Myrna Sabbagh Manager – Projects, DMX Group EMEA ( Myrna@DMXGroup)

Business IntelligenceA Solution by DMX Group and MicrosoftNovember 9, 2005Jaime CharafVice President, Operations for Europe, Africa and the Middle East(Jaime@DMXGroup.com) Myrna SabbaghManager – Projects, DMX Group EMEA (Myrna@DMXGroup.com) Rawad Yazigi Senior System Engineer , DMX Group EMEA(Rawad@DMXGroup.com)

Today’s Agenda • Who is DMX Group? • Why is a data strategy necessary? • What is Business Intelligence? - DMX Group perspective • Data Mining deep-dive • The most powerful tools combined with Excellence in Consultancy ( The DMX Group Mechanics ) • DMX Group Solution - Specific BI and data mining modules • Questions

DMX Group Background Started as Venture Capital-funded company in March 2000. • Headquartered in Bellevue, Washington • $45 million in funding – Mayfield, Mohr Davidow, American Express, Deutsche Bank • Grew to over 120 employees • 35 patents in technology and processes • Both technology and services

DMX Group Background • Beirut, Lebanon Office – May 15, 2005 • Fransabank Implementation Started • Significant expansion in Lebanon • Staff • Amman, Jordan office • Opened November 2003 • Staff and offices • Regional product support center • Fully scaled data center

Example Customers (1 of 2) Telecommunications Financial Services/Publications Manufacturers (especially Auto)

Example Customers (2 of 2) Retailers Technology Media & Portals

DMX Group Principals • Dr. Usama Fayyad – World-renowned authority in data mining, one of founders of field, editor-in-Chief of technical journal, academic and business leader, authored 2 books and over 150 technical articles. NASA, Microsoft, and technology companies leadership roles • Mr. Bassel Ojjeh – Expert in data warehousing, BI, and data operations, built some of largest DW in the world at Microsoft and DMX Group, built and shipped major products at Microsoft and Fox Software over 20 year career in DB, BI and IT. • Mr. Jaime Charaf – 15 years experience as a large systems integrator with EDS. 5 plus years experience building IT outsourcing organizations. Expert in advanced engineering CAD/CAM and data management systems with General Motors at the General Motors Tech Center and at the Milford Proving Grounds.

DMX Group & Microsoft Solution • Internationally and locally based • .Net web services expertise • Advanced and Advancing BI capabilities at little extra software costs • IT World’s leading company, dedicated to advancing BI tools • Industries most efficient use of hardware • Scales to largest BI projects in the world ( terabytes ) • New generation data mining algorithms integrated into DMX Group Solutions • Close partnership over the past 16 years

DMX Group Mission • Make enterprise data a working asset across the Institution: • Data strategy for the business • Implementation of BI and data mining capabilities • Enable companies to truly access data on an enterprise level • Illuminate business issues around data • Look for the possibilities • Expose data it to business users • Train people and change processes • Integrate with existing operational systems

The myths… • Companies have built up large impressive data warehouses • True data mining is pervasive across verticals • Large companies know how to do it • Off-the-shelf tools effectively discover valuable information at an enterprise-wide level

The truths… • Data is not organized or integrated, most data mining efforts end up not benefiting from existing data infra-structure • Firms care a lot about data • Firms are very concerned with understanding customer behavior • An extremely small number of firms are successfully mining data • Tailored BI and advanced mining solutions are always optimal

Progress • Who is DMX Group? • Why is a data strategy necessary? • What is Business Intelligence? - DMX Group perspective • Data Mining deep-dive • The most powerful tools combined with Excellence in Consultancy ( The DMX Group Mechanics ) • DMX Group Solution - Specific BI and data mining modules • Questions

Business data strategy • How can your data influence your revenues? • How do you optimize operations based on data? • How do you increase customer retention based on data? • How do you utilize enterprise data assets to spot new opportunities: • Cross-sell to existing customers • Grow new markets • Avoid problems such as fraud, abuse, churn, etc

Why care about data strategy? Data is the primary enabler of fact-based decisions in all departments: • Marketing • Identify long-term targets • Determine optimal customer mix • Attract, retain and maintain profitable relationships • Target intelligent offers to specific customers • Product Management • Support product design, pricing, packaging, and relationships • Assist in future product and offer development and testing

PhDs, • Statisticians Business Users Marketers DMX Group Strategy Strategies, Solutions, and Technologies That Bring the Power of Business Intelligence to the Business Decision Maker.

