SlideShare a Scribd company logo
1 of 17
Extracting ROI From The Engaged Customer:
A Portfolio Management Approach to CRM
Magnify Analytic Solutions:
Keith Shields, Chief Analytics Officer – Magnify, Chief Credit Officer – Loan Science
Susan Arnot, Director, Decision Sciences
Laura Benard, Director, Client Services
Jen Boyer, Marketing Strategy Manager, Ford Customer Service Division
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
“Extracting ROI From The Engaged Customer: A Portfolio
Management Approach to CRM”…
2
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• What does the title mean?
• Portfolio Management, loosely, is the application of a set of collections and servicing
techniques (typically analytically-driven) aimed at maximizing a loan portfolio’s cash flows.
• Why managing a loan portfolio, especially a student loan portfolio, is a CRM problem…
www.loanscience.com
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
“Extracting ROI From The Engaged Customer: A Portfolio
Management Approach to CRM”…
3
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• Loans can be thought of as bonds that throw off a stochastic series of
cash flows.
• CRM is the same as portfolio management, and an engaged customer
can be thought of as a bond.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Managing a Portfolio Requires That We Turn Impaired Loans
Into Cash-Flowing Bonds…
4
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
EVENTS AND TACTICS
CASH FLOWS
$A1
Customer makes
partial payment
$A2
Customer’s loan
becomes
impaired, collections
calls ensue
Customer pays off
rewritten balance
Time
t=0
t=1 t=3
Customer’s loan is
rewritten for empirically-
derived optimal amount
• The value of this “bond” (loan) is $A1/(1+i)1 + $A2/(1+i)3
• This paradigm applies equally to CRM. The portfolios managed by CRM
professionals are the customer bases of the companies they serve.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
An Engaged Customer Is a Bond…
5Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
EVENTS AND TACTICS
CASH FLOWS
$A1
Customer comes
in for service
$A2
Customer signs up
for rewards program
Customer purchases
a new vehicle
Time
t=0
t=1 t=3
Customer visits
company website
• The value of this “bond” (customer) is $A1/(1+i)1 + $A2/(1+i)3
• But that doesn’t tell the whole story…what are the CRM implications?
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
More Thoughts on CRM and Portfolio Management…
6
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• Portfolio Management, loosely, is the application of a set of collections and servicing
techniques (typically analytically-driven) aimed at maximizing a loan portfolio’s cash flows.
• Is it fair to define CRM as the application of marketing techniques (often analytically-driven)
aimed at maximizing the repeat purchases of a set of customers?
• Loans can be thought of as bonds that throw off a stochastic series of cash flows.
• Customers can be thought of as bonds that throw off a stochastic series of cash flows, where
that stochastic element is often estimated by statistical models that predict loyalty and
retention.
• Uncollected debt can be placed in a variety of collections agencies based on which agency
extracts the best cash flow.
• Wouldn’t a CMO be willing to “place” his/her customer portfolio with a new CRM entity
(internal or external) if that entity could extract more repeat purchases from that portfolio?
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Does This Change the Way We Practice CRM?
7
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• We think so, especially in the following areas:
1. Measuring success
• Metrics should be more bottom-line oriented and exact
• PM example: cash received over time, cumulative default rate
2. Predicting success
• Loyalty and in-market models have value relatively (they rank-order the customers and
enable better targeting), but also in absolute (they tell you the probability a customer
will purchase)
• PM example: actual cash flows are measured against predicted for the purpose of
identifying when and why performance deviates from expected
3. Affecting success
• Remediating and incentivizing measures should be taken based on #1 and #2. If
customers become less loyal over time in a predictable manner, that problem should be
handled differently when they do so in an unpredictable manner.
• PM example: staffing and loss reserves
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Measuring Success
8
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• Champion / Challenger tests are very important, but serial analysis of Champion /
Challenger tests can lead to the wrong view of the “big picture”.
• Isolated Champion-challenger tests measure enticement, not necessarily engagement.
• Enticement is measured cross-sectionally, whereas engagement is measured longitudinally.
• Measure success at a high-level (all the while inviting segmentation and drill-down). Make
sure the measures are simple, with exact, and computed at over time:
• Example from auto parts and service: “20% of Jan13 servicers returned for service over the
subsequent year. 15% of Feb13 servicers returned for service over the next year. 10% of
Mar13 servicers returned for service over the subsequent year.” What’s the problem?
• A note: in general the most important metric associated with a loan portfolio is the sum of the
cash it throws off over time: both as an absolute amount and relative to the original forecast.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Measuring Success - Example
9
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• From auto parts and service again…
• “Return Rate”, the % of Servicers Returning in
the Next 12 Months is an exact measure, and
it’s easy to track over time. Graph top right.
• The metric itself also invites segmentation;
allowing insight into mix-shift and untreated
customer populations. This sounds a lot like
portfolio management.
• “Mix-shift” is a very important effect to
understand. See the graph bottom right.
How does this dynamic affect us when
