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Business Intelligence with SAP BI and SAP BusinessObjects Software Christine Davis – University of Arkansas Nitin Kale – University of Southern California.

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Presentation on theme: "Business Intelligence with SAP BI and SAP BusinessObjects Software Christine Davis – University of Arkansas Nitin Kale – University of Southern California."— Presentation transcript:

1 Business Intelligence with SAP BI and SAP BusinessObjects Software Christine Davis – University of Arkansas Nitin Kale – University of Southern California SAP Curriculum Congress 2010

2 ©SAP AG All rights reserved. / Page 2 Introduction to Data Mining Data Mining Process Data Mining Methods Data Mining Case Studies Resources SAP University Alliances Module BI1-M6

3 ©SAP AG All rights reserved. / Page 3 Introduction to Data Mining The majority of reports are based on known facts BUT We don’t know what we don’t know

4 ©SAP AG All rights reserved. / Page 4 What is Driving Data Mining? Changes in Technology: Increased usage of the Internet Appearance of data warehouses Increase in computing power Better modeling approaches Changes in Competition: Evolution of strategies: Mass marketing vs. One-to-One marketing Increased competition Fast-paced environment Emergence of niche players Changes in Customer Behavior: Better informed More demanding Increased willingness to switch to competitors Evolution of needs: more complex, harder to satisfy

5 ©SAP AG All rights reserved. / Page 5 Definition Data mining is the process of discovering meaningful new correlations, patterns and trends by "mining" large amounts of stored data using pattern recognition technologies, as well as statistical and mathematical techniques. (Ashby, Simms (1998))

6 ©SAP AG All rights reserved. / Page 6 Data Mining Examples Market Based Analysis and Up- Selling/Cross- Selling Pharmaceutical Industry: Drug Effectiveness by Patient Type Defect Analysis in Manufacturing University and Employee Recruitment Employee Turnover Predictions Credit Risk Determination Credit Card Fraud Customer Grouping and Behaviour Prediction

7 ©SAP AG All rights reserved. / Page 7 Introduction to Data Mining Data Mining Process Data Mining Methods Data Mining Case Studies Resources SAP Business Intelligence Module 6

8 ©SAP AG All rights reserved. / Page 8 CRISP DM: Overview

9 ©SAP AG All rights reserved. / Page 9 K nowledge D iscovery in D atabases (KDD) Knowledge Discovery in Data is the non-trivial process of identifying –valid - novel -potentially useful -and ultimately understandable patterns in data. Advances in Knowledge Discovery and Data Mining, Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy, (Chapter 1), AAAI/MIT Press 1999

10 ©SAP AG All rights reserved. / Page 10 Introduction to Data Mining Data Mining Process Data Mining Methods Data Mining Case Studies Resources SAP Business Intelligence Module 6

11 ©SAP AG All rights reserved. / Page 11 Data Mining Models – Predictive Supervised Learning

12 ©SAP AG All rights reserved. / Page 12 Data Mining Models – Explorative Unsupervised Learning

13 ©SAP AG All rights reserved. / Page 13 CustomerIncomeAgeCredit RatingEtc.Buying Behavior Customers - Historical Data (query) Mick Jones$ Excellent…Yes Elton Brown$ Fair…No Jack Turner$ Excellent…Yes Etc.…………… How will other Customers behave? New Data (query) Willie Nelson$ Fair… Carol Lee$ Excellent… Etc.………… Identify the factors driving customer behavior and predict future behavior ? ? ? Predictive: Decision Tree* *Ayati: This example shows the common features of Decision Tree and Decision Table, w hich is the underlying principle of Expert Systems

14 ©SAP AG All rights reserved. / Page 14 Model process: A record in the query starts at the root node A test (in the model) determines which node the record should go to next All records end up in a leaf node Interpreting the Results Read the tree from top to bottom Rule: If Age is less than 35 and Income is greater than $5000 and Credit standing is Excellent, then the customer has a 35% chance of buying the product Age, then Income and credit rating, are the most influential attributes determining buying behavior. Age Income Buy 100% Won’t Buy 100% Credit Rating Will Buy 35% Won’t Buy 65% Leaf Nodes Root Node Decision Node <35>= 35 >$5000<=$5000 Fair Excellent Test Predictive: Decision Tree

