business intelligence presentation - part 2 of 2

8/9/2019 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!

business intelligence presentation - part 2 of 2

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