SlideShare a Scribd company logo
Extracting ROI From The 
Engaged Customer 
A Portfolio Management Approach to CRM 
Keith Shields Laura Benard Jen Boyer 
Chief Analytics Officer Client Services Director Marketing Strategy Manager 
Magnify Analytic Solutions Magnify Analytic Solutions Ford Motor Company 
10/28/2014
Q. How related are the following two topics? 
Consumer Loan 
Management 
VS. CRM Marketing 
1
Q. How related are the following two topics? 
Consumer Loan 
Management 
VS. CRM Marketing 
a. Not At All Related 
2
Q. How related are the following two topics? 
Consumer Loan 
Management 
VS. CRM Marketing 
b. Somewhat Related 
3
Q. How related are the following two topics? 
Consumer Loan 
Management 
VS. CRM Marketing 
c. Very Related 
4
Both Consumer Loan Managers and CRM Managers… 
Start with a portfolio of customers 
Have access to enormous amounts of customer data 
Can manage their portfolio using predictive analytics 
Both are responsible for the long term value of their assets 
5
Why is this an important discussion for us? 
We can apply learnings from the Consumer Loan 
Industry to manage our customer portfolios… 
Spending on Marketing Analytics is expected 
to increase 72% over the next 3 years* 
Only 32% of marketing projects use analytics 
Most companies do not have the right talent 
to fully leverage Marketing Analytics 
“…77% of marketers surveyed believe data on 
customer purchase histories can improve 
marketing ROI, yet only 21% actually use it. 
Likewise, 88% believe behavioral data can do 
the same, but only 20% use it” 
Big Data and new tools are quickly 
changing this landscape 
*2014 February; The CMO Survey.org Highlights and Insights 
6
CONCEPTUAL NONSENSE FROM THE 
SCIENTIST…
Defining “Portfolio Management” 
A portfolio of consumer loans, not securities. 
• Portfolio Management, loosely, is the application of a set of 
analytically-driven collections and servicing techniques aimed at 
forecasting and maximizing a loan portfolio’s cash flows. 
• Quantifying credit risk and predicting future payment is at the heart 
of portfolio management. 
• Credit Risk and CRM seemingly dominate the Big Data landscape. 
Next slide… 
7
“Big Data” infects the CRM and Credit Risk 
disciplines more than almost any other… 
Why the pervasive interest in Big Data? 
• Largely to satisfy CRM and credit risk needs… 
Data: Information Week Analytics, Business Intelligence and Information Management 
Survey of 417 business technology professionals at companies using or planning to deploy 
data analytics, BI or statistical analysis software, October 2012 
CREDIT RISK 
NEEDS 
CRM NEEDS 
8
(Re)Defining CRM 
A portfolio of customers, not loans. 
• Portfolio Management CRM, loosely, is the application of a set of 
analytically-driven collections and servicing marketing techniques aimed 
at forecasting and maximizing a loan portfolio’s customers’ cash flows 
purchases. 
• Quantifying credit defection risk and predicting future payment 
purchases is at the heart of portfolio management CRM. 
• Incidentally, the Wikipedia definition of CRM is: 
• CRM is a system for managing a company’s interactions with current and future 
customers. It involves using technology to organize, automate and synchronize sales, 
marketing, customer service, and technical support. 
9
Portfolio Management and CRM… 
From an Analytics perspective, these are the same. The 
only difference lies in the target variable and predictors. 
• PD = 1 / (1+e-z), where z = A + Bx1 + Cx2 + Dx3 + … 
• Portfolio Management: 
• PD = Probability of DEFAULT 
• x1 = credit score, x2 = days past due, x3 = loan to value ratio, etc… 
• CRM: 
• PD = Probability of DEFECTION 
• x1 = prior purchases, x2 = months since last purchase, x3 = 
unfavorable tweets, etc… 
10
Others have recognized and leveraged the 
overlap… 
11 
• Auto Pre-Approval 
• Merchant Cash Advance and Small Business Loans 
• Pier-to-Pier lending 
• Student loan servicing 
• Business Rules Engines
The Portfolio Management Paradigm 
1 
4 
Managing a loan portfolio requires that we turn impaired (high 
credit risk) loans into cash-flowing bonds… 
Customer’s loan is 
rewritten for empirically-derived 
optimal amount 
CASH FLOWS 
Customer makes 
partial payment 
$A1 
$A2 
Loan impaired, 
collections calls 
ensue 
Customer pays off 
rewritten balance 
Time 
t=0 
t=1 t=3 
• 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. 
12
Adopting the PM paradigm for CRM… 
1 
5 
An engaged customer is a bond. The effectiveness of our CRM 
strategies determines the yield of that bond. 
1 
5 
Customer comes 
in for service 
Customer visits 
company website 
$A1 CASH FLOWS 
$A2 
Customer signs up 
for rewards program 
Customer purchases 
a new vehicle 
Time 