marketing to a portfolio of customers?
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Predicting Success
10
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• From an analytics perspective, CRM and collections are essentially the same
thing.
• In both cases you collect all the data you know about a customer at a point in
time, predict likely behavior of the next 6-12 months, take action on that customer
based on the prediction.
• Collections calls are the base treatment of portfolio management; private offers
are the base treatment of CRM. Nuanced versions of those are left to champion
/ challenger testing.
• Infrastructure (DW, BRE) can and should be shared. www.zootweb.com
• => A full view of engagement: shared infrastructure allows real-time integration of
CRM and collections.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Predicting Success (continued)
11
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• Per the Basel III Accord, a bank must know the probability of default (PD) for
every loan on the portfolio. When the number of defaults exceeds forecast
(PD*# active loans), then there can be a capital adequacy problem. This is a
useful discipline…
• Shouldn’t CRM managers have a forecast of repeat sales / return visits?
Seems like this comes directly from the loyalty & in-market models
already in place.
• Lifetime Value Models are, in some sense, a statement about the worth
of the company. When calibrated properly, they equal the net present
value of the profit stream from a given customer.
• An eroding aggregated score from the Lifetime Value models can be
symptom bad CRM.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Predicting Success - Example
12
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• The “probability of return in the next 12
months” (PR12), is a model that can be
applied to the servicer portfolio at the
customer level.
• Aggregating the PR12 for each vehicle
age segment allows us to predict the
return rate for the segment.
• This puts us in a position to understand
when return rates, and thus return
visits, are higher or lower than we should
expect…which in turn puts us in a better
position to evaluate uncontrolled
tests, like national rebate offers or ad
campaigns. See graph right.
A national rebate offer in
1Q2012 creates 700 bps of
unexpected response.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Affecting Success
13
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• Back to PM…
• When managing a loan we use the models and analytics to keep the loan in its
most “valuable” state. Example: student loans, forbearance, reduced payment
plans…
• Even small decisions, like the decision to place a collections call is, and should
be, analytically-driven. Example: a pool of 1,000 loans are 15 days delinquent.
• Contacting a 15-day delinquency reduces the probability of default from 5% to 4.8%.
• We lose $5,000 for each default, so a contact is worth 0.2%*$5,000 = $10.
• The contact rate is 5% => calling the 1,000 will generate 1,000*.05*$10 = $500 per day in
value.
• We need three extra collectors to collect the 1,000 loans. Say collectors cost $6,000 per
month…roughly $200 per day. The additional three thus cost $600 per day.
• $600 cost > $500 revenue => we do not call 15-day delinquent borrowers.
• How does this apply to customer engagement and CRM? See next slide.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Affecting Success - Example
14
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• A CRM Manager should strive to keep customers in their most valuable state.
Determining the most valuable state is often a matter of predictive modeling.
For example (parts and service again):
• A customer requests $200 financial assistance with a repair that has occurred just
outside of warranty.
• Loyalty and customer satisfaction models tell us that, given this particular
customer’s demographics and past behavior, knocking $800 off the repair will
increase his “satisfaction rating” from 3 to 5, which has the impact of increasing
the likelihood of repurchase by 500 bps (5 percentage points).
• Putting the customer in a more valuable state (satisfaction=5) is worth 5% * $6,000
(the profit per vehicle sale) = $300.
• => The cost of putting the customer in a more valuable state ($200) is less than the
benefit of having him there ($300) , so the assistance is approved.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Closing Remarks
15
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• Understand that the job of CRM is to extract repeat sales and revenue from the
portfolio of customers. The best way to do this is make sure that customers remain
engaged over a long period of time.
• If a customer is a bond, then improving engagement, in effect, increases the life of the
bond.
• CRM groups should measure themselves with this standard in mind.
• Keeping customers in their “most valuable state” is a matter of advanced analytics and
strong marketing tactics…both of which are done with an eye towards engagement.
• The disciplines applied routinely to the management of loan portfolios are equally
applied to CRM. Champion / Challenger tests are simply one tool in a larger toolbox.
• Thank you for your time and attention.
magnify • simplify • amplifyMagnifyAnalytic Solutions
DMA Detroit 2014: Marketing to The Engaged Customer
Questions
16
Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
• “Judge a man by his questions rather than by his answers.” -- Voltaire
• So questions please…
Connect with and mention @MA_Detroit on Twitter and stop by the
Magnify booth to stay up-to-date on social listening news and updates!
777 Woodward Ave
Suite 500
Detroit, MI 48226
p 313.202.6324
f 313.965.2800
Keith Shields
kshields@magnifyas.com
kshields@loanscience.com
313.418.2734
Christina Polakowski
cpolakowski@marketingassociates.com
313.202.6355
Meagen Mazur
mmazur@marketingassociates.com
313.202.6347
17Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC.
Make smarter business decisions by outnumbering the competition
www.magnifyas.com

