IBM Business Analytics and Optimization - Introduktion till Prediktiv Analys

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Är din organisation prediktiv? Genom att arbeta förutseende med styrning och beslutsfattande kan ett Predictive Enterprise – ett förutseende företag – minimera risker och uppnå högt ställda affärsmål. …

Är din organisation prediktiv? Genom att arbeta förutseende med styrning och beslutsfattande kan ett Predictive Enterprise – ett förutseende företag – minimera risker och uppnå högt ställda affärsmål. Under sessionen lär du dig vad prediktiv analys är, vad det ger för mervärde, vad olika kunder har använt det till och vilken ROI det gett dem samt
hur du kan använda det med din existerande infrastruktur.

Talare: Mats Stellwall, Predictive Analytics Specialist, IBM

Denna presentation hölls på ett seminariepass för Business Analytics and Optimization under IBM Software Day 2010.

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  • 1. Introduction to Predictive Analytics Mats Stellwall – Predictive Analytics Specialist
  • 2. How Decision-Making is Changing “We are in a historic moment of horse-versus-locomotive competition, where intuitive and experiential expertise is losing out time and time again to number crunching.” Ian Ayres, author of “Super Crunchers” Quality and value of decisions Predictive Decision-Making • Accurate predictions based on historic Automated Decision-Making patterns Decisions from “Intuition” • Knowledge, policies and practices • Leverage all available data • “Instinct” embodied in business rules • Flexible, evidence-based decisions • “Hunches” • Decisions made efficiently and • Robust in volatile environments – models re- consistently generated from latest data to reflects changing • Based on experience • Objective fashions, trends, etc.
  • 3. Imagine If Your Decision Makers Could… …predict and treat …adjust credit lines …determine who is …apply inferred social infection in premature as transactions are most likely to buy if relationships of newborns 24 hours occurring to account offered discounts at customers to prevent earlier? for risk fluctuations? time of sale? churn? Retail Sales Telco Call Physician Loan Officer Associate Center Rep …optimize every transaction, process and decision at the point of impact, based on the current situation, without requiring that everyone be an analytical expert
  • 4. 4 What is Predictive Analytics? Predictive Analytics helps connect data to effective action by drawing reliable conclusions about current conditions and future events. — Gareth Herschel, Research Director, Gartner Group
  • 5. The Predictive Advantage Predict & Transformational Deployment of Predictive Models: Act M • Leverage current data to drive better decisions • Make robust predictions on current and future cases • Embed predictive models into points of interaction “NOW” Insight Driven Predictive Analytics: • Algorithms automatically discover significant patterns • “Learn” from historical data – create predictive models • Valuable insight into behaviour improves strategic and operational decision making “NOW” KPI KPI Traditional BI and Conventional Analysis: • KPIs and metrics provide insight Sense & KPI •Aggregate data up to and including current point in time • Self guided exploration of data Respond “NOW”
  • 6. Data is the heart of Predictive Analytics High-value, dynamic - source of competitive differentiation Interaction data Attitudinal/External data - E-Mail / chat transcripts - Opinions - Call center notes - Preferences - Web Click-streams - Needs & Desires - In person dialogues - Weather Conditions - Maintenance History -… - Repairs performed -… Customers Events Spare Parts … Descriptive data Behavioral data - Attributes - Orders - Characteristics - Transactions - Self-declared info - Payment history - (Geo)demographics - Usage history -… - Machine readings - Alarms -- … “Traditional”
  • 7. IBM SPSS Driving Predictive Analytics
  • 8. Some have started the journey… Advanced Auto Cablecom Significant cost reduction in supply chain Reducing churn • Provide the right (!) amount of goods in a store • 100% in churn prediction and initial reduction in or a storage location churn rate from 19% to 2% • Identify where to open a new store • Conversion of 53% of unhappy customers into “Promoters” Richmond Police Department Infinity Property and Casualty Corporation Crime prediction and proactive deployment Fighting fraud • 20-30 % Reduction in capital crimes within the • Identification of suspicious cases within 24h first year instead of 14 days • Identification of “Hotspots” and allocation of troopers according to needs 8
  • 9. Example 1: Optimize Marketing Campaigns Campaign History • Contact Data • Response/Decline • Test/Control Group Interaction Data • Call Center • Website Visit Data • Service Request Analyses Scoring Marketing Predict who is likely to Apply campaign Customer Data respond, based on each model to process • Demographics customer’s profile new Use models to customers identify who • Account Activity should receive • Product Holdings what offer • Survey Data Results: - Lower mailing costs, higher response, more profit - Better cross-sell/up-sell rates
  • 10. Example 2: Understanding Product Mix (Retail) In-store promotion decisions Association Point of Sale Transaction detection Data “Blanket” marketing Demographics @ Customer Analysis Interactions Segments Targeted marketing Profiles Scoring models ... Attitudes Results: - Data-driven promotions and in-store brochures lead to more sales - Targeted marketing reduces mailing costs and improves response rates
  • 11. Example 3: Proactive Customer Retention Customer data: Demographics Targeted … retention offers Transaction & through (e) billing data: mail Calls, SMS, MMS, mobile internet, … Targeted In- store Interaction data: promotions Website usage, call center interactions Analyses Scoring … Look at Retentio Apply Attitudinal data: customers who n offers have churned model to Satisfaction; new in the Segments customers call Net promoter score, Profiles center … Scoring models ... Results: - Reduction in churn due to proactive reach-out - Maintain market share - Proactive issue identification
  • 12. How does it fit?
  • 13. Everyone has data
  • 14. IBM SPSS Predictive Software links data into intelligence We b Call Ce nte r Association IBM SPSS Classification Predictive Software Re porting & Analys is Segmentation
  • 15. Added business value to Business Intelligence Top-Down Bottom-Up Query Data Mining Search (OLAP, BI) Text Mining Measurement (historical) Prediction (future) Business value Which cities Integrated were they Analytical located in? How many Solutions Data subscribers mining did we lose? What should OLAP we offer this customer Which customer today? Query & types are at risk Reporting and why? Facts Segments & Trends Predictions 15
  • 16. IBM SPSS Modeler and IBM Cognos 8 BI Access to Cognos BI Streamline process to data inside IBM SPSS distribute results Modeler Common Business Model Automatically publish Leverage investment in Cognos BI predictive results to BI data modeling and package access of Cognos BI Enabling Data Mining with Business Intelligence
  • 17. IBM SPSS Modeler and IBM InfoSphere Warehouse Bring together the best-in-class business analytics capabilities of IBM InfoSphere Warehouse and IBM SPSS Modeler software, and experience IBM SPSS Modeler adds business-oriented predictive modeling and model management InfoSphere Warehouse PMML IBM SPSS Modeler SQL Server Tables 17
  • 18. Questions? Mats Stellwall Predictive Analytics Specialist E-Mail mats.stellwall@se.ibm.com Mobile: +46 70 793 51 66