t=0 
t=1 t=3 
• The value of this “bond” (customer) is $A1/(1+i)1 + $A2/(1+i)3 
• Customer Lifetime Value (CLV) models help quantify the value of customer 
behaviors and CRM tactics. The success of CRM can be measured by the 
extent to which CLV increases, irrespective of test-control results. 
13
Marketers already recognize the need to 
view their customer base as a portfolio… 
Types of Data that Marketers Worldwide Would 
Like to Add to Their Customer Data Profile 
42% 
42% 
14 
19% 
19% 
14% 
12% 
24% 
35% 
45% 
53% 
71% 
Predictive analytics around lifetime… 
Online customer profile 
Customer service feedback 
User survey and preference data 
Social media data 
Third-party demographic data 
Sales executive insights 
Finance / customer payment data 
Order history 
Analyting is better than what we… 
0% 10% 20% 30% 40% 50% 60% 70% 80% 
In-store / agent exchanges 
(1Q2013) 
Source: CMO Council and SAS 
% of respondents 
• 71% of marketers want 
“predictive analytics around 
lifetime value” added to their 
customer data profiles… 
• Lifetime value models are 
nothing more than a forecast of 
cash flows at the customer 
level… 
• Survival analysis, vintage-level 
monitoring, and other popular 
PM disciplines are a must…
But CRM trails Credit Risk / Portfolio 
Management in the adoption of Big Data…why? 
“…77% of marketers surveyed believe data on customer purchase histories can 
improve marketing ROI, yet only 21% actually use it. Likewise, 88% believe 
behavioral data can do the same, but only 20% use it” 
15 
• Regulation 
• Accountability is “fuzzy” 
• Metrics are inexact and not directly reflective of behavior. 
• Secondary markets 
• What would CRM analytics look like if marketers were 
forced to buy, sell, and “value” their customer 
portfolios? 
• Metrics are inexact and not directly reflective of behavior.
What PM practices will help our CRM? 
• Take a longitudinal view of the customer. This is the only way to get an 
accurate outlook and valuation. Implies a need for a CLV model… 
16 
• CLV = p(sale at time 1)*E($ profit from sale) / (1 + d)1 + 
p(sale at time 2)*E($ profit from sale) / (1 + d)2 + 
p(sale at time 3)*E($ profit from sale) / (1 + d)3 + … 
• Engagement is measured longitudinally; enticement is measured cross-sectionally. 
• Quantify the impact of “mix shift” on outcomes of interest. 
• Establish “regulatory-like” rigor around model validation. 
• Understand that the two share not only a brain, but also a nervous 
system. Next slide…
ENOUGH CONCEPTUAL NONSENSE 
FROM THE SCIENTIST. NOW SOME 
PRACTICAL STUFF THE MARKETER…
Does This Change the Way We Practice CRM? 
17 
We think so, especially in the following areas: 
Measuring Success 
Metrics should be more bottom-line oriented 
and exact 
Shift from basing success solely on campaign 
performance to understanding performance of 
the portfolio 
Predicting Outcomes 
Predictions should go beyond the “next 
transaction” 
All available data should be leveraged to 
proactively manage customers throughout the 
lifecycle to desired business objectives 
Influencing Behavior 
CRM becomes our “sand box” for going beyond 
understanding just correlations; to understanding 
causation as a way to change customer behavior
Predicting Outcomes 
18 
Transaction vs. Portfolio Management approach to predicting outcomes…. 
% In-Market 
Short-Term: Optimizing campaign performance to 
campaign objectives 
Segment Size 
Opportunity 
Longer-Term: Enables management of entire 
portfolio to business objectives (i.e. increasing CLV)
Influencing Behavior 
Test and learn approach will determine how we influence and change the long term 
health of our customer portfolio… 
Monitor drivers across 
the portfolio… 
Design treatments, messaging and investment 
based on customer value, individual customer 
drivers and predicted outcome 
Understand 
Drivers of 
Desired 
Outcomes 
Every CRM treatment should be analytically driven…ensuring that every CRM 
dollar spent is working to move the customer into a more valuable state 
19
Measuring Success 
Strategic 
Operational 
Tactical 
Portfolio Health 
What is the value of my customer portfolio? 
What is the mix and risk of my customer portfolio? 
Performance and Forecasting 
Do I understand both rear-ward and forward-looking performance? 
What is the aggregate impact of our CRM initiatives on improving sales? 
Dashboard and Diagnostics 
Which champion vs. challenger campaign performs best? 
Which actions influence customer outcomes both positive and negative? 
20
Thus Ends the Prepared Remarks… 
• 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. 
21 
• 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.
“JUDGE A MAN BY HIS QUESTIONS 
RATHER THAN HIS ANSWERS.” -- 
VOLTAIRE