More Related Content

What's hot

Understanding Credit Scoring for Mortgage Professionals
Understanding Credit Scoring for Mortgage ProfessionalsUnderstanding Credit Scoring for Mortgage Professionals
Understanding Credit Scoring for Mortgage ProfessionalsSusan McCullah
 
Model building in credit card and loan approval
Model building in credit card and loan approval Model building in credit card and loan approval
Model building in credit card and loan approval Venkata Reddy Konasani
 
Delopment and testing of a credit scoring model
Delopment and testing of a credit scoring modelDelopment and testing of a credit scoring model
Delopment and testing of a credit scoring modelMattia Ciprian
 
Credit Scoring for FInancial Institutions, Eland Capital
Credit Scoring for FInancial Institutions, Eland Capital Credit Scoring for FInancial Institutions, Eland Capital
Credit Scoring for FInancial Institutions, Eland Capital Innovation Enterprise
 
Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...
Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...
Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...Fitzgerald Analytics, Inc.
 
Project Report - Acquisition Credit Scoring Model
Project Report - Acquisition Credit Scoring ModelProject Report - Acquisition Credit Scoring Model
Project Report - Acquisition Credit Scoring ModelSubhasis Mishra
 
Consumer Credit Scoring Using Logistic Regression and Random Forest
Consumer Credit Scoring Using Logistic Regression and Random ForestConsumer Credit Scoring Using Logistic Regression and Random Forest
Consumer Credit Scoring Using Logistic Regression and Random ForestHirak Sen Roy
 
Credit scoring using Rattle and R
Credit scoring using Rattle and RCredit scoring using Rattle and R
Credit scoring using Rattle and RAyan Das
 
Creditscore
CreditscoreCreditscore
Creditscorekevinlan
 
Predicting Delinquency-Give me some credit
Predicting Delinquency-Give me some creditPredicting Delinquency-Give me some credit
Predicting Delinquency-Give me some creditpragativbora
 
Analytics in financial services prez behavioral finance + data visualizatio...
Analytics in financial services prez   behavioral finance + data visualizatio...Analytics in financial services prez   behavioral finance + data visualizatio...
Analytics in financial services prez behavioral finance + data visualizatio...Fitzgerald Analytics, Inc.
 
Kaggle "Give me some credit" challenge overview
Kaggle "Give me some credit" challenge overviewKaggle "Give me some credit" challenge overview
Kaggle "Give me some credit" challenge overviewAdam Pah
 
Business Strategy for Banks and Credit Unions
Business Strategy for Banks and Credit UnionsBusiness Strategy for Banks and Credit Unions
Business Strategy for Banks and Credit UnionsSerge Milman
 
Sas credit scorecards
Sas credit scorecardsSas credit scorecards
Sas credit scorecardsTEMPLA73
 
Introduction to predictive modeling v1
Introduction to predictive modeling v1Introduction to predictive modeling v1
Introduction to predictive modeling v1Venkata Reddy Konasani
 
Data analytics in finance broucher
Data analytics in finance broucher Data analytics in finance broucher
Data analytics in finance broucher Daniel Thomas
 

What's hot (20)

Understanding Credit Scoring for Mortgage Professionals
Understanding Credit Scoring for Mortgage ProfessionalsUnderstanding Credit Scoring for Mortgage Professionals
Understanding Credit Scoring for Mortgage Professionals
 
Model building in credit card and loan approval
Model building in credit card and loan approval Model building in credit card and loan approval
Model building in credit card and loan approval
 
Delopment and testing of a credit scoring model
Delopment and testing of a credit scoring modelDelopment and testing of a credit scoring model
Delopment and testing of a credit scoring model
 
Credit Scoring for FInancial Institutions, Eland Capital
Credit Scoring for FInancial Institutions, Eland Capital Credit Scoring for FInancial Institutions, Eland Capital
Credit Scoring for FInancial Institutions, Eland Capital
 
Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...
Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...
Governing the Data to Dollars Value Chain™ - Sept 2012 NYC Data Governance Co...
 
