1.
Business Intelligence
Data Mining
(Part 2 of 2)

2.
The End?

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.
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.
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
• Association Rules / 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 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.
Project Cycle
• Business Problem Formulation
• Data Gathering
• Data transformation and cleansing
• Model Construction
• Model Evaluation
• Reports and Prediction
• Application Integration
• Model Management

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.
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 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.
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.
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.
Clustering
• “Self” Customer Segmentation
• Descriptive Characteristics
• Behavioural Characteristics
• Relationship
• Purchases
• Payments

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.
Association Rules
• Market Basket Analysis
• Cross Selling
• Up Selling

22.
Prediction / Forecasting
• Revenue Projection
• Payment Projection
• Number of Products sold Projection
• Cash Flow Projection

23.
Some other DM cases
• 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.
Thank you!