A Case Study in Predictive Modeling: How One Firm Achieved Dramatic Results with Analytics

HOW ONE FIRM ACHIEVED DRAMATIC RESULTS WITH ANALYTICS
A CASESTUDYINPREDICTIVEMODELING

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Copyright 2014 Senturus, Inc. All Rights Reserved

•Introductions
•Predictive Modeling Overview
•Case Study
•IBM SPSS Demonstration
•About Senturus
•Next Steps
Today’s Agenda
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Copyright 2014 Senturus, Inc. All Rights Reserved.

Eric Zankman
Predictive Analytics Practice Leader
Senturus
Greg Herrera
President & Co-Founder
Senturus
Introduction: Today’s Presenters
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ArikKillion
SPSS Client Technical Lead
IBM

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

Why Build Predictive Models?
Predictive models harness the knowledge within your dataabout 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

Uses of Predictive Models
•Underwriting
•Direct Marketing
•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
•Credit Policy Decisions
•Risk Management
•Channel Preference
•Predictive Maintenance
•Warranty Claims
•Portfolio Management
•HR Management
•Collections
•Write-offs / Recoveries
•Fraud Detection
•Tax Audits

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Predictive Analytics: Case Study
•Large communications firm has history of using predictive models for accounts receivable and collections
•Company is seeing rising delinquencies and write-offs among its residential and business customersCopyright 2014 Senturus, Inc. All Rights Reserved.

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•Reduce delinquencies and write-offs
•Maintain customer satisfaction and loyalty
•Establish process for ongoing strategy improvement
Case Study: Business Goals

Case Study -Business Challenges
Limited historical customer data for modeling
Strategy effectiveness not evaluated through controlled testing
Organizational process not yet established for ongoing strategy improvement
Strong Model
Weaker Model
Predictive strength of models had declined over time, as expected, due to changes in market conditions and customer portfolios

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Case Study -Solution Summary Customer Data MartCustomerSegmentation
Champion/Challenger
Strategy TestsModel, Segmentation, and Strategy Execution
New Champion Strategy
Strategy Test
Evaluation
Predictive Models
Continuous Learning and Improvement

Solution –Customer Data MartStore the right customer data for analysisCustomerDataMart
Billing
Data
Customer Data
Collections
DataPredictive ModelDevelopmentStrategy TestEvaluation
Ongoing Analysisand Model Validation

Solution –Predictive Risk ModelsForecast customer’s likelihood to become delinquent or write off
Developed 12 risk models to predict delinquency (3 or more months of unpaid bills) or balance write-off:
–Residential and business customers
–Active and closed accounts
–New and mature accounts

Solution –Customer SegmentationCreate customer groups to enable differentiated strategies
Applied model scores and other criteria to segment customers

Solution –Champion/Challenger TestsEmploy test-and-learn methodologies to evolve strategies
•Developed “champion” and “challenger” strategies for each segment
•Executed strategy tests and analyzed results
•Performed model validation
•Deployed new champion strategy with measurable business improvements
•Created new round of promising challengers

20Solution –Organizational Focus on AnalyticsBuild infrastructure to promote test-and-learn activities
•Established a test-and-learn culture to support continuous strategy improvements
•Created an analytics environment for repeatable strategy testing

22Strategy Test Example -Design

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Strategy Test Example -ResultsCopyright 2014 Senturus, Inc. All Rights Reserved

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Strategy Test Example – New Champion and Next Set of Challengers

Cost/Benefit Trade-Off AnalysisAlternative New Champion Strategies
Cost/benefit trade-offs determine desired champion strategy
Active Accounts
Percent Decrease/Increase compared to Champion
Delq. 3+ ($) -12%
Write-off ($) -13%
Coll. cost ($) -7%
Balanced ApproachDelq. 3+ ($) -26% Write-off ($) -15% Coll. cost ($) 12% Delq. 3+ ($) -35% Write-off ($) -5% Coll. cost ($) 30%
Delq. 3+ ($) -17%
Write-off ($) -15%
Coll. cost ($) 0%
Minimizing Write-offs
Maintaining Collections HeadcountMinimizing Delinquencies
Selected StrategyClosed AccountsPercent Decrease/Increase compared to Champion
Delq. 3+ ($) N/A
Write-off ($) -19%
Coll. cost ($) 0%
Maintaining Collections Headcount
Selected StrategyCopyright 2014 Senturus, Inc. All Rights Reserved

Benefits Summary
•Dramatic improvements in profitability
•Improved customer satisfaction and loyalty
•Enhanced portfolio management capabilities
•Organizational and analytical infrastructure for ongoing strategy improvements
•Customer data for Business Intelligence/Analytics and future predictive model development

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Applying Predictive Analytics to other Business Areas

Customer SegmentationChampion/Challenger Strategy Tests
Model, Segmentation, and Strategy ExecutionStrategy TestEvaluation
Value and Churn Predictive Models
Customer
Data
Mart Continuous Learning and 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
Retention/Churn Management

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Cross-sellingCustomer Segmentation
Champion/Challenger Strategy TestsModel, Segmentation, and Strategy Execution
Strategy TestEvaluation
Prop-to-Buy / Channel Predictive ModelsCustomerDataMart
Solution
Develop predictive models to forecast propensity-to-buy and channel preference
Identify customers likely to buy certain products for targeted sales efforts; further segment customers by channel preference to deliver offers/promotions through the most effective channels
Conduct champion/challenger tests to identify the best offers/promotions for each segment
Implement new champion and develop next set of challengers
Performance
Measures
Customer value
Offer/promotion response rates
Activation rates
Usage rates
Average Revenue per User (ARPU)
Average number of products
Churn rates
Marketing spend
Challenges
Large number of single product customers
Low revenue per customer
High likelihood to churn among single product customers
Continuous Learning and Improvement

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

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Copyright 2014Senturus,Inc.
AllRightsReservedHear the Recording
Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website.
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Senturus: Business Analytics Consultants
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Copyright 2014 Senturus, Inc. All Rights ReservedBusinessIntelligence
Enterprise
Planning
Predictive
Analytics
Our Team:
Business depth combined with technical expertise. Former CFOs, CIOs, Controllers, Directors

800+ Clients, 1400+ Projects, 14 Years
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More Resources Available
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www.senturus.com/resources/

•Predictive Modeling Roadmap (2-3 weeks)
–Identify biggest predictive modeling opportunities
–Define business objectives and success metrics
–Identify modeling data sources
–Create modeling project roadmap
•Predictive Modeling Prototype (4-6 weeks)
–Specify predictive model definition
–Create prototype predictive model
–Demonstrate model effectiveness
How to Get Started with Predictive Modeling?
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For More Information
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Eric Zankman
ezankman@senturus.com
650 533 6419
Kay Knowles
kknowles@senturus.com
303 330 7321

www.senturus.com
Upcoming Events
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*Custom, tailored training also available*
Cognos Training Options
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Thank
You!!
www.senturus.com888-601-6010info@senturus.com
Copyright2014bySenturus,
Inc.
Thisentirepresentationiscopyrightedandmaynotbereusedor
distributedwithoutthewrittenconsentofSenturus,Inc.

A Case Study in Predictive Modeling: How One Firm Achieved Dramatic Results with Analytics

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A Case Study in Predictive Modeling: How One Firm Achieved Dramatic Results with Analytics