Business Intelligence Presentation - Data Mining (2/2)

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In this second part of the Business Intelligence Presentation, we dive into Data Mining, what it is, its business applications and some CRM related examples.

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Business Intelligence Presentation - Data Mining (2/2)

  1. 1. Business Intelligence Data Mining (Part 2 of 2)
  2. 2. The End?
  3. 3. How far can I go? • Storing and analyzing historical data you can see just one part of reality (the past and the present) • Is there a way to answer questions not yet made? Can I look into the future? • Can I predict how my business is going to work? What about the market? And my customers?
  4. 4. Data Mining • Is a process to extract patterns from data • “We’re drowning in data but information thirsty” • Data Mining borrows techniques from statistics, probability, maths, artificial intelligence and other fields
  5. 5. Business Problems • Recommendations • Anomaly Detection • Customer abandon analysis • Risk Management • Customer segmentation • Targeted advertising • Projections
  6. 6. Data Mining Tasks • Classification • Estimation / Regression • Prediction / Projection (Forecasting) • Association Rules / Affinity Groups • Clusterization
  7. 7. Predictive Models • Classifications • Discrete value prediction • Yes, No • High, Medium, Low • Estimation / Regression • Continuous value prediction • Amounts • Numbers • Projection / Forecasting
  8. 8. Descriptive Models • Association Rules / Affinity • Looks for correlation indexes among diverse associated elements • Market Basket Analysis • Clusterization • Groups items according to similarity • “Automatic” classification
  9. 9. Work Cycle Transform Data to Information Identify Business Opportunities Act with Information Measure Results
  10. 10. Data Mining and DWh • The Data Warehsouse unifies diverse data sources in one common repository • Before the DM process, you must have reliable data sources • Data must be presented in a way that eases analysis
  11. 11. Project Cycle • Business Problem Formulation • Data Gathering • Data transformation and cleansing • Model Construction • Model Evaluation • Reports and Prediction • Application Integration • Model Management
  12. 12. What is a Model? • The model is a set of conclusions reached (in mathematical format) after data processing • Is used to extract knowledge and to compare it to new data to reach to new conclusions • It has some efficency percentage • Must be adjusted to make helpful predictions • It is time-constrainted
  13. 13. Cases Outlook Temperature (C) Humidity Wind Play Golf? Sunny 29.4 85% NO No Sunny 26.6 90% YES No Overcast 28.3 78% NO Yes Rainy 21.1 96% NO Yes Rainy 20.0 80% NO Yes Rainy 18.3 70% YES No Overcast 17.7 65% YES Yes Sunny 22.2 95% NO No Sunny 20.5 70% NO Yes Rainy 23.8 80% NO Yes Sunny 23.8 70% YES Yes Overcast 22.2 90% YES Yes Overcast 27.2 75% NO Yes Rainy 21.6 80% YES No
  14. 14. Model Outlook Overcast Rainy Sunny YES Wind Humidity NO YES <=77.5 >77.5 YES NO YES NO
  15. 15. Data Mining Algorithms • Naive Bayes • Decission Trees • Autoregression trees (ARTxp and ARIMA) • K-Means • Kohonen Maps • Neural Networks • Logistic regression • Time Series
  16. 16. Where can I use them? • Marketing: Segmentation, Campaigns, Results, Loyalty,... • Sales: Behaviour detection, Sales habits • Finances: Investments, Portfolio Management • Banks and Assurance: Credit Check • Security: Fraud Detection • Medicine: Possible treatment analysis • Manufacturing: Quality Control • Internet: Click analysis, Text Mining
  17. 17. Data Mining and CRM (1) • Detect the best prospect / customers • Select the best communication channel for prospects / customers • Select an appropriate message to prospects / customers • Cross-selling, Up-selling and sales recommendation engines
  18. 18. Data Mining and CRM (2) • Improve direct marketing campaign results • Customer base segmentation • Reduce credit risk exposure • Customer Lifetime Value • Customer retention and loss
  19. 19. Clustering • “Self” Customer Segmentation • Descriptive Characteristics • Behavioural Characteristics • Relationship • Purchases • Payments
  20. 20. Classification • Customers by purchase behaviour • Customers by payment behaviour • Customers by resources devoted/needed to their service • Customers by credit profile • Customers by attention required
  21. 21. Association Rules • Market Basket Analysis • Cross Selling • Up Selling
  22. 22. Prediction / Forecasting • Revenue Projection • Payment Projection • Number of Products sold Projection • Cash Flow Projection
  23. 23. Some other DM cases • Key Influencers • Predictions Calculator
  24. 24. Some Possible Problems (1) • To learn things that are not true • The patterns may not represent any underlying rule • The model may not represent a relevant number of examples • Data may be in a detail level not enough for analysis
  25. 25. Possible Problems... (1I) • To learn things that are true, but not useful • Learn things that we already knew • Learn things that cannot be applied
  26. 26. Thank you!

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