What is Data Warehousing? • Much confusion exists about the following terms • Data Warehousing • Data Model • Business Intelligence • Data Mining • Artificial Intelligence • Executive Information Systems

Billing Roaming Mediation CDRs Customer Care Data Warehouse - Summary SMS Prepaid Behavior Revenue Analysis Profitability Customer Segments Business Intelligence Data Warehouse Targeted Recommendations KYC

Data Warehouse / Business Intelligence

The Data Model –An Example

What is Business Intelligence? • Gartner, coined the term “business intelligence” during the 1990s. • BI is the transformation of raw data companies collect from their various operations into usable information. • BI software comprises specialized computer systems that allow an enterprise to easily aggregate, manipulate, and display data as actionable information

What is Data Mining? Finding interestingstructure in data • Structure: refers to statistical patterns, predictive models, correlations or hidden relationships • Interesting: is what is important to accomplishing the Enterprise’s goal – The Data Strategy!! • Examples of data mining tasks • Predictive Modeling (classification, regression) • Segmentation (Data Clustering ) • Affinity (Summarization) • This is relationships between fields, associations, and visualization

Data Mining and Databases Many interesting analysis queries are difficult to state precisely • Examples: • which records represent fraudulent transactions? • which households are likely to prefer one service or provider over another? • Who’s are the best credit risks in my customer DB? • Yet database may contain the needed information • Good or bad customers, profitability • Did or did not respond to mailed survey...

None of these tell you anything beyond the answer to a well-formulated question What is Data Mining? • Specific database queries • “Drill-through” reports • OLAP • Cubes • Custom Reports • Export to Excel • Lists of Users • Tracking Segments of Users • Data Templates NOT DATA MINING

Data Mining Tasks • Descriptive Analysis: to provide a concise and succinct summarization of a collection of data and distinguishes it from others. • Association: to discover relationships or correlation among items in transactions. • Prediction: to predict the value of a variable (the class) based on a set of training data based on values of measured variables.

Data Mining Tasks • Segmentation (Clustering): To identify clusters of data objects that are similar to one another. • Time series analysis: To analyze large set of time series data to find certain regularities and interesting characteristics, including search for similar sequences or subsequences and mining sequential patterns, periodicities, trends and deviations.

The Predictive Modeling Process • Define the Problem • Process the Data • Collect the Data • Clean the Data • Encode the Data • Transform the Data • Run and Evaluate Experiment s • Try different learning algorithms • Try different models • Try different data processing • Select Final Model • Test Final Model • Apply the Model

Class1 X2 Class 2 Classification • To analyze a set of training data whose class label is known and to construct a model for each class based on the features in the data. Then to classify a given input into these categories (classes). X1

X2 Segment 3 Segment 1 Segment 2 X1 Segmentation &Clustering • To identify clusters embedded in the data, where a cluster is a collection of data objects that are similar to one another. • Unlike classification, segmentation doesn’t associate categories to inputs. Instead, the division into groups is based solely on the geometrical structure of the input data.

Association • To discover relationships or correlation among a set of data. • It’s a predictive modeling task whose goal is to reveal combinations of items or events that often occur together. • Once identified these associations can be used to improve decision-making in a wide variety of applications.

Powerful Tools • Database queries - SQL • “Drill-Down” reports • Data Base / OLAP • SQL Server 2005 • Cubes • Export to Excel * (Clear example) • Lists of Users (Black Lists) • Tracking Segments of Users

BI Solutions do not come in boxes BI Tools used to build your roof are not BI

Implementation of BI – Service Model • In-depth interviewing of Department Heads and End Users • Define the specific reporting capabilities needed • Allows Tailored Solution – Size as needed • Locating data required by specific reports – The hunt • Top down analysis of needed data • Granularity is not beneficial if not necessary • From “your questions” to the data warehouse not vice versa • Avoid the “Data Tomb” • Cleaning data • Data that has never seen the light of day is always interesting! • Erroneous data extract reports are imperative • E/T/L • Custom reports • Custom data mining algorithms

A BI Implementation User Interface Portals & Reports Two Days Level of Time and Effort E/T/L Communication Business Intelligence Data Mining One Month Data Warehousing / Cubes / Data Marts Extraction and Loading Two Months Data Integration Data Discovery and Data Transformation ? Months

A Brief History of BI - Yesterday In the beginning there was Artificial Intelligence ( Data Mining ) and all tools were built by data miners Scientists built and exclusively used the first BI/AI applications Think about this…the same scientist that wrote 10,000 lines of AI code was creating HTML and basic queries in order to get this information to end Users Data mining methods have their origins in a variety of fields: Statistics, Databases, Pattern Recognition, AI, Visualization, High-Performance Computing, and Information Retrieval. Successful deployment of these technologies to e-business enterprise data requires: data warehouse construction, mechanisms to efficiently update the warehouse, integration of data mining technologies, and delivery of results in a form consumable by business end-users. – Dr. Usama Fayyad, April 2000

