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Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
Predictive analytics km chicago
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Predictive analytics km chicago

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http://kmchicago.blogspot.com/2010/05/may-11-meeting-using-predictive_06.html

http://kmchicago.blogspot.com/2010/05/may-11-meeting-using-predictive_06.html

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  • 1. Jing Shyr Chief Statistician, SPSS Predictive Analytics Product Development Predictive Analytics :Overview Business Analytics 1 © 2010 IBM Corporation
  • 2. 2 Business Analytics Introducing SPSS, an IBM Company  A leading provider of predictive analytic software, services and solutions – Software – data collection, text and data mining, advanced statistical analysis and deployment technologies – Services – implementation, training, consulting, and customization – Solutions – combine software and services to deliver high-value line- of-business solutions; used for optimizing marketing campaigns, call center effectiveness, identification of fraudulent activity and more  40 years of experience and a broad customer base – 250,000 customers: 100 countries, 50 states, 100% of top universities Enables decision makers to predict future events and proactively act upon that insight to drive better business outcomes © 2010 IBM Corporation
  • 3. 3 Business Analytics Vision –The world becomes smarter… Copyright © DreamWorks SKG. 2002. © 2010 IBM Corporation
  • 4. Business Analytics The Predictive Analytics Process Predict Analyze data to Recommend provide insight and the most predict the future appropriate action to take Predictive Analytics Understand Improve customer retention Act Customers Constituents Grow share of wallet Prospects Employees Store new data Minimize risk on customers, Increase customer satisfaction Students Patients events, etc. for  Enhance market share continuous improvement People Data Decision & Enterprise Data Sources Optimization © 2010 IBM Corporation
  • 5. 5 C o p y Business Analytics r i g h t Predictive analytics … Derives maximum value from its data assets 2 0 0 3 - 5 , S P S S Understands its business by gaining deep insight I n c . Leverages advanced analytics to predict outcomes Close the “Knowledge Gap” Turns this knowledge into action Optimize decision making across all operations Close the “Execution Gap” © 2010 IBM Corporation
  • 6. 7 C o p y Business Analytics r i g h t How PA relates to statistics and data mining? 2 0 0 3  PA uses statistics and data mining to Understand and Predict - 5 ,  Both of them examine and prepare data, apply or try different algorithms for S better prediction P • Stat: Regression, ANOVA, MANOVA, Logistic regression, Discriminant, S S I n Factor, K-mean Cluster, Hierarchical Cluster, generalized linear model, Arima, … c . • Data mining: Neural Network including MLP (Multi-Layer Perceptron), RBF (Radial Basis function), Kohonen, Bayesian network, Naïve Bayes, Association, sequence … • Other: Support vector machine (SVM), Decision Tree, projection pursuit regression (PPR), nonnegative factorization… © 2010 IBM Corporation
  • 7. 8 Business Analytics Problems lead to decisions Predictive Analytics Driving Decisions  Customs & Border Protection – Problem: I can’t search every car that crosses the border. – Decision: Which car should I search?  Infinity – Problem: I can’t investigate every claim for fraud. – Decision: Should I investigate this claim?  Cablecom – Problem: I can’t save every customer. – Decision: Is it worth trying to save this customer? © 2010 IBM Corporation
  • 8. Business Analytics Healthcare & Insurance Claims Management What if you could predict fraud before it happened? What if you could recommend preventative care to those who most need it? What if you could process low risk claims faster and with less headache? What if you could plot the expected course of treatments for veterans? What if you could react differently in times of crisis? © 2010 IBM Corporation
  • 9. 1 0 Business Analytics Two special data sources  Text : unstructured data  Capture customer issues/measure preferences expressed in survey text, call center notes, and Web data  Social Network data Call Detail Record (CDR): A CDR contains all the details pertaining to a call such as the time, duration, origin, destination, etc. E-mail, Facebook, … © 2010 IBM Corporation
  • 10. Business Analytics Text Analytics Overview  What does Text Analytics deliver? –Breadth: Take into account qualitative input from all sources –Clarity: Understand related facts, opinions, and what to do about it –Speed: Rapid understanding of qualitative feedback  What does Text Analytics do for people? –Extracts and classifies unstructured data in multiple languages –Discovers patterns in events and opinions and categorizes them –Models customer behavior based on qualitative insights  How does Text Analytics do it? –Natural Language Processing –Sentiment and Event Analysis © 2010 IBM Corporation