15 ©SAP AG All rights reserved. / Page 15 A tree showing survival of passengers on the Titanic ("sibsp" is the number of spouses or siblings aboard). The figures under the leaves show the probability of survival and the percentage of observations in the leaf.Titanic Source: Wikipedia.org

16 ©SAP AG All rights reserved. / Page 16 Source: Wikipedia.org

17 ©SAP AG All rights reserved. / Page 17 Decision Tree: Practical Applications How can we reduce customer fraud? Analyze customer characteristics: Fraudulent behavior (Y or N), age, education, occupation, frequency of purchase, dollar value of purchase, etc. Who is likely to “churn” (stop buying from us)? Analyze customer characteristics; who is: (1) still with us, and (2) no longer “on board”, Plus other demographic or transactional attributes... Who is likely to be a credit risk? Analyze customer characteristics: who has: (1) not been a credit risk in the past, and (2) who has been a credit risk in the past Include relevant customer characteristics

18 ©SAP AG All rights reserved. / Page 18 Weighted Score Tables Customer groups) AgePoints (Age) Income Points (Income) Region Points (Region) Weight30%50%20% 110 – South5 220 – West3 330 – East7 Calculated score for Customer 2: = (10 x 30%)+ (5 x 50%) + (3 x 20%) = 6.1 Use weighted scoring to rank customers according to the importance of certain attributes.

19 ©SAP AG All rights reserved. / Page 19 Predictive: Regression Linear Regression Nonlinear Regression Use regression to predict the impact of one (or more) on another. Example: impact of price reduction on sales in Regions NY, PA and TX. Example: Impact of age, income, HH size, region, length of subscription on canceling a subscription

20 ©SAP AG All rights reserved. / Page 20 Informative: Clustering Clustering is a data mining technique that creates groups of records that are: Similar to each other within a particular group Very different across different groups The degree of association between members is measured by all the characteristics specified in the analysis Clustering helps the user explore vast amounts of data and organize it in a systematic way

21 ©SAP AG All rights reserved. / Page 21 Income Age High Low High Informative: Clustering

22 ©SAP AG All rights reserved. / Page 22 Informative: Clustering Process

23 ©SAP AG All rights reserved. / Page 23 Informative: Association Analysis Association Analysis uncovers the hidden patterns, correlations or casual structures among a set of items or objects. It is typically used for Market Basket Analysis (MBA). It allows the user to: Understand and quantify the relationship between different items (e.g. products, clickstream, etc...) Group different items by affinity Create readily-understandable rules describing.... Organize web pages in order to optimize user accessibility

24 ©SAP AG All rights reserved. / Page 24 Association Analysis Data Mining Cross-Selling Rules C D D A B E E E A Customers Products B C D What products / services are typically bought together? Export rules to Web Shop Use in merchandising Informative: Association Analysis - Example

25 Amazon using Association Analysis

26 ©SAP AG All rights reserved. / Page 26 Informative: Association Analysis - Measures

27 ©SAP AG All rights reserved. / Page 27 Informative: ABC Classification Use ABC to classify objects (such as customers, employees, vendors or products) based on a particular measure (such as revenue or profit). Examples: Customers with revenue >$100M = Class “A”, etc Customers who generate top 20% of our revenue = Class “A”, etc Rank customers by their revenue: The top 20% on the list = Class “A”, etc OR The first 50 customers = Class “A”, etc Practical applications Classify customers into Platinum, Gold, Silver Rank vendors based on product quality (returned goods)

28 ©SAP AG All rights reserved. / Page 28 Informative: ABC Analysis - Example

29 ©SAP AG All rights reserved. / Page 29 Introduction to Data Mining Data Mining Process Data Mining Methods Data Mining Case Studies Resources SAP Business Intelligence Module 6

30 ©SAP AG All rights reserved. / Page 30 Data Mining: Terrorism Five Were Active FBI Terrorist Investigations Including Hijacker: Marwin Youseff Alsherri Delivered List to Authorities Prior to Names Being Made Public Within 16 Hours Seisint Delivered 419 Names of Interest On September 14, 2001 Seisint’s Artificial Intelligence Billions Of Public Records FAA Public Record Information Seisint’s Data Supercomputer +++

