Predictive Analytics Demystified

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Helping Companies Learn From the Past, Manage the
Present and Shape the Future
www.senturus.com
Predictive Analytics Demystified

2
This slide deck is part of a recorded webinar.
To view the FREE recording of this entire
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The Senturus library has over 90 other recorded webinars,
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which may interest you.
Go to the recorded resources

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Agenda
• Introduction
• What are Predictive Models?
• Why Build Predictive Models?
• Comprehensive Analytics Solution
• Reaping the Benefits of Models
• Live Demonstration: IBM SPSS Modeler
• Questions and Answers

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Presenters Today
Greg Herrera
Co-Founder/CEO
Senturus
Arik Killion
Client Technical Professional
IBM Business Analytics, SPSS
Eric Zankman
Analytics and BI Consultant
Senturus, Inc
20-year track record of
• applying data mining
• predictive modeling
• customer segmentation,
• experimental design and
optimization

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Who is Senturus ?
• Consulting firm specializing in Corporate Performance
Management
– Business Intelligence
– Predictive Analytics
– San Francisco Business Times Hall of Fame -- Four
consecutive years in Fast 100 list of fastest-growing
private companies in the Bay Area
• Experience
– 13-year focus on performance management
– More than 1,200 projects for 650+ clients
• People
– Business depth combined with technical expertise.
Former CFOs, CIOs, Controllers, Directors...
– DBAs with MBAs
www.Senturus.com 888.601.6010 info@senturus.com

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What are Predictive Models?
Empirically-derived algorithms used to predict future outcomes
In the context of customer analytics, a model:
• predicts future customer actions
• combines individual attributes that are strong predictors
• produces an assessment score for each customer

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Uses of Predictive Models
• Direct Marketing
• Underwriting
• Usage Stimulation
• Cross-sell / Up-sell
• Retention / Churn
• Customer Value
Acquire
Grow
Retain
Predictive
Customer Analytics
Predictive
Threat & Fraud Analytics
Monitor
Detect
Control
Predictive
Operational Analytics
Manage
Maintain
Maximize
• Risk Management
• Credit Policy
Decisions
• Channel Preference
• Portfolio
Management
• Fraud Detection
• Collections
• Write-offs /
Recoveries

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Predictive Modeling Timeline:
Data snapshots from multiple points-in-time used to simulate forecast
Forecast
Observation Period Performance Period
Observation
Point
Future Customer BehaviorPast Customer Behavior

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Model Objective Function
Specifies the Customer Behavior to be Predicted
• Who is Included in the Customer Sample?
– Define all Inclusions and Exclusions
• How is Customer Performance Defined?
– Define “desirable” customers
– Define “undesirable” customers
– Define “indeterminate” customers
• How Long is the Performance Period?
– Define the Observation and Performance Dates

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Example Objective Function for Churn
Customer Performance
(Pertains only performance period)
– Good: Non-churn
– Bad: Voluntary Churn
– Indeterminate: Involuntary Churn
(customer terminated for non-payment)
Forecast
Observation Period =
prior to 1/1/2013
Performance Period =
1/1/2013 to 4/30/2013
Observation
Point =
1/1/2013
Future Customer BehaviorPast Customer Behavior
Customer Sample
– Include current customers
as of 1/1/2013
– Exclude customers with no
purchase/payment activity
in the 24 months prior to 1/1/2013

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Example Predictive Model for Churn
Account Age Score
Less than 1 year 41
1 to less than 2 years 58
3 to less than 5 years 97
5 years or more 102
Average Balance
in the Last 6 Months
$0 - $75 80
$75.01 - $120 49
$120.01 or more 41
Roaming Charges
$0 - $20 80
$20.01 - $40 68
$40.01 - $75 40
$75.01 or more 29
Dropped Calls
in the Last Month
0 110
1 57
2 41
3 or more 30
Number of Score
Premium Services
0 35
1 - 2 57
3 or more 69
Number of Customer Service
Calls in the Last 3 Months
0 85
1 - 2 38
3 - 4 29
5 or more 18
Number of Customer Disputes
0 132
1 - 2 97
3 or more 40
Total Score
for example customer
= 421

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Why Build Predictive Models?
Predictive models harness the knowledge
within your data about customer behavior
so you can treat different customers
differently, tailoring the right treatments
and offers to the right customers, thereby
improving customer strategies and
increasing profitability

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Evaluating Model Performance
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
ACTUALCustomerBadRateatTIME1
FORECASTED Customer Score (in deciles) at TIME 0
• Customers were scored at TIME 0 and
rank-ordered by Customer Score
• Bars show actual bad rate at TIME 1
Low Score High Score

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Translating Insights into Action
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
ACTUALCustomerBadRateatTIME1
FORECASTED Customer Score (in deciles) at TIME 0
Treat
different
customers
differently
Low Score High Score

16
The Senturus library has over 90 other recorded
webinars, whitepapers, and demonstrations on assorted Cognos and
BI topics which may interest you.

