis, by automatic means, of large quantities of data to discover meaningful patterns and rules What does this mean from a business standpoint ? What does this mean from a business standpoint ? –Capitalization of above learning to maximize ROI for a given business process.

6. Boire Filler Group What is Data Mining? Continued... Data Mining is revolutionizing business today Data Mining is revolutionizing business today The old business paradigms are no longer acceptable The old business paradigms are no longer acceptable Companies recognize their information as a critical asset Companies recognize their information as a critical asset The most successful companies in the coming millennium will be able to intelligently utilize this information for profit-maximization decisions The most successful companies in the coming millennium will be able to intelligently utilize this information for profit-maximization decisions

7. Boire Filler Group Why the Growth in Data Mining? Marketers are no longer revenue-driven, but ROI driven Marketers are no longer revenue-driven, but ROI driven Organizations have are becoming customer centric vs. product centric Organizations have are becoming customer centric vs. product centric Too much noise and confusion in the market place Too much noise and confusion in the market place Societal changes include: Societal changes include: –Consumers are time conscious –Emphasis on quality and value –Aging population –Emphasis on “What's in it for me” ?

8. Boire Filler Group Why the Growth in Data Mining? Technological Changes Technological Changes –Increased storage and processing capacity within a constantly cost-reduction environment –Increased use of statistical tools and software for enhancing business decision-making One-to-One Marketing is becoming the “norm” One-to-One Marketing is becoming the “norm” –Increased emphasis on developing customer loyalty programs –Information represents a critical requirement in developing customer loyalty programs –Mining the above information intelligently is the key towards successful customer loyalty programs. The Web The Web –Easy and timely access to large volume of data

9. Boire Filler Group Data Mining as a Profession The most important asset for successful data mining is people. The most important asset for successful data mining is people. Successful hiring factors to look for are: Successful hiring factors to look for are: Quantitative skills Quantitative skills Business and problem-solving skills Business and problem-solving skills Programming skills Programming skills Knowledge of data structure, file structure, system structure and their integration Knowledge of data structure, file structure, system structure and their integration Communication skills and ability to liase with marketing and systems departments Communication skills and ability to liase with marketing and systems departments

10. Boire Filler Group Common Software SAS (Enterprise Miner, Base SAS) SAS (Enterprise Miner, Base SAS) SPSS SPSS IBM Intelligent Miner IBM Intelligent Miner Angoss Knowledge Studio Angoss Knowledge Studio

11. Boire Filler Group Common applications Fraud detection Fraud detection Direct marketing Direct marketing Call analysis Call analysis Customer segmentation Customer segmentation Drug testing Drug testing Quality control Quality control Credit scoring Credit scoring Click stream analysis Click stream analysis

12. Boire Filler Group Common Marketing Applications 1) Acquisition of new customers. 2) Developing Up-Sell strategies 3) Developing Cross-Sell strategies 4) Reducing customer defection 5) Creation of target customer groups for existing customer marketing programs 6) Campaign management analysis 7) Identifying high value and high potential value customers 8) Product affinity and bundling analysis 9) Retail site location analysis and product distribution analysis One of the primary objectives of data mining is to align marketing investment with customer potential.

13. Boire Filler Group Improving Business Results Data Mining is about identifying opportunities to improve business results. Data Mining is about identifying opportunities to improve business results. This may be achieved by identifying segments of customers that outperform others based on certain business objectives (an objective function) This may be achieved by identifying segments of customers that outperform others based on certain business objectives (an objective function) For example, the results from the predictive model below identifies customers more or less likely to respond to a particular DM offer. For example, the results from the predictive model below identifies customers more or less likely to respond to a particular DM offer.