More Related Content

What's hot

Overview of Data Analytics in Lending Business
Overview of Data Analytics in Lending BusinessOverview of Data Analytics in Lending Business
Overview of Data Analytics in Lending Business
Sanjay Kar
 
Market Practice Series (Credit Losses Modeling)
Market Practice Series (Credit Losses Modeling)Market Practice Series (Credit Losses Modeling)
Market Practice Series (Credit Losses Modeling)
Yahya Kamel
 
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
Mattia Ciprian
 
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 Forest
Hirak Sen Roy
 
Credit scoring using Rattle and R
Credit scoring using Rattle and RCredit scoring using Rattle and R
Credit scoring using Rattle and R
Ayan Das
 
Predicting Delinquency-Give me some credit
Predicting Delinquency-Give me some creditPredicting Delinquency-Give me some credit
Predicting Delinquency-Give me some credit
pragativbora
 
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
 
Creditscore
CreditscoreCreditscore
Creditscorekevinlan
 
Credit scorecard
Credit scorecardCredit scorecard
Credit scorecard
Tuhin AI Advisory
 
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
Adam Pah
 
Credit Risk Analytics
Credit Risk AnalyticsCredit Risk Analytics
Credit Risk Analytics
Senthil Ramanath
 
Introduction to predictive modeling v1
Introduction to predictive modeling v1Introduction to predictive modeling v1
Introduction to predictive modeling v1
Venkata Reddy Konasani
 
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...
brucelb
 
Sas credit scorecards
Sas credit scorecardsSas credit scorecards
Sas credit scorecards
TEMPLA73
 
Forecasting peer to_peer_lending_risk
Forecasting peer to_peer_lending_riskForecasting peer to_peer_lending_risk
Forecasting peer to_peer_lending_risk
stevenllerner
 
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.
 
MSc research project report - Optimisation of Credit Rating Process via Machi...
MSc research project report - Optimisation of Credit Rating Process via Machi...MSc research project report - Optimisation of Credit Rating Process via Machi...
MSc research project report - Optimisation of Credit Rating Process via Machi...
AmarnathVenkataraman
 
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)

Overview of Data Analytics in Lending Business
Overview of Data Analytics in Lending BusinessOverview of Data Analytics in Lending Business
Overview of Data Analytics in Lending Business
 
Market Practice Series (Credit Losses Modeling)
Market Practice Series (Credit Losses Modeling)Market Practice Series (Credit Losses Modeling)
Market Practice Series (Credit Losses Modeling)
 
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
 
Project Report - Acquisition Credit Scoring Model
Project Report - Acquisition Credit Scoring ModelProject Report - Acquisition Credit Scoring Model
Project Report - Acquisition Credit Scoring Model
 
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
 
Predicting Delinquency-Give me some credit
Predicting Delinquency-Give me some creditPredicting Delinquency-Give me some credit
Predicting Delinquency-Give me some credit
 