Project Report - Acquisition Credit Scoring Model
Project Report - Acquisition Credit Scoring ModelProject Report - Acquisition Credit Scoring Model
Project Report - Acquisition Credit Scoring Model
 
SP Five FF. ICBA handouts
SP Five FF. ICBA handoutsSP Five FF. ICBA handouts
SP Five FF. ICBA handouts
 
Consumer Credit Scoring Using Logistic Regression and Random Forest
Consumer Credit Scoring Using Logistic Regression and Random ForestConsumer Credit Scoring Using Logistic Regression and Random Forest
Consumer Credit Scoring Using Logistic Regression and Random Forest
 
Credit scoring using Rattle and R
Credit scoring using Rattle and RCredit scoring using Rattle and R
Credit scoring using Rattle and R
 
Creditscore
CreditscoreCreditscore
Creditscore
 
Predicting Delinquency-Give me some credit
Predicting Delinquency-Give me some creditPredicting Delinquency-Give me some credit
Predicting Delinquency-Give me some credit
 
Credit scorecard
Credit scorecardCredit scorecard
Credit scorecard
 
5 C's of Credit
5 C's of Credit5 C's of Credit
5 C's of Credit
 
Analytics in financial services prez behavioral finance + data visualizatio...
Analytics in financial services prez   behavioral finance + data visualizatio...Analytics in financial services prez   behavioral finance + data visualizatio...
Analytics in financial services prez behavioral finance + data visualizatio...
 
Kaggle "Give me some credit" challenge overview
Kaggle "Give me some credit" challenge overviewKaggle "Give me some credit" challenge overview
Kaggle "Give me some credit" challenge overview
 
Business Strategy for Banks and Credit Unions
Business Strategy for Banks and Credit UnionsBusiness Strategy for Banks and Credit Unions
Business Strategy for Banks and Credit Unions
 
Credit Risk Analytics
Credit Risk AnalyticsCredit Risk Analytics
Credit Risk Analytics
 
Sas credit scorecards
Sas credit scorecardsSas credit scorecards
Sas credit scorecards
 
Introduction to predictive modeling v1
Introduction to predictive modeling v1Introduction to predictive modeling v1
Introduction to predictive modeling v1
 
Data analytics in finance broucher
Data analytics in finance broucher Data analytics in finance broucher
Data analytics in finance broucher
 

Viewers also liked

Dynamically Evolving Systems: Cluster Analysis Using Time
Dynamically Evolving Systems: Cluster Analysis Using TimeDynamically Evolving Systems: Cluster Analysis Using Time
Dynamically Evolving Systems: Cluster Analysis Using TimeMagnify Analytic Solutions
 
Death of a Salesman: Account Acquisition in a New Environment
Death of a Salesman: Account Acquisition in a New Environment Death of a Salesman: Account Acquisition in a New Environment
Death of a Salesman: Account Acquisition in a New Environment Magnify Analytic Solutions
 
Non-Temporal ARIMA Models in Statistical Research
Non-Temporal ARIMA Models in Statistical ResearchNon-Temporal ARIMA Models in Statistical Research
Non-Temporal ARIMA Models in Statistical ResearchMagnify Analytic Solutions
 
Logistic Modeling with Applications to Marketing and Credit Risk in the Autom...
Logistic Modeling with Applications to Marketing and Credit Risk in the Autom...Logistic Modeling with Applications to Marketing and Credit Risk in the Autom...
Logistic Modeling with Applications to Marketing and Credit Risk in the Autom...Magnify Analytic Solutions
 
Risk and insurance management model questions
Risk and insurance management model questionsRisk and insurance management model questions
Risk and insurance management model questionsMostafa Ahmed
 

Viewers also liked (10)

Quantifying the Buzz Effect
Quantifying the Buzz Effect Quantifying the Buzz Effect
Quantifying the Buzz Effect
 
Whitepaper: Maximizing the Power of Hash Tables
Whitepaper: Maximizing the Power of Hash TablesWhitepaper: Maximizing the Power of Hash Tables
Whitepaper: Maximizing the Power of Hash Tables
 
Dynamically Evolving Systems: Cluster Analysis Using Time
Dynamically Evolving Systems: Cluster Analysis Using TimeDynamically Evolving Systems: Cluster Analysis Using Time
Dynamically Evolving Systems: Cluster Analysis Using Time
 
Model Validation
Model Validation Model Validation
Model Validation
 
Death of a Salesman: Account Acquisition in a New Environment
Death of a Salesman: Account Acquisition in a New Environment Death of a Salesman: Account Acquisition in a New Environment
Death of a Salesman: Account Acquisition in a New Environment
 
Leading & Lagging Indicators in SAS
Leading & Lagging Indicators in SAS Leading & Lagging Indicators in SAS
Leading & Lagging Indicators in SAS
 
Non-Temporal ARIMA Models in Statistical Research
Non-Temporal ARIMA Models in Statistical ResearchNon-Temporal ARIMA Models in Statistical Research
Non-Temporal ARIMA Models in Statistical Research
 
Three "Real Time" Analytics Solutions
Three "Real Time" Analytics Solutions Three "Real Time" Analytics Solutions
Three "Real Time" Analytics Solutions
 
Logistic Modeling with Applications to Marketing and Credit Risk in the Autom...
Logistic Modeling with Applications to Marketing and Credit Risk in the Autom...Logistic Modeling with Applications to Marketing and Credit Risk in the Autom...
Logistic Modeling with Applications to Marketing and Credit Risk in the Autom...
 
