Business Intelligence Presentation - Data Mining (2/2)

Business IntelligenceData Mining

(Part 2 of 2)

The End?

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?

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

Business Problems• Recommendations

• Anomaly Detection

• Customer abandon analysis

• Risk Management

• Customer segmentation

• Targeted advertising

• Projections

Data Mining Tasks

• Classification

• Estimation / Regression

• Prediction / Projection (Forecasting)

• Association Rules / Affinity Groups

• Clusterization

Predictive Models• Classifications

• Discrete value prediction

• Yes, No

• High, Medium, Low

• Estimation / Regression

• Continuous value prediction

• Amounts

• Numbers

• Projection / Forecasting

Descriptive Models

• Association Rules / Affinity

• Looks for correlation indexes among diverse associated elements

• Market Basket Analysis

• Clusterization

• Groups items according to similarity

• “Automatic” classification

Work Cycle

Transform Data to

Information

Act with Information

Measure Results

Identify Business Opportunities

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

Project Cycle• Business Problem Formulation

• Data Gathering

• Data transformation and cleansing

• Model Construction

• Model Evaluation

• Reports and Prediction

• Application Integration

• Model Management

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

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 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

Model

Outlook

YES Wind Humidity

YES YESNO NO

Overcast Rainy Sunny

NO YES >77.5<=77.5

Data Mining Algorithms• Naive Bayes

• Decission Trees

• Autoregression trees (ARTxp and ARIMA)

• K-Means

• Kohonen Maps

• Neural Networks

• Logistic regression

• Time Series

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

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

Data Mining and CRM (2)

• Improve direct marketing campaign results

• Customer base segmentation

• Reduce credit risk exposure

• Customer Lifetime Value

• Customer retention and loss

Clustering

• “Self” Customer Segmentation

• Descriptive Characteristics

• Behavioural Characteristics

• Relationship

• Purchases

• Payments

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

Association Rules

• Market Basket Analysis

• Cross Selling

• Up Selling

Prediction / Forecasting

• Revenue Projection

• Payment Projection

• Number of Products sold Projection

• Cash Flow Projection

Some other DM cases

• Key Influencers

• Predictions Calculator

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

Possible Problems... (1I)

• To learn things that are true, but not useful

• Learn things that we already knew

• Learn things that cannot be applied

Thank you!