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
 
Creditscore
CreditscoreCreditscore
Creditscore
 
Credit scorecard
Credit scorecardCredit scorecard
Credit scorecard
 
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
 
Credit Risk Analytics
Credit Risk AnalyticsCredit Risk Analytics
Credit Risk Analytics
 
SP Five FF. ICBA handouts
SP Five FF. ICBA handoutsSP Five FF. ICBA handouts
SP Five FF. ICBA handouts
 
Introduction to predictive modeling v1
Introduction to predictive modeling v1Introduction to predictive modeling v1
Introduction to predictive modeling v1
 
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...
Negotiation Strategies: Using Game Theory and Decision Tree Analysis to Deter...
 
Sas credit scorecards
Sas credit scorecardsSas credit scorecards
Sas credit scorecards
 
Forecasting peer to_peer_lending_risk
Forecasting peer to_peer_lending_riskForecasting peer to_peer_lending_risk
Forecasting peer to_peer_lending_risk
 
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...
 
MSc research project report - Optimisation of Credit Rating Process via Machi...
MSc research project report - Optimisation of Credit Rating Process via Machi...MSc research project report - Optimisation of Credit Rating Process via Machi...
MSc research project report - Optimisation of Credit Rating Process via Machi...
 
Data analytics in finance broucher
Data analytics in finance broucher Data analytics in finance broucher
Data analytics in finance broucher
 

Viewers also liked

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
Magnify Analytic Solutions
 
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
Magnify Analytic Solutions
 
Quantifying the Buzz Effect
Quantifying the Buzz Effect Quantifying the Buzz Effect
Quantifying the Buzz Effect
Magnify Analytic Solutions
 
Model Validation
Model Validation Model Validation
Model Validation
Magnify 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
 
Leading & Lagging Indicators in SAS
Leading & Lagging Indicators in SAS Leading & Lagging Indicators in SAS
Leading & Lagging Indicators in SAS
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 Research
Magnify Analytic Solutions
 
Three "Real Time" Analytics Solutions
Three "Real Time" Analytics Solutions Three "Real Time" Analytics Solutions
Three "Real Time" Analytics Solutions
Magnify 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)

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
 
Quantifying the Buzz Effect
Quantifying the Buzz Effect Quantifying the Buzz Effect
Quantifying the Buzz Effect
 
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 DMA presentation 2014

Magnify DMA presentation 2014
Magnify DMA presentation 2014Magnify DMA presentation 2014
Magnify DMA presentation 2014Keith Shields
 
Introduction to XM.pptx
Introduction to XM.pptxIntroduction to XM.pptx
Introduction to XM.pptx
Jayce32
 
Lecture 3 Customer Relationship Management
Lecture 3 Customer Relationship ManagementLecture 3 Customer Relationship Management
Lecture 3 Customer Relationship Management
Ali Noman
 
Types of crm
Types of crmTypes of crm
Types of crm
PROF.JITENDRA PATEL
 
The effect of CRM- short
The effect of CRM-  shortThe effect of CRM-  short
The effect of CRM- short
Ahmed Moussa
 
CRM - Customer Relationship Management
CRM - Customer Relationship ManagementCRM - Customer Relationship Management
CRM - Customer Relationship Management
vinaya.hs
 
HWZ-Darden Konferenz: Building a Sustainable Analytics Orientation
HWZ-Darden Konferenz: Building a Sustainable Analytics OrientationHWZ-Darden Konferenz: Building a Sustainable Analytics Orientation
HWZ-Darden Konferenz: Building a Sustainable Analytics Orientation
HWZ Hochschule für Wirtschaft
 
MEDDPICC Guide to Smarter Deal Coaching.pdf
MEDDPICC Guide to Smarter Deal Coaching.pdfMEDDPICC Guide to Smarter Deal Coaching.pdf
MEDDPICC Guide to Smarter Deal Coaching.pdf
The Brevet Group
 
Customer relationship management in banking sector
Customer relationship management in banking sectorCustomer relationship management in banking sector
Customer relationship management in banking sector
Vivekanandha College of arts and Science for Women (Autonomous)
 