Risk and insurance management model questions
Risk and insurance management model questionsRisk and insurance management model questions
Risk and insurance management model questions
 

Similar to Magnify AIMS presentation 2014

Magnify DMA presentation 2014
Magnify DMA presentation 2014Magnify DMA presentation 2014
Magnify DMA presentation 2014Keith Shields
 
Chapter 1 marketing managment
Chapter 1 marketing managmentChapter 1 marketing managment
Chapter 1 marketing managmentZohaib Ahmed
 
Lecture 3 Customer Relationship Management
Lecture 3 Customer Relationship ManagementLecture 3 Customer Relationship Management
Lecture 3 Customer Relationship ManagementAli Noman
 
Why Your Customer HealthScore is Useless and How to Overcome It
Why Your Customer HealthScore is Useless and How to Overcome ItWhy Your Customer HealthScore is Useless and How to Overcome It
Why Your Customer HealthScore is Useless and How to Overcome ItBoaz S. Maor
 
1crm 120208025631-phpapp01
1crm 120208025631-phpapp011crm 120208025631-phpapp01
1crm 120208025631-phpapp01Khaled Tarawneh
 
How to account for customer success
How to account for customer successHow to account for customer success
How to account for customer successGainsight
 
Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.
Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.
Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.Revegy, Inc.
 
- 1 - Ivey Business Journal NovemberDecember 2002No one
- 1 - Ivey Business Journal  NovemberDecember 2002No one - 1 - Ivey Business Journal  NovemberDecember 2002No one
- 1 - Ivey Business Journal NovemberDecember 2002No one SilvaGraf83
 
- 1 - Ivey Business Journal NovemberDecember 2002No one
- 1 - Ivey Business Journal  NovemberDecember 2002No one - 1 - Ivey Business Journal  NovemberDecember 2002No one
- 1 - Ivey Business Journal NovemberDecember 2002No one RayleneAndre399
 
Total Customer Experience Management Overview #TCE #CEM -- The Why, What and...
Total Customer Experience Management Overview #TCE #CEM  -- The Why, What and...Total Customer Experience Management Overview #TCE #CEM  -- The Why, What and...
Total Customer Experience Management Overview #TCE #CEM -- The Why, What and...Stephen King
 
ABM Charter Template and Explanation
ABM Charter Template and ExplanationABM Charter Template and Explanation
ABM Charter Template and ExplanationDemandbase
 
Quant5 Ten Critical Business Insights
Quant5 Ten Critical Business InsightsQuant5 Ten Critical Business Insights
Quant5 Ten Critical Business InsightsDoug Levin
 
Marketing Operations ROI: It`s Simpler and Way Harder Than You Think
Marketing Operations ROI: It`s Simpler and Way Harder Than You ThinkMarketing Operations ROI: It`s Simpler and Way Harder Than You Think
Marketing Operations ROI: It`s Simpler and Way Harder Than You ThinkClearAction Continuum
 

Similar to Magnify AIMS presentation 2014 (20)

Magnify DMA presentation 2014
Magnify DMA presentation 2014Magnify DMA presentation 2014
Magnify DMA presentation 2014
 
Ibm crm
Ibm crmIbm crm
Ibm crm
 
Ibm crm
Ibm crmIbm crm
Ibm crm
 
Chapter 1 marketing managment
Chapter 1 marketing managmentChapter 1 marketing managment
Chapter 1 marketing managment
 
Lecture 3 Customer Relationship Management
Lecture 3 Customer Relationship ManagementLecture 3 Customer Relationship Management
Lecture 3 Customer Relationship Management
 
Why Your Customer HealthScore is Useless and How to Overcome It
Why Your Customer HealthScore is Useless and How to Overcome ItWhy Your Customer HealthScore is Useless and How to Overcome It
Why Your Customer HealthScore is Useless and How to Overcome It
 
1crm 120208025631-phpapp01
1crm 120208025631-phpapp011crm 120208025631-phpapp01
1crm 120208025631-phpapp01
 
Crm case study
Crm case studyCrm case study
Crm case study
 
How to account for customer success
How to account for customer successHow to account for customer success
How to account for customer success
 
Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.
Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.
Mine the Gold You Already Have! 5 Steps to Better Strategic Account Management.
 
