This document introduces predictive analytics and how it can improve decision making. It discusses how predictive analytics uses historical data patterns to make accurate predictions about current conditions and future events. This allows decisions to be made based on evidence rather than intuition. Examples are given of how predictive analytics has been used to reduce costs, increase sales and reduce customer churn. The document also outlines how IBM SPSS predictive software links different data sources into intelligence that can be used to target marketing campaigns, optimize product mix decisions and conduct proactive customer retention efforts.

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