17
Predictive Models: Part of a Comprehensive
Analytics SolutionCustomer
Data Mart
Customer
Segmentation
Champion/Challenger
Strategy Tests
Model, Segmentation, and
Strategy Execution
New Champion
Strategy
Strategy Test
Evaluation
Predictive
Models
Continuous
Learning and
Improvement

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Solution – Customer Data Mart
Store the right data for predictive modeling and analysis
Customer-level Data
Summary Billing
Data
Detailed Transaction
Data
Predictive Model
Development
Strategy Test
Evaluation
Ongoing Analysis
and Model Validation
Custom
er Data
Mart

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Solution – Predictive Models
Apply Modeling Methodology
• Define business goals
• Specify model objective function
• Design/build modeling database
• Partition modeling data
• Derive potential predictors
• Analyze predictor strength
• Perform sub-population analysis
• Build model algorithms
• Evaluate model performance
• Deploy model

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Solution – Customer Segmentation
Create customer groups to enable differentiated strategies
Apply model scores and other criteria to segment customers
All Customers

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Solution – Champion/Challenger Tests
Employ test-and-learn methodologies to evolve strategies
• Develop a “champion”
strategy and “challenger”
strategies for each segment
• Execute strategy tests and
analyze results after a
defined test period
• Perform model validation
(controlling for treatment)
• Deploy new champion
strategy with quantifiable
business improvements
• Create new round of
promising challengers
Model Score Low Medium High
Champion
(85%)
Treatment A1 Treatment B1 Treatment C1
Challenger 1
(5%)
Treatment A2 Treatment A1 Treatment C2
Challenger 2
(5%)
Treatment B1 Treatment C1 Treatment B1
Challenger 3
(5%)
Treatment B2 Treatment B2 Treatment B2

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Solution – Test and Learn Feedback Loop
Establish culture and capabilities for continuous strategy improvement

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Reaping the Benefits: Churn Management
Customer
Segmentation
Champion/Challenger
Strategy Tests
Model, Segmentation
and Strategy Execution
Strategy Test
Evaluation
New Champion
Strategy
Value and Churn
Predictive Models
Customer
Data
Mart
Continuous
Learningand
Improvement
Solution
 Develop predictive models for customer value and churn
 Identify customers with high value and/or high churn propensity for tailored treatments
(e.g., special retention campaigns, VIP service, liberal fee-reversal policies)
 Conduct champion/challenger tests to identify the best treatments for each segment
 Implement new champion and develop next set of challengers
Performance Measures
 Customer value
 Churn rates
 Average Revenue per User
(ARPU)
 Customer tenure
 Average number of products
 Retention costs
Challenges
 Rising churn rates
 Declining revenue per customer
 High costs to acquire new
customers

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Predictive Analytics - Summary
• Predictive analytics can greatly improve profitability
when part of a comprehensive solution
• A well-designed data mart is the first step toward
effective predictive analytics
• Organizations must be committed to ongoing strategy
testing to maximize their benefits

25
The Senturus library has over 90 other recorded webinars,
whitepapers, and demonstrations on assorted Cognos and BI topics
which may interest you.

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DEMONSTRATION: IBM SPSS MODELER

0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
Attrition Rate

• Through gut instinct and hypothesis – this analyst concludes
subscribers that are Single and pay by Credit Card have the
largest attrition
• Another analyst might arrive at a completely different conclusion

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Other Resources
In-Person IBM/SPSS Events
IBM Business Analytics Summit June 13, 2013 San Francisco
• complimentary, one-day event
• Emphasis on SPSS with many breakout sessions focused on predictive analytics
• Click below to go to IBM's website and learn more about the event and register:
http://www.senturus.com/continue.php?link=aHR0cHM6Ly93d3cuaWJtLmNvbS9ldmVudHMvd3dlL2dycC9ncnA
wMDQubnNmL2FnZW5kYT9vcGVuZm9ybSZzZW1pbmFyPUIyRktSM0VTJmxvY2FsZT1lbl9VUyZTX1RBQ1Q9QlBfU
2VudHVydXM=
SPSS Modeling Workshops
• San Francisco, CA June 18
• Costa Mesa, CA June 19
• Minneapolis, MN June 20
Dates being finalized for workshops in July – December throughout the U.S. and Canada
Email to follow with details.

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Other Resources: IBM Analytic Answers
• Prepackaged solutions
• Cloud based
Analytic Answers for Student Retention
http://public.dhe.ibm.com/common/ssi/ecm/en/ytd03292usen/YTD03292USEN.PDF
Analytic Answers for Prioritized Collections
Analytic Answers for Retail Purchase Analysis and Offer Targeting
Analytic Answers for Insurance Renewals

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The Senturus library has over 90 other recorded
webinars, whitepapers, and demonstrations on assorted Cognos and
BI topics which may interest you.

36
Helping Companies Learn From the Past, Manage the
Present and Shape the Futurewww.senturus.com
888-601-6010
info@senturus.com
Copyright 2013 by Senturus, Inc. This entire presentation is
copyrighted and may not be reused or distributed without the written consent of
Senturus, Inc.

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