- 1 - Ivey Business Journal NovemberDecember 2002No one
- 1 - Ivey Business Journal  NovemberDecember 2002No one - 1 - Ivey Business Journal  NovemberDecember 2002No one
- 1 - Ivey Business Journal NovemberDecember 2002No one
 
- 1 - Ivey Business Journal NovemberDecember 2002No one
- 1 - Ivey Business Journal  NovemberDecember 2002No one - 1 - Ivey Business Journal  NovemberDecember 2002No one
- 1 - Ivey Business Journal NovemberDecember 2002No one
 
Total Customer Experience Management Overview #TCE #CEM -- The Why, What and...
Total Customer Experience Management Overview #TCE #CEM  -- The Why, What and...Total Customer Experience Management Overview #TCE #CEM  -- The Why, What and...
Total Customer Experience Management Overview #TCE #CEM -- The Why, What and...
 
Reaptransform
ReaptransformReaptransform
Reaptransform
 
ABM Charter Template and Explanation
ABM Charter Template and ExplanationABM Charter Template and Explanation
ABM Charter Template and Explanation
 
Quant5 Ten Critical Business Insights
Quant5 Ten Critical Business InsightsQuant5 Ten Critical Business Insights
Quant5 Ten Critical Business Insights
 
Mba ii ewis u iv crm
Mba ii ewis u iv crmMba ii ewis u iv crm
Mba ii ewis u iv crm
 
Types of crm
Types of crmTypes of crm
Types of crm
 
Keys to Success
Keys to SuccessKeys to Success
Keys to Success
 
Marketing Operations ROI: It`s Simpler and Way Harder Than You Think
Marketing Operations ROI: It`s Simpler and Way Harder Than You ThinkMarketing Operations ROI: It`s Simpler and Way Harder Than You Think
Marketing Operations ROI: It`s Simpler and Way Harder Than You Think
 

Recently uploaded

Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)jennyeacort
 
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...limedy534
 
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptxNLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptxBoston Institute of Analytics
 
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfBoston Institute of Analytics
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsVICTOR MAESTRE RAMIREZ
 
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...GQ Research
 
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Jack DiGiovanna
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfgstagge
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改yuu sss
 
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...Florian Roscheck
 
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort servicejennyeacort
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our WorldEduminds Learning
 
How we prevented account sharing with MFA
How we prevented account sharing with MFAHow we prevented account sharing with MFA
How we prevented account sharing with MFAAndrei Kaleshka
 
GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]📊 Markus Baersch
 
PKS-TGC-1084-630 - Stage 1 Proposal.pptx
PKS-TGC-1084-630 - Stage 1 Proposal.pptxPKS-TGC-1084-630 - Stage 1 Proposal.pptx
PKS-TGC-1084-630 - Stage 1 Proposal.pptxPramod Kumar Srivastava
 
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.natarajan8993
 
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...Boston Institute of Analytics
 
DBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfDBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfJohn Sterrett
 

Recently uploaded (20)

Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
Call Us ➥97111√47426🤳Call Girls in Aerocity (Delhi NCR)
 
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
 
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptxNLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
NLP Project PPT: Flipkart Product Reviews through NLP Data Science.pptx
 
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
 
Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
Deep Generative Learning for All - The Gen AI Hype (Spring 2024)Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
Deep Generative Learning for All - The Gen AI Hype (Spring 2024)
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business Professionals
 
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
Biometric Authentication: The Evolution, Applications, Benefits and Challenge...
 
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
Building on a FAIRly Strong Foundation to Connect Academic Research to Transl...
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdf
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
 
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
 
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
9711147426✨Call In girls Gurgaon Sector 31. SCO 25 escort service
 
Learn How Data Science Changes Our World
Learn How Data Science Changes Our WorldLearn How Data Science Changes Our World
Learn How Data Science Changes Our World
 
Call Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort ServiceCall Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort Service
 
How we prevented account sharing with MFA
How we prevented account sharing with MFAHow we prevented account sharing with MFA
How we prevented account sharing with MFA
 
GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]
 
PKS-TGC-1084-630 - Stage 1 Proposal.pptx
PKS-TGC-1084-630 - Stage 1 Proposal.pptxPKS-TGC-1084-630 - Stage 1 Proposal.pptx
PKS-TGC-1084-630 - Stage 1 Proposal.pptx
 
RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.RABBIT: A CLI tool for identifying bots based on their GitHub events.
RABBIT: A CLI tool for identifying bots based on their GitHub events.
 
