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Business Intelligence Presentation - Data Mining (2/2)
The document explores the concept of business intelligence and data mining, highlighting methods for analyzing historical data and making forecasts for future business scenarios. It outlines various data mining tasks, predictive models, and algorithms, alongside their applications in different industries such as marketing, finance, and medicine. The text also addresses potential challenges in data mining, including the risk of deriving incorrect or irrelevant conclusions from analyzed data.
In this document
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Introduces Business Intelligence and serves as part 2 of data mining.
Discusses the limitations of historical data and the quest for predictive insights in business.
Defines Data Mining as a process that extracts patterns using various techniques from multiple disciplines.
Outlines business problems solved by data mining including recommendations, anomaly detection, and customer segmentation.
Lists key tasks in data mining like classification, regression, prediction, and cluster analysis.
Defines predictive modeling methods including classification and regression for discrete and continuous values.
Explains descriptive models that reveal correlations and similarities within data, focusing on clusterization and market basket analysis.
Illustrates the data mining cycle, transforming data into information, identifying opportunities, and measuring results.
Describes the role of data warehouses in unifying data sources and ensuring reliable data for mining.
Details the steps involved in a data mining project from problem formulation to application integration.
Defines a model in data mining as a mathematical conclusion used for predictions and knowledge extraction.
Presents a case study using data to determine golf-playing decisions based on weather conditions.
Visual representation of a classification model showing predicted behaviors based on weather conditions.
Lists various data mining algorithms such as Naive Bayes, Decision Trees, and Neural Networks.
Explores various fields where data mining can be applied including marketing, finance, and healthcare.
Discusses how data mining assists in CRM through targeted marketing, customer segmentation, and retention.
Describes methods of clustering customers based on behavioral characteristics and purchase patterns.
Elaborates on classifying customers based on purchase and payment behavior.
Explains association rules in data mining that focus on cross-selling and upselling.
Covers forecasting and projections regarding revenue, payment, and product sales.
Introduces other cases of data mining applications like influencer identification and prediction calculators.
Discusses potential pitfalls in data mining like finding misleading patterns and non-actionable insights.
End of the presentation with thanks to the audience.
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Business Intelligence Presentation - Data Mining (2/2)
- 1. Business Intelligence Data Mining (Part 2 of 2)
- 2.
- 3. How far canI 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. Data Mining • Isa 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. Business Problems • Recommendations •Anomaly Detection • Customer abandon analysis • Risk Management • Customer segmentation • Targeted advertising • Projections
- 6. Data Mining Tasks •Classification • Estimation / Regression • Prediction / Projection (Forecasting) • Association Rules / Affinity Groups • Clusterization
- 7. Predictive Models • Classifications • Discrete value prediction • Yes, No • High, Medium, Low • Estimation / Regression • Continuous value prediction • Amounts • Numbers • Projection / Forecasting
- 8. Descriptive Models • AssociationRules / Affinity • Looks for correlation indexes among diverse associated elements • Market Basket Analysis • Clusterization • Groups items according to similarity • “Automatic” classification
- 9. Work Cycle Transform Data to Information Identify Business Opportunities Act with Information Measure Results
- 10. Data Mining andDWh • 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. Project Cycle • BusinessProblem Formulation • Data Gathering • Data transformation and cleansing • Model Construction • Model Evaluation • Reports and Prediction • Application Integration • Model Management
- 12. What is aModel? • 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. 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. Model Outlook Overcast Rainy Sunny YES Wind Humidity NO YES <=77.5 >77.5 YES NO YES NO
- 15. Data Mining Algorithms • Naive Bayes • Decission Trees • Autoregression trees (ARTxp and ARIMA) • K-Means • Kohonen Maps • Neural Networks • Logistic regression • Time Series
- 16. Where can Iuse 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. Data Mining andCRM (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. Data Mining andCRM (2) • Improve direct marketing campaign results • Customer base segmentation • Reduce credit risk exposure • Customer Lifetime Value • Customer retention and loss
- 19. Clustering • “Self” CustomerSegmentation • Descriptive Characteristics • Behavioural Characteristics • Relationship • Purchases • Payments
- 20. Classification • Customers bypurchase behaviour • Customers by payment behaviour • Customers by resources devoted/needed to their service • Customers by credit profile • Customers by attention required
- 21. Association Rules • MarketBasket Analysis • Cross Selling • Up Selling
- 22. Prediction / Forecasting •Revenue Projection • Payment Projection • Number of Products sold Projection • Cash Flow Projection
- 23. Some other DMcases • Key Influencers • Predictions Calculator
- 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. 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.