WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
Business Intelligence Presentation - Part 2 of 2
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Transcript of Business Intelligence Presentation - Part 2 of 2
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Business Intelligence
Data Mining(Part 2 of 2)
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The End?
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How far can I go?
Storing and analyzing historical data you can see justone 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?
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Data Mining
Is a process to extract patterns from data
Were drowning in data but informationthirsty
Data Mining borrows techniques fromstatistics, probability, maths, artificialintelligence and other fields
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Business Problems
Recommendations
Anomaly Detection
Customer abandon analysis Risk Management
Customer segmentation
Targeted advertising
Projections
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Data Mining Tasks
Classification
Estimation / Regression
Prediction / Projection (Forecasting)
Association Rules / Affinity Groups Clusterization
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Predictive Models
Classifications Discrete value prediction Yes, No
High, Medium, Low Estimation / Regression Continuous value prediction
Amounts Numbers Projection / Forecasting
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Descriptive Models
Association Rules / Affinity
Looks for correlation indexes amongdiverse associated elements
Market Basket Analysis
Clusterization
Groups items according to similarity
Automatic classification
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Work Cycle
Transform
Data to
Information
Act with
Information
Measure Results
Identify Business Opportunities
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Data Mining and DWh
The Data Warehsouse unifies diverse data sources
in one common repository
Before the DM process, you must have reliable datasources
Data must be presented in a way that eases analysis
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Project Cycle
Business Problem Formulation Data Gathering
Data transformation and cleansing
Model Construction
Model Evaluation
Reports and Prediction Application Integration
Model Management
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What is a Model?
The model is a set of conclusions reached (inmathematical format) after data processing
Is used to extract knowledge and to compare itto new data to reach to new conclusions
It has some efficency percentage
Must be adjusted to make helpful predictions
It is time-constrainted
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CasesOutlook 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 YesRainy 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 YesSunny 23.8 70% YES Yes
Overcast 22.2 90% YES Yes
Overcast 27.2 75% NO Yes
Rainy 21.6 80% YES No
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Model
Outlook
YES Wind Humidity
YES YESNO NO
Overcast Rainy Sunny
NO YES >77.5
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Data Mining Algorithms
Naive Bayes
Decission Trees
Autoregression trees (ARTxp and ARIMA) K-Means
Kohonen Maps
Neural Networks
Logistic regression
Time Series
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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
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Data Mining and CRM (1)
Detect the best prospect / customers
Select the best communication channel forprospects / customers
Select an appropriate message toprospects / customers
Cross-selling, Up-selling and salesrecommendation engines
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Data Mining and CRM (2)
Improve direct marketing campaign results
Customer base segmentation
Reduce credit risk exposure
Customer Lifetime Value Customer retention and loss
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Clustering
Self Customer Segmentation
Descriptive Characteristics
Behavioural Characteristics
Relationship
Purchases Payments
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Classification
Customers by purchase behaviour
Customers by payment behaviour
Customers by resources devoted/neededto their service
Customers by credit profile
Customers by attention required
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Association Rules
Market Basket Analysis Cross Selling
Up Selling
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Prediction / Forecasting
Revenue Projection
Payment Projection
Number of Products sold Projection
Cash Flow Projection
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Some other DM cases
Key Influencers
Predictions Calculator
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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 ofexamples
Data may be in a detail level not enough for analysis
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Possible Problems... (1I)
To learn things that are true, but notuseful
Learn things that we already knew
Learn things that cannot be applied
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Thank you!