A Brief History of BI - Today • Each year BI tools grow more powerful • Today we are rolling out one of the most powerful BI tools yet created • Today’s successful data warehousing implementations MUST combine • The most powerful BI and Data Mining tools • The most experienced implementation methodologies • And yet must reduce costs to BI customers

A Brief History of BI – Tomorrow • DMX Group’s R&D organizations are studying how to get out of the UI, E/T/L, and data base building business – SQL Server 2005 helps us… • Mass produced tools significantly lower BI costs • We are excited about Microsoft’s Investment in BI • This allows DMX Group to focus on • Refined Implementation Methodology • Tailoring to meet each customer’s specific needs • Patented Data Mining (AI) Algorithms • More Exact Behavioral Prediction and Segmentation models • Industry/Environmental Specific Data Models

Abraham Lincoln was elected to Congress in 1846. John F. Kennedy was elected to Congress in 1946. Abraham Lincoln was elected President in 1860. John F. Kennedy was elected President in 1960. Lincoln's secretary was named Kennedy. Kennedy's Secretary was named Lincoln. Andrew Johnson, who succeeded Lincoln, was born in 1808. Lyndon Johnson, who succeeded Kennedy, was born in 1908. John Wilkes Booth, who assassinated Lincoln, was born in 1839.Lee Harvey Oswald, who assassinated Kennedy, was born in 1939. Lincoln was shot at the theater named "Ford.“ Kennedy was shot in a car called "Lincoln" made by "Ford." Lincoln was shot in a theater and the assassin ran to a warehouse. Kennedy was shot from a warehouse and the assassin ran to a theater. Both Presidents were shot in the head. Both assassins were known by their three names. Both names are composed of fifteen letters. Booth and Oswald were assassinated before their trials. A week before Lincoln was shot, he was in Monroe, Maryland. A week before Kennedy was shot, he was with Marilyn Monroe. Interesting…

Progress • Who is DMX Group? • Data Strategy – A critical success factor • Evolution of data management • What is Business Intelligence? - DMX Group perspective • What data mining is… What data mining is NOT • BTW: Why am I talking about services more than products? • DMX Group Solution - Specific BI and data mining modules • Questions

ATM Data Customer Segments Analytics Trans - actions Data Warehouse Product Customer Operational CRM How it Works Internet Banking SMS Banking Credit Cards Loans Targeted Recommendations

Advanced Mining Modules Available • Advanced Mining module available on top of Business Intelligence system • Customer Segmentation • Cross-sell / Up-sell • Churn Prediction • Marketing Campaign Analysis • Fraud Analysis (Due Diligence) • Customer Life Time Value

BI and Advanced Modeling ModulesCustomer Loyalty Modeling and Prediction

Assign Customer Value Build Churn Model Sample Database Score Database High Val Med Risk High Val High Risk High Val Low Risk Med Val Med Risk Med Val High Risk Med Val Low Risk High Risk Low Val Med Val Low Risk Med Risk High Val Low Val Med Risk Low Val High Risk Low Val Low Risk Client Analysis 6 2 4 3 6 5 Risk 1 Customer Interaction Base Value Customer/Account Data

LTV and Its Application • A customer’s life-time value (LTV) is the net value that a customer brings in to a business by the end of their service. I.e. their profit contribution. • LTV modeling allows for decisions to be made about individual customers that optimize return-on-investment (ROI). Examples: • Aggressive retention programs, such as account upgrade and Card renewal for high LTV. • Differentiated customer care treatment for reactivations/loyalty by customers with low LTV

Cost Rules Applied… Cost Rules are introduced to define value For Example: • Deposit Value • Product mix • Average. daily balance • Monthly service fees • Technical operations/Support costs • Branch/teller usage • Late payment/Overdraft history • Interest rate • Contract term • Credit Score • Employment history/Income

Sample Analytics Region 1 Region 3 Region 4 Region 6 Region 2 Region 5

Map Segments to Actions High Save Program NSF, high-costs Cautiously Defend Aggressively Defend Let them go Card Renewal Account Upsell Cost Reducing Programs Feature Add Elite Program Churn Probability Change Bad Behavior Grow Margin Nurture / Maintain Migration Loyalty Programs Feature Use Low Low Forecasted LTV High Negative

Sample Analytics Segment 2 Segment 1 Segment 3 Segment 4 Segment 5

BI and Advanced Modeling Modules Customer Segmentation

Myrna Sabbagh Manager – Projects, DMX Group EMEA ( Myrna@DMXGroup)