  • 11. Business Analytics Horizontal Solution Architecture: Text Analytics Dashboard or Presentation Tool File RDBS System Data Concept Sentiment Concept Record Category Access Extraction Analysis Classification Scoring Deployment Analytical Social Application Media or Tool Dashboard or Presentation Tool RDBS Data is Data is Opinions Using either Using the Categorized accessed indexed, (positive manual or classification records or for tokenized, and automated definitions, documents analysis. normalized. negative) means, records or are ready for Sometimes Concepts are concepts are documents further data might and text associated classified. are scored. analysis or be link with Classification for various translated patterns persons, can be done reporting after being are places, and on a per options. imported. generated. things. record basis or on a concept basis. 12 © 2010 IBM Corporation
  • 12. 1 3 Business Analytics IBM SPSS Text Analytics  Uses natural language processing heuristic rules and statistical techniques to reveal conceptual meaning in text  Extracts concepts from text and categorizes them  Makes unstructured qualitative data more quantifiable, enabling the discovery of key insights from sources such as survey responses, documents, emails, call center notes, web pages, blogs, forums and more Brings repeatability to ongoing decision making © 2010 IBM Corporation
  • 13. Business Analytics Trust Network Described as A Circle Graph © 2010 IBM Corporation
  • 14. Business Analytics Traditional Applications SNA applies to a wide range of business problems, including:  Knowledge Management and Collaboration. SNAs can help locate expertise, seed new communities of practice, develop cross-functional knowledge-sharing, and improve strategic decision-making across leadership teams.  Team-building. SNAs can contribute to the creation of innovative teams and facilitate post- merger integration. SNAs can reveal, for example, which individuals are most likely to be exposed to new ideas.  Human Resources. SNAs can identify and monitor the effects of workforce diversity, on- boarding and retention, and leadership development. For instance, an SNA can reveal whether or not mentors are creating relationships between mentees and other employees.  Sales and Marketing. SNAs can help track the adoption of new products, technologies, and ideas. They can also suggest communication strategies.  Strategy. SNAs can support industry ecosystem analysis as well as partnerships and alliances. They can pinpoint which firms are linked to critical industry players and which are not. © 2010 IBM Corporation
  • 15. Business Analytics What are the results SNA produce?  Identify groups (communities)  Identify leaders or influencers  Execute viral marketing strategies  Identify product Up-selling and Cross-selling opportunities  Manage contagious churn  Identify subscriber acquisition and retention opportunities © 2010 IBM Corporation
  • 16. 1 7 Business Analytics Act: IBM SPSS Decision Management  Framework for domain specific applications that combine Models, Rules, and Optimization to solve business problems  Extends predictive insights to the business user at the point of decision – E.g. Should a claim be ‘fast tracked’ or evaluated more closely based on a calculated risk score? Automating high volume, high value decisions © 2010 IBM Corporation
  • 17. 1 8 Business Analytics Repeatable Approach : 7 Steps to Analytical Decision Making 1. Connect to Data 2. Define Global Selections Identify who or what is to be included as well explicitly excluded from the decision making process 3. Define Desired Outcomes Define the set of potential decisions that can be made (what campaigns are available, which types of investigation can be performed etc) 4. Define Operational Decisions with Rules & Models Define and use rules and/or predictive models that dictate or help decide on the appropriate outcomes 5. Optimize Outcomes Specify how the rules and models should be combined to make the most optimal decision 6. Deploy the solution in batch or for real time decisioning 7. Report Monitor the decisions that have been deployed through reporting Best practices approach to decision making based on our experience in the marketplace © 2010 IBM Corporation
  • 18. 1 9 Business Analytics Configurability Configurable in the field to new business problems  Enable services / partners to deploy decisioning services to a wide range of business problems  Terminology is configurable to different applications – Customer Interactions, Claims, Risk, Churn, Underwriting, Claims, Subrogation etc.  Configurable around the 7 steps – Which steps are required? – Various options for working with and combining rules / models and for optimizing the decision returned © 2010 IBM Corporation
