Finding Hidden Intelligence with Predictive Analysis of Data MiningRafal LukawieckiStrategic Consultant, Project Botticelli Ltd

rafal@projectbotticelli.com

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Objectives

• Show use of Microsoft SQL Server 2008 Analysis Services Data Mining

• Tantalise you with the power of DM

This seminar is based on a number of sources including a few dozen of Microsoft-owned presentations, used with permission. Thank you to Marin Bezic, Kathy Sabourin, Aydin Gencler, Bryan Bredehoeft, and Chris Dial for all the support. Thank you to Maciej Pilecki for assistance with demos.

The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or RafalLukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express,implied or statutory, as to the information in this presentation.

Portions © 2009 Project Botticelli Ltd & entire material © 2009 Microsoft Corp. Some slides contain quotations from copyrightedmaterials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved.Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S.and/or other countries. The information herein is for informational purposes only and represents the current view of Project BotticelliLtd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it shouldnot be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy ofany information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as tothe information in this presentation. E&OE.

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Agenda

• Data Mining and Predictive Analytics

• Server and Process Considerations

• Scenarios & Demos

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What does Data Mining Do?

Explores Your Data

Finds Patterns

Performs Predictions

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Typical Uses

Data Mining

Seek Profitable Customers

Understand Customer

Needs

Anticipate Customer

Churn

Predict Sales &

Inventory

Build Effective

Marketing Campaigns

Detect and Prevent Fraud

Correct Data

During ETL

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Analysis ServicesServer

Mining Model

Data Mining Algorithm DataSource

Server Mining Architecture

Excel/Visio/SSRS/Your App

OLE DB/ADOMD/XMLA

Deploy

BIDSExcelVisioSSMS

AppData

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Mining Model Mining ModelMining Model

Mining Process

DM EngineDM Engine

Training data

Data to be

predictedMining Model

With

predictions

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Concepts

• Case – set of Columns (attributes) you want to analyse

• Age, Gender, Annual Spending

• Column Usage

• Input: We analyse them

• Predict: Build a model for them

• Nested Case – case containing a table column

• Age, Gender, Annual Spending, Products, Purchases

• Case Key – unique ID of a case

• Data Mining Model – container of patterns discovered by a DM algorithm in your data

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SCENARIO: CUSTOMER CLASSIFICATION & SEGMENTATION

Who are our customers? Are there any relationships between their demographics and their buying power?

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Microsoft Decision Trees

• Use for:

• Classification: churn and risk analysis

• Regression: predict profit or income

• Association analysis based on multiple predictable variable

• Builds one tree for each predictable attribute

• Fast

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Decision Trees for Classification of Customers’ Buying Potential

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SCENARIO: PROFITABILITY AND RISK

Who are our most profitable customers? Can I predict profit of a future customer based on demographics? Are they creditworthy? How much should I charge them to give a good loan and protect against losses?

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Profitability and Risk

• Finding what makes a customer profitable is also classification or regression

• Typically solved with:

• Decision Trees (Regression), Linear Regression,

• and Neural Networks or Logistic Regression

• Often used for prediction

• Important to predict probability of the predicted, or expected profit

• Risk scoring

• Logistic Regression and Neural Networks

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Neural Network & Logistic Regression

• Applied to

• Classification

• Regression

• Great for finding complicated relationship among attributes

• Difficult to interpret results

• Gradient Descent method

• LR is NNet with no hidden layers

Age Education Sex Income

Input

Layer

Hidden

Layers

Output

LayerLoyalty

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1. Neural Networks for Profitability Analysis2. Predicting Lending Risk with Neural Networks

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SCENARIO: CUSTOMER NEEDS ANALYSIS

How do they behave? What are they likely to do once they bought that really expensive car? Should I intervene?

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Sequence Clustering

• Analysis of:

• Customer behaviour

• Transaction patterns

• Click stream

• Customer segmentation

• Sequence prediction

• Mix of clustering and sequence technologies

• Groups individuals based on their profiles including sequence data

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Analysis Customer Behaviour with Sequence Clustering

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SCENARIO: FORECASTING

What are my sales going to be like in the next few months? Will I have credit problems? Will my server need an upgrade in the next 3 months?

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Time Series

• Uses:

• Forecast sales

• Inventory prediction

• Web hits prediction

• Stock value estimation

• Regression trees with extras

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Forecasting Using Time Series

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TECHNIQUE SUMMARY

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Algorithms

Algorithm Description

Decision Trees Finds the odds of an outcome based on values in a training set

Association Rules

Identifies relationships between cases

Clustering Classifies cases into distinctive groups based on any attribute sets

Naïve Bayes Clearly shows the differences in a particular variable for various data elements

Sequence Clustering

Groups or clusters data based on a sequence of previous events

Time Series Analyzes and forecasts time-based data combining the powerof ARTXP (developed by Microsoft Research) for short-term predictionswith ARIMA (in SQL 2008) for long-term accuracy.

Neural Nets Seeks to uncover non-intuitive relationships in data

Linear Regression

Determines the relationship between columns in order to predict an outcome

Logistic Regression

Determines the relationship between columns in order to evaluate the probability that a column will contain a specific state

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Time Series

Sequence Clustering

Neural Nets

Naïve Bayes

Logistic Regression

Linear Regression

Decision Trees

Clustering

Association Rules

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Summary

• Data Mining is a powerful, predictive technology

• Turns data into valuable, decision-making knowledge

• SQL Server 2008 Analysis Services support Predictive Analytics

• Mine your mountains of data for gems of intelligence today!

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© 2009 Microsoft Corporation & Project Botticelli Ltd. All rights reserved.

The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The materialpresented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in thispresentation.

Portions © 2009 Project Botticelli Ltd & entire material © 2009 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors,as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and otherproduct names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informationalpurposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft mustrespond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticellicannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied orstatutory, as to the information in this presentation. E&OE.