14. Boire Filler Group Mass marketing. Same investment for all customers High HighMarketingInvestment$/Customer Low Low Low Customer Value / Potential High

15. Boire Filler Group Align marketing investment with customer potential High HighMarketingInvestment$/Customer Low Low Low Customer Value / Potential High

16. Boire Filler Group Different objectives ===> Different approaches Directed data mining  When you know what you are looking for. e.g. Produce a predictive model to identify customers most likely to respond. Undirected data mining  A process of discovery. e.g.What can the data tell us about customers?

17. Boire Filler Group Example: Which are these? Predicting the likelihood of response in the next campaign Predicting the likelihood of response in the next campaign Analyzing call logs to determine which are complaints Analyzing call logs to determine which are complaints Determining the data mining strategy for the next year Determining the data mining strategy for the next year Why are sales decreasing in the last 3 years Why are sales decreasing in the last 3 years Assigning a likelihood of default score on a mortgage applicant Assigning a likelihood of default score on a mortgage applicant Grouping customers together into segments Grouping customers together into segments

18. Boire Filler Group Four Stages of Data Mining Four Stages of Data Mining

19. Boire Filler Group The Data Mining Process - Problem Identification Stage 1)Problem Identification Identify overall business strategy Identification and Prioritization of business strategy components which can be resolved through data mining Provide information regarding current data environment Role of MarketerRole of Data MinerRole of Systems Example: Improve retention results. What is the data mining impact?

20. Boire Filler Group Conduct preliminary data diagnostics: -source file extractions -Data Dumps -Determination of links and keys between files -Frequency distributions on all fields on all files The Data Mining Process: Creation of the Analytical File Role of Marketer Role of Data MinerRole of Systems Understand sources of data that are used in data mining project Acts as Data Consultant to Data Miner: -Data Dictionary -File Layouts -Star Schema -Data Nuances /Interpretations

21. Boire Filler Group Role of Marketer –Have clear understanding of the key information within data mining solution –Have clear understanding of how data mining solution performs from business perspective –Have clear understanding of how to use data mining solution in future campaign Role of Data Miner/ Analyst –Design appropriate reports to communicate final data mining solution and its expected performance –Consult and advise on how data mining solution should be used and tracked in future campaign The Data Mining Process : Application of Data Mining Techniques

22. Boire Filler Group The Data Mining Process : Implementation Role of Marketer Review current results of solution vs. results of solution achieved through development Role of Data Miner Apply solution to database for upcoming campaign Validate application of learning by checking random dump of 10 records Produce results Role of Systems Assist or run program to apply data mining solution to database for upcoming campaign

23. Boire Filler Group What is the impact of data mining First Example: Increase number of orders from 100000 to 200000. Is this caused by data mining First Example: Increase number of orders from 100000 to 200000. Is this caused by data mining Second Example: Increase the order rate per customer from 1% to 2% with total orders decreasing by 100000. Is this caused by data mining Second Example: Increase the order rate per customer from 1% to 2% with total orders decreasing by 100000. Is this caused by data mining A third example to illustrate the impact of data mining A third example to illustrate the impact of data mining

24. Boire Filler Group Problem Identification How does data mining impact the business? How does data mining impact the business? –Example 1: Direct Mail Campaign to 500000 customers. Promotion cost per piece is $1.00 –Assume data mining can bring 10% improvement in performance for all campaigns. What is the potential data mining impact here? –What other metric do we need to think of ?

25. Boire Filler Group Problem Identification How does data mining impact the business? How does data mining impact the business? –Example 1: Direct Mail Campaign to 500,000 customers. Promotion cost per piece is $1.00 Note: the calculation is an opportunity cost. It calculates the additional promotional cost to achieve 5500 responders without data mining.

26. Boire Filler Group Problem Identification –Example 2: Outbound telemarketing campaign to 300,000 customers. Promotion cost person is $6.00 –Example 3: Email campaign to 1,000,000 customers with cost per promotion of $.10 Of the three examples, which campaign would you focus your data mining activities on?