Crm at big bazaar
Crm at big bazaarCrm at big bazaar
Crm at big bazaarNeel Shyam
 
Module i
Module iModule i
Crm in axis bank stage 2 mid-review of the project
Crm in axis bank  stage 2 mid-review of  the projectCrm in axis bank  stage 2 mid-review of  the project
Crm in axis bank stage 2 mid-review of the projectRajnish Dubey
 
Effectiveness of CRM programme in sbi
Effectiveness of CRM programme in sbiEffectiveness of CRM programme in sbi
Effectiveness of CRM programme in sbi
Eguardian India
 

Similar to Magnify DMA presentation 2014 (20)

Magnify DMA presentation 2014
Magnify DMA presentation 2014Magnify DMA presentation 2014
Magnify DMA presentation 2014
 
Introduction to XM.pptx
Introduction to XM.pptxIntroduction to XM.pptx
Introduction to XM.pptx
 
Lecture 3 Customer Relationship Management
Lecture 3 Customer Relationship ManagementLecture 3 Customer Relationship Management
Lecture 3 Customer Relationship Management
 
Types of crm
Types of crmTypes of crm
Types of crm
 
The effect of CRM
The effect of CRMThe effect of CRM
The effect of CRM
 
The effect of CRM- short
The effect of CRM-  shortThe effect of CRM-  short
The effect of CRM- short
 
CRM - Customer Relationship Management
CRM - Customer Relationship ManagementCRM - Customer Relationship Management
CRM - Customer Relationship Management
 
HWZ-Darden Konferenz: Building a Sustainable Analytics Orientation
HWZ-Darden Konferenz: Building a Sustainable Analytics OrientationHWZ-Darden Konferenz: Building a Sustainable Analytics Orientation
HWZ-Darden Konferenz: Building a Sustainable Analytics Orientation
 
MEDDPICC Guide to Smarter Deal Coaching.pdf
MEDDPICC Guide to Smarter Deal Coaching.pdfMEDDPICC Guide to Smarter Deal Coaching.pdf
MEDDPICC Guide to Smarter Deal Coaching.pdf
 
Customer relationship management in banking sector
Customer relationship management in banking sectorCustomer relationship management in banking sector
Customer relationship management in banking sector
 
Crm
CrmCrm
Crm
 
Crm at big bazaar
Crm at big bazaarCrm at big bazaar
Crm at big bazaar
 
3e779 Module I
3e779 Module I3e779 Module I
3e779 Module I
 
Ibm crm
Ibm crmIbm crm
Ibm crm
 
Ibm crm
Ibm crmIbm crm
Ibm crm
 
CRM
CRMCRM
CRM
 
Module i
Module iModule i
Module i
 
Crm in axis bank stage 2 mid-review of the project
Crm in axis bank  stage 2 mid-review of  the projectCrm in axis bank  stage 2 mid-review of  the project
Crm in axis bank stage 2 mid-review of the project
 
Effectiveness of CRM programme in sbi
Effectiveness of CRM programme in sbiEffectiveness of CRM programme in sbi
Effectiveness of CRM programme in sbi
 
Crm ppt
Crm pptCrm ppt
Crm ppt
 

Recently uploaded

Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTESAdjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
Subhajit Sahu
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
balafet
 
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
ewymefz
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
nscud
 
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
ukgaet
 
SOCRadar Germany 2024 Threat Landscape Report
SOCRadar Germany 2024 Threat Landscape ReportSOCRadar Germany 2024 Threat Landscape Report
SOCRadar Germany 2024 Threat Landscape Report
SOCRadar
 
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
slg6lamcq
 
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
ewymefz
 
一比一原版(NYU毕业证)纽约大学毕业证成绩单
一比一原版(NYU毕业证)纽约大学毕业证成绩单一比一原版(NYU毕业证)纽约大学毕业证成绩单
一比一原版(NYU毕业证)纽约大学毕业证成绩单
ewymefz
 
Empowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptxEmpowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptx
benishzehra469
 
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
ewymefz
 
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
vcaxypu
 
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdfSample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Linda486226
 
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Subhajit Sahu
 
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdfCh03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
haila53
 
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
axoqas
 
一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单
enxupq
 
FP Growth Algorithm and its Applications
FP Growth Algorithm and its ApplicationsFP Growth Algorithm and its Applications
FP Growth Algorithm and its Applications
MaleehaSheikh2
 