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
 
DBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdfDBA Basics: Getting Started with Performance Tuning.pdf
DBA Basics: Getting Started with Performance Tuning.pdf
 

Magnify AIMS presentation 2014

  • 1. Extracting ROI From The Engaged Customer: A Portfolio Management Approach to CRM Magnify Analytic Solutions: Keith Shields, Chief Analytics Officer – Magnify, Chief Credit Officer – Loan Science Susan Arnot, Director, Decision Sciences Laura Benard, Director, Client Services Jen Boyer, Marketing Strategy Manager, Ford Customer Service Division
  • 2. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer “Extracting ROI From The Engaged Customer: A Portfolio Management Approach to CRM”… 2 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • What does the title mean? • Portfolio Management, loosely, is the application of a set of collections and servicing techniques (typically analytically-driven) aimed at maximizing a loan portfolio’s cash flows. • Why managing a loan portfolio, especially a student loan portfolio, is a CRM problem… www.loanscience.com
  • 3. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer “Extracting ROI From The Engaged Customer: A Portfolio Management Approach to CRM”… 3 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • Loans can be thought of as bonds that throw off a stochastic series of cash flows. • CRM is the same as portfolio management, and an engaged customer can be thought of as a bond.
  • 4. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Managing a Portfolio Requires That We Turn Impaired Loans Into Cash-Flowing Bonds… 4 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. EVENTS AND TACTICS CASH FLOWS $A1 Customer makes partial payment $A2 Customer’s loan becomes impaired, collections calls ensue Customer pays off rewritten balance Time t=0 t=1 t=3 Customer’s loan is rewritten for empirically- derived optimal amount • The value of this “bond” (loan) is $A1/(1+i)1 + $A2/(1+i)3 • This paradigm applies equally to CRM. The portfolios managed by CRM professionals are the customer bases of the companies they serve.
  • 5. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer An Engaged Customer Is a Bond… 5Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. EVENTS AND TACTICS CASH FLOWS $A1 Customer comes in for service $A2 Customer signs up for rewards program Customer purchases a new vehicle Time t=0 t=1 t=3 Customer visits company website • The value of this “bond” (customer) is $A1/(1+i)1 + $A2/(1+i)3 • But that doesn’t tell the whole story…what are the CRM implications?
  • 6. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer More Thoughts on CRM and Portfolio Management… 6 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • Portfolio Management, loosely, is the application of a set of collections and servicing techniques (typically analytically-driven) aimed at maximizing a loan portfolio’s cash flows. • Is it fair to define CRM as the application of marketing techniques (often analytically-driven) aimed at maximizing the repeat purchases of a set of customers? • Loans can be thought of as bonds that throw off a stochastic series of cash flows. • Customers can be thought of as bonds that throw off a stochastic series of cash flows, where that stochastic element is often estimated by statistical models that predict loyalty and retention. • Uncollected debt can be placed in a variety of collections agencies based on which agency extracts the best cash flow. • Wouldn’t a CMO be willing to “place” his/her customer portfolio with a new CRM entity (internal or external) if that entity could extract more repeat purchases from that portfolio?
  • 7. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Does This Change the Way We Practice CRM? 7 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • We think so, especially in the following areas: 1. Measuring success • Metrics should be more bottom-line oriented and exact • PM example: cash received over time, cumulative default rate 2. Predicting success • Loyalty and in-market models have value relatively (they rank-order the customers and enable better targeting), but also in absolute (they tell you the probability a customer will purchase) • PM example: actual cash flows are measured against predicted for the purpose of identifying when and why performance deviates from expected 3. Affecting success • Remediating and incentivizing measures should be taken based on #1 and #2. If customers become less loyal over time in a predictable manner, that problem should be handled differently when they do so in an unpredictable manner. • PM example: staffing and loss reserves
  • 8. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Measuring Success 8 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • Champion / Challenger tests are very important, but serial analysis of Champion / Challenger tests can lead to the wrong view of the “big picture”. • Isolated Champion-challenger tests measure enticement, not necessarily engagement. • Enticement is measured cross-sectionally, whereas engagement is measured longitudinally. • Measure success at a high-level (all the while inviting segmentation and drill-down). Make sure the measures are simple, with exact, and computed at over time: • Example from auto parts and service: “20% of Jan13 servicers returned for service over the subsequent year. 15% of Feb13 servicers returned for service over the next year. 10% of Mar13 servicers returned for service over the subsequent year.” What’s the problem? • A note: in general the most important metric associated with a loan portfolio is the sum of the cash it throws off over time: both as an absolute amount and relative to the original forecast.
  • 9. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Measuring Success - Example 9 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • From auto parts and service again… • “Return Rate”, the % of Servicers Returning in the Next 12 Months is an exact measure, and it’s easy to track over time. Graph top right. • The metric itself also invites segmentation; allowing insight into mix-shift and untreated customer populations. This sounds a lot like portfolio management. • “Mix-shift” is a very important effect to understand. See the graph bottom right. How does this dynamic affect us when marketing to a portfolio of customers?