27. Boire Filler Group Identifying data mining opportunities within your organization Explore the organizations key business challenges Explore the organizations key business challenges Determine if improved customer/prospect targeting or segmentation would improve results Determine if improved customer/prospect targeting or segmentation would improve results Review the following questions: Review the following questions: Are the overall business results reasonable? Are the overall business results reasonable? Is the product or service in a stable business environment? Is the product or service in a stable business environment? What is the current data environment? What is the current data environment? What type of budgets are available? What type of budgets are available? What type of margins does the product or service contribute to the organization? What type of margins does the product or service contribute to the organization? How many customers or prospects do you currently target? How many customers or prospects do you currently target? Will the results of your data mining exercise be actionable based on the results you are trying to improve? Will the results of your data mining exercise be actionable based on the results you are trying to improve?

28. Boire Filler Group Example 1-Identifying Data Opportunities Company A has a 10,000 customers enrolled in a service that is renewed on an annual basis. Each year only 10% of all customers renew their service. Their renewal rates for other products and services averages 70%. Should data mining be used to improve retention? Company A has a 10,000 customers enrolled in a service that is renewed on an annual basis. Each year only 10% of all customers renew their service. Their renewal rates for other products and services averages 70%. Should data mining be used to improve retention?

29. Boire Filler Group Example 2-Identifying Data Opportunities Company B has a 1,000,000 customers and has been cross selling a long distance phone plan for over 2 years. Over the last 6 months acquisition results have decline and the cost per new plan member has increased beyond target levels. Should data mining be used to improve results? Company B has a 1,000,000 customers and has been cross selling a long distance phone plan for over 2 years. Over the last 6 months acquisition results have decline and the cost per new plan member has increased beyond target levels. Should data mining be used to improve results? Give me an example of a data mining solution? Give me an example of a data mining solution?

30. Boire Filler Group Example 3-Identifying Data Opportunities Art vs. Science Art vs. Science Retail Company collect no information on its customers. Market research has indicated that the key drivers of purchase behaviour are high income, female immigrants. Retail Company collect no information on its customers. Market research has indicated that the key drivers of purchase behaviour are high income, female immigrants. –No individual-level information –Information is available only at aggregate or postal code level –Advantages of using advanced statistical techniques are minimized within this data environment. –Quicker and simpler solutions will suffice.

31. Boire Filler Group Example 3-Identifying Data Opportunities The Solution: Using an “RFM” index approach, create postal code index based on three Statistics Canada Variables: Using an “RFM” index approach, create postal code index based on three Statistics Canada Variables: Median taxfiler income of postal code Median taxfiler income of postal code % of population female within postal code % of population female within postal code % of population landed immigrants within postal code % of population landed immigrants within postal code Income % Female % Landed Immig. Average Postal Code $40,00052%5% M5A 1J2 $50,00060%10% Index1.251.152 The index for M5A 1J2 is (.33 x 1.25)+(.33 x 1.15)+(.33 x 2) = 1.45

32. Boire Filler Group Example 3-Identifying Data Opportunities This index scheme can then be used to score each postal code. The 800000 postal codes in Canada are then ranked into 20 half deciles based on descending index score. How would you use this above tool?

33. Boire Filler Group Example 4: An SVP of a large bank has spent thousands of dollars creating a credit card response model. An SVP of a large bank has spent thousands of dollars creating a credit card response model. The predictive model identifies those who are most likely to respond to the banks next offer. The predictive model identifies those who are most likely to respond to the banks next offer. The model will allow the bank to save considerable money – mailing only 20% of the prospects, they will generate 70% of all the responders. The model will allow the bank to save considerable money – mailing only 20% of the prospects, they will generate 70% of all the responders.

34. Boire Filler Group Example 4 (continued) “But I need the maximum number of responders” “But I need the maximum number of responders” Attaining even 70% of the responders will not meet the campaign expectations Attaining even 70% of the responders will not meet the campaign expectations What is the real problem here What is the real problem here  Data Mining is not always necessary