Q1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year ReboundQ1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year Rebound
Oppotus
 
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
一比一原版(UofS毕业证书)萨省大学毕业证如何办理一比一原版(UofS毕业证书)萨省大学毕业证如何办理
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
v3tuleee
 

Recently uploaded (20)

Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTESAdjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
 
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
 
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
 
SOCRadar Germany 2024 Threat Landscape Report
SOCRadar Germany 2024 Threat Landscape ReportSOCRadar Germany 2024 Threat Landscape Report
SOCRadar Germany 2024 Threat Landscape Report
 
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
 
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单
 
一比一原版(NYU毕业证)纽约大学毕业证成绩单
一比一原版(NYU毕业证)纽约大学毕业证成绩单一比一原版(NYU毕业证)纽约大学毕业证成绩单
一比一原版(NYU毕业证)纽约大学毕业证成绩单
 
Empowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptxEmpowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptx
 
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单
 
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
 
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdfSample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
 
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
 
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdfCh03-Managing the Object-Oriented Information Systems Project a.pdf
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
 
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
 
一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单一比一原版(YU毕业证)约克大学毕业证成绩单
一比一原版(YU毕业证)约克大学毕业证成绩单
 
FP Growth Algorithm and its Applications
FP Growth Algorithm and its ApplicationsFP Growth Algorithm and its Applications
FP Growth Algorithm and its Applications
 
Q1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year ReboundQ1’2024 Update: MYCI’s Leap Year Rebound
Q1’2024 Update: MYCI’s Leap Year Rebound
 
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
一比一原版(UofS毕业证书)萨省大学毕业证如何办理一比一原版(UofS毕业证书)萨省大学毕业证如何办理
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
 