  • 10. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Predicting Success 10 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • From an analytics perspective, CRM and collections are essentially the same thing. • In both cases you collect all the data you know about a customer at a point in time, predict likely behavior of the next 6-12 months, take action on that customer based on the prediction. • Collections calls are the base treatment of portfolio management; private offers are the base treatment of CRM. Nuanced versions of those are left to champion / challenger testing. • Infrastructure (DW, BRE) can and should be shared. www.zootweb.com • => A full view of engagement: shared infrastructure allows real-time integration of CRM and collections.
  • 11. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Predicting Success (continued) 11 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • Per the Basel III Accord, a bank must know the probability of default (PD) for every loan on the portfolio. When the number of defaults exceeds forecast (PD*# active loans), then there can be a capital adequacy problem. This is a useful discipline… • Shouldn’t CRM managers have a forecast of repeat sales / return visits? Seems like this comes directly from the loyalty & in-market models already in place. • Lifetime Value Models are, in some sense, a statement about the worth of the company. When calibrated properly, they equal the net present value of the profit stream from a given customer. • An eroding aggregated score from the Lifetime Value models can be symptom bad CRM.
  • 12. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Predicting Success - Example 12 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • The “probability of return in the next 12 months” (PR12), is a model that can be applied to the servicer portfolio at the customer level. • Aggregating the PR12 for each vehicle age segment allows us to predict the return rate for the segment. • This puts us in a position to understand when return rates, and thus return visits, are higher or lower than we should expect…which in turn puts us in a better position to evaluate uncontrolled tests, like national rebate offers or ad campaigns. See graph right. A national rebate offer in 1Q2012 creates 700 bps of unexpected response.
  • 13. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Affecting Success 13 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • Back to PM… • When managing a loan we use the models and analytics to keep the loan in its most “valuable” state. Example: student loans, forbearance, reduced payment plans… • Even small decisions, like the decision to place a collections call is, and should be, analytically-driven. Example: a pool of 1,000 loans are 15 days delinquent. • Contacting a 15-day delinquency reduces the probability of default from 5% to 4.8%. • We lose $5,000 for each default, so a contact is worth 0.2%*$5,000 = $10. • The contact rate is 5% => calling the 1,000 will generate 1,000*.05*$10 = $500 per day in value. • We need three extra collectors to collect the 1,000 loans. Say collectors cost $6,000 per month…roughly $200 per day. The additional three thus cost $600 per day. • $600 cost > $500 revenue => we do not call 15-day delinquent borrowers. • How does this apply to customer engagement and CRM? See next slide.
  • 14. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Affecting Success - Example 14 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • A CRM Manager should strive to keep customers in their most valuable state. Determining the most valuable state is often a matter of predictive modeling. For example (parts and service again): • A customer requests $200 financial assistance with a repair that has occurred just outside of warranty. • Loyalty and customer satisfaction models tell us that, given this particular customer’s demographics and past behavior, knocking $800 off the repair will increase his “satisfaction rating” from 3 to 5, which has the impact of increasing the likelihood of repurchase by 500 bps (5 percentage points). • Putting the customer in a more valuable state (satisfaction=5) is worth 5% * $6,000 (the profit per vehicle sale) = $300. • => The cost of putting the customer in a more valuable state ($200) is less than the benefit of having him there ($300) , so the assistance is approved.
  • 15. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Closing Remarks 15 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • Understand that the job of CRM is to extract repeat sales and revenue from the portfolio of customers. The best way to do this is make sure that customers remain engaged over a long period of time. • If a customer is a bond, then improving engagement, in effect, increases the life of the bond. • CRM groups should measure themselves with this standard in mind. • Keeping customers in their “most valuable state” is a matter of advanced analytics and strong marketing tactics…both of which are done with an eye towards engagement. • The disciplines applied routinely to the management of loan portfolios are equally applied to CRM. Champion / Challenger tests are simply one tool in a larger toolbox. • Thank you for your time and attention.
  • 16. magnify • simplify • amplifyMagnifyAnalytic Solutions DMA Detroit 2014: Marketing to The Engaged Customer Questions 16 Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. • “Judge a man by his questions rather than by his answers.” -- Voltaire • So questions please… Connect with and mention @MA_Detroit on Twitter and stop by the Magnify booth to stay up-to-date on social listening news and updates!
  • 17. 777 Woodward Ave Suite 500 Detroit, MI 48226 p 313.202.6324 f 313.965.2800 Keith Shields kshields@magnifyas.com kshields@loanscience.com 313.418.2734 Christina Polakowski cpolakowski@marketingassociates.com 313.202.6355 Meagen Mazur mmazur@marketingassociates.com 313.202.6347 17Copyright ©2012 Marketing Associates LLC. All rights reserved. Magnify is a division of Marketing Associates, LLC. Make smarter business decisions by outnumbering the competition www.magnifyas.com