  • 19. Business Analytics Demo Business Problem – Claims Management  A large insurance company wants to manage claims more effectively –Reduce the time needed to process a typical claim. –Reduce the amount paid to fraudulent claims  The Claims Management Application processes incoming claims in real time, and recommends the best action for each claim © 2010 IBM Corporation
  • 20. Business Analytics Step 1 & 2: Define Decision Scope… (Sample Illustration: Insurance) The decision process begins with leveraging enterprise data and identifying the focus of the operational decision. excluded. The Insurance Company elects to exclude data related to natural phenomenon's. Application: “I don’t want to worry about Claims associated with Katrina” © 2010 IBM Corporation
  • 21. Business Analytics Step 3: Defining Desired Outcomes… (Sample Illustration: Insurance) Typically with all decisions there is a finite set of desired outcomes that can be achieved. This structure can be multidimensional The Insurance Company identifies three possible outcomes to the decision. Application: “There are three things we could do: Fast track, Standard process, Investigate” © 2010 IBM Corporation
  • 22. Business Analytics Steps 4: Define Operational Decisions… (Sample Illustration: Insurance) Business people define rules that embody their priorities and experiences. Business People leverage existing predictive models – or create new ones, to support the business problem. Both are critical to optimize outcomes! Application: “I know that claims for active servicemen go through a serious evaluation before submittal, so even if the profile is high risk, we can still process it.” © 2010 IBM Corporation
  • 23. Business Analytics Step 5: Optimize Outcomes using Matrix… (Sample Illustration: Insurance) The decision outcome is optimized and balanced between the predictive components that provide real time insight and the rules that govern the policy and practices of the company. Business people run multiple simulations and identify the best approach Application: “I wonder what would happen if we evaluated all the claims for fraud? Hmmm the allocation would overwhelm the department” © 2010 IBM Corporation
  • 24. Business Analytics Step 5: Optimize Outcomes using Formula Approach The decision outcome can also be determined by configuring formulas which will automatically determine the right action as projected by rules and models It’s all controlled by the business Application: “Recommend preventative care if the risk profile is high” © 2010 IBM Corporation
  • 25. Business Analytics Step 6: Deploy Decision Pattern to Enterprise  Single button deploy alerts IT that it’s time to move the solution into production –Point of Interaction Systems can drive best practices for every real time decision. –Automation service can update data records to reflect operational policy decisions –Model Management capabilities allow ongoing monitoring / improvement of the models in production © 2010 IBM Corporation
  • 26. Business Analytics Step 7: Report on outcomes – and Learn!  The Report tab allows you to monitor the status of deployed applications  The business can check up on results, and adjust how things are handled – starting the process over…….. Application: “How did our new policy impact total claim costs?” © 2010 IBM Corporation
  • 27. 2 8 Business Analytics Summary: Enabling the Business User Optimizing Operational Decisions for Better Results  Web based business user interface configurable in the field to new business problems  Built on Convergence! • Data Mining • Business Intelligence • Business Rules • Event Processing • Data Management © 2010 IBM Corporation
  • 28. Business Analytics © Copyright IBM Corporation 2010 All rights reserved. The information contained in these materials is provided for informational purposes only, and is provided AS IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, these materials. Nothing contained in these materials is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in these materials to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in these materials may change at any time at IBM’s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. IBM, the IBM logo, Cognos, the Cognos logo, and other IBM products and services are trademarks of the International Business Machines Corporation, in the United States, other countries or both. Other company, product, or service names may be trademarks or service marks of others. © 2010 IBM Corporation

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