Magnify DMA presentation 2014

  • 1. Extracting ROI From The Engaged Customer A Portfolio Management Approach to CRM Keith Shields Laura Benard Jen Boyer Chief Analytics Officer Client Services Director Marketing Strategy Manager Magnify Analytic Solutions Magnify Analytic Solutions Ford Motor Company 10/28/2014
  • 2. Q. How related are the following two topics? Consumer Loan Management VS. CRM Marketing 1
  • 3. Q. How related are the following two topics? Consumer Loan Management VS. CRM Marketing a. Not At All Related 2
  • 4. Q. How related are the following two topics? Consumer Loan Management VS. CRM Marketing b. Somewhat Related 3
  • 5. Q. How related are the following two topics? Consumer Loan Management VS. CRM Marketing c. Very Related 4
  • 6. Both Consumer Loan Managers and CRM Managers… Start with a portfolio of customers Have access to enormous amounts of customer data Can manage their portfolio using predictive analytics Both are responsible for the long term value of their assets 5
  • 7. Why is this an important discussion for us? We can apply learnings from the Consumer Loan Industry to manage our customer portfolios… Spending on Marketing Analytics is expected to increase 72% over the next 3 years* Only 32% of marketing projects use analytics Most companies do not have the right talent to fully leverage Marketing Analytics “…77% of marketers surveyed believe data on customer purchase histories can improve marketing ROI, yet only 21% actually use it. Likewise, 88% believe behavioral data can do the same, but only 20% use it” Big Data and new tools are quickly changing this landscape *2014 February; The CMO Survey.org Highlights and Insights 6
  • 8. CONCEPTUAL NONSENSE FROM THE SCIENTIST…
  • 9. Defining “Portfolio Management” A portfolio of consumer loans, not securities. • Portfolio Management, loosely, is the application of a set of analytically-driven collections and servicing techniques aimed at forecasting and maximizing a loan portfolio’s cash flows. • Quantifying credit risk and predicting future payment is at the heart of portfolio management. • Credit Risk and CRM seemingly dominate the Big Data landscape. Next slide… 7
  • 10. “Big Data” infects the CRM and Credit Risk disciplines more than almost any other… Why the pervasive interest in Big Data? • Largely to satisfy CRM and credit risk needs… Data: Information Week Analytics, Business Intelligence and Information Management Survey of 417 business technology professionals at companies using or planning to deploy data analytics, BI or statistical analysis software, October 2012 CREDIT RISK NEEDS CRM NEEDS 8
  • 11. (Re)Defining CRM A portfolio of customers, not loans. • Portfolio Management CRM, loosely, is the application of a set of analytically-driven collections and servicing marketing techniques aimed at forecasting and maximizing a loan portfolio’s customers’ cash flows purchases. • Quantifying credit defection risk and predicting future payment purchases is at the heart of portfolio management CRM. • Incidentally, the Wikipedia definition of CRM is: • CRM is a system for managing a company’s interactions with current and future customers. It involves using technology to organize, automate and synchronize sales, marketing, customer service, and technical support. 9
  • 12. Portfolio Management and CRM… From an Analytics perspective, these are the same. The only difference lies in the target variable and predictors. • PD = 1 / (1+e-z), where z = A + Bx1 + Cx2 + Dx3 + … • Portfolio Management: • PD = Probability of DEFAULT • x1 = credit score, x2 = days past due, x3 = loan to value ratio, etc… • CRM: • PD = Probability of DEFECTION • x1 = prior purchases, x2 = months since last purchase, x3 = unfavorable tweets, etc… 10
  • 13. Others have recognized and leveraged the overlap… 11 • Auto Pre-Approval • Merchant Cash Advance and Small Business Loans • Pier-to-Pier lending • Student loan servicing • Business Rules Engines
  • 14. The Portfolio Management Paradigm 1 4 Managing a loan portfolio requires that we turn impaired (high credit risk) loans into cash-flowing bonds… Customer’s loan is rewritten for empirically-derived optimal amount CASH FLOWS Customer makes partial payment $A1 $A2 Loan impaired, collections calls ensue Customer pays off rewritten balance Time t=0 t=1 t=3 • 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. 12
  • 15. Adopting the PM paradigm for CRM… 1 5 An engaged customer is a bond. The effectiveness of our CRM strategies determines the yield of that bond. 1 5 Customer comes in for service Customer visits company website $A1 CASH FLOWS $A2 Customer signs up for rewards program Customer purchases a new vehicle Time t=0 t=1 t=3 • The value of this “bond” (customer) is $A1/(1+i)1 + $A2/(1+i)3 • Customer Lifetime Value (CLV) models help quantify the value of customer behaviors and CRM tactics. The success of CRM can be measured by the extent to which CLV increases, irrespective of test-control results. 13
  • 16. Marketers already recognize the need to view their customer base as a portfolio… Types of Data that Marketers Worldwide Would Like to Add to Their Customer Data Profile 42% 42% 14 19% 19% 14% 12% 24% 35% 45% 53% 71% Predictive analytics around lifetime… Online customer profile Customer service feedback User survey and preference data Social media data Third-party demographic data Sales executive insights Finance / customer payment data Order history Analyting is better than what we… 0% 10% 20% 30% 40% 50% 60% 70% 80% In-store / agent exchanges (1Q2013) Source: CMO Council and SAS % of respondents • 71% of marketers want “predictive analytics around lifetime value” added to their customer data profiles… • Lifetime value models are nothing more than a forecast of cash flows at the customer level… • Survival analysis, vintage-level monitoring, and other popular PM disciplines are a must…
  • 17. But CRM trails Credit Risk / Portfolio Management in the adoption of Big Data…why? “…77% of marketers surveyed believe data on customer purchase histories can improve marketing ROI, yet only 21% actually use it. Likewise, 88% believe behavioral data can do the same, but only 20% use it” 15 • Regulation • Accountability is “fuzzy” • Metrics are inexact and not directly reflective of behavior. • Secondary markets • What would CRM analytics look like if marketers were forced to buy, sell, and “value” their customer portfolios? • Metrics are inexact and not directly reflective of behavior.
  • 18. What PM practices will help our CRM? • Take a longitudinal view of the customer. This is the only way to get an accurate outlook and valuation. Implies a need for a CLV model… 16 • CLV = p(sale at time 1)*E($ profit from sale) / (1 + d)1 + p(sale at time 2)*E($ profit from sale) / (1 + d)2 + p(sale at time 3)*E($ profit from sale) / (1 + d)3 + … • Engagement is measured longitudinally