31 ©SAP AG All rights reserved. / Page 31 Data Mining: Examples Banking Lloyds TSB Saved $35 million by reducing credit card fraud HSBC 4x more leads, 37% more asset potential Bank Financial 7x increase in response rates, 80% reduction in costs Insurance Aegon Generated $30M additional revenue in service call center FBTO Decreased direct mailing costs by 35%, increased conversion rates by 40%, increased profit by 29% Telecommunications Verizon Wireless Cut churn by 20%, saved 33% of “at-risk” clients and reduced marketing costs by 60% Telstra Increased sales in call centers by 120% Other industries Experian Generated $2.5 million in catalog revenue while reducing hardware and software maintenance costs by 80% Center Parcs Added $3 million to their bottom line Reduced mail costs by 46% Sofmap.com (retail) Tripled profitability of online store De Telegraaf (media) Reduced acquisition cost per subscription by 90%

32 ©SAP AG All rights reserved. / Page 32 Introduction to Data Mining Data Mining Process Data Mining Methods Data Mining Case Studies Resources SAP Business Intelligence Module 6

33 ©SAP AG All rights reserved. / Page 33 Data Mining: Resources Data Mining Resources Blog Data The Data Warehousing Institute

34 ©SAP AG All rights reserved. / Page 34 SAP Resources  SAP University Alliances community  Collaboration workspace from SAP https://cw.sdn.sap.com/cw/index.jspahttps://cw.sdn.sap.com/cw/index.jspa  Business Intelligence workspace: content and discussions https://cw.sdn.sap.com/cw/community/uac/bi https://cw.sdn.sap.com/cw/community/uac/bi  SAP BusinessObjects Community  University of Arkansas, Walton College Enterprise Systems  University of Southern California, Viterbi School of Engineering, Information Technology Program/SAP Program

35 Contact Christine Davis Nitin Kale University of Southern California 3650 McClintock Ave, OHE 412 Los Angeles, CA T: +01 (213) 740 – 7083 F: +01 (213) 740 – 1051

36 ©SAP AG All rights reserved. / Page 36 Thank you!

37 ©SAP AG All rights reserved. / Page 37 Copyright 2010 SAP AG All Rights Reserved No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG. The information contained herein may be changed without prior notice. Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors. Microsoft, Windows, Excel, Outlook, and PowerPoint are registered trademarks of Microsoft Corporation. IBM, DB2, DB2 Universal Database, System i, System i5, System p, System p5, System x, System z, System z10, System z9, z10, z9, iSeries, pSeries, xSeries, zSeries, eServer, z/VM, z/OS, i5/OS, S/390, OS/390, OS/400, AS/400, S/390 Parallel Enterprise Server, PowerVM, Power Architecture, POWER6+, POWER6, POWER5+, POWER5, POWER, OpenPower, PowerPC, BatchPipes, BladeCenter, System Storage, GPFS, HACMP, RETAIN, DB2 Connect, RACF, Redbooks, OS/2, Parallel Sysplex, MVS/ESA, AIX, Intelligent Miner, WebSphere, Netfinity, Tivoli and Informix are trademarks or registered trademarks of IBM Corporation. Linux is the registered trademark of Linus Torvalds in the U.S. and other countries. Adobe, the Adobe logo, Acrobat, PostScript, and Reader are either trademarks or registered trademarks of Adobe Systems Incorporated in the United States and/or other countries. Oracle is a registered trademark of Oracle Corporation. UNIX, X/Open, OSF/1, and Motif are registered trademarks of the Open Group. Citrix, ICA, Program Neighborhood, MetaFrame, WinFrame, VideoFrame, and MultiWin are trademarks or registered trademarks of Citrix Systems, Inc. HTML, XML, XHTML and W3C are trademarks or registered trademarks of W3C®, World Wide Web Consortium, Massachusetts Institute of Technology. Java is a registered trademark of Sun Microsystems, Inc. JavaScript is a registered trademark of Sun Microsystems, Inc., used under license for technology invented and implemented by Netscape. SAP, R/3, SAP NetWeaver, Duet, PartnerEdge, ByDesign, SAP Business ByDesign, and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and other countries. Business Objects and the Business Objects logo, BusinessObjects, Crystal Reports, Crystal Decisions, Web Intelligence, Xcelsius, and other Business Objects products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of Business Objects S.A. in the United States and in other countries. Business Objects is an SAP company. All other product and service names mentioned are the trademarks of their respective companies. Data contained in this document serves informational purposes only. National product specifications may vary. These materials are subject to change without notice. These materials are provided by SAP AG and its affiliated companies ("SAP Group") for informational purposes only, without representation or warranty of any kind, and SAP Group shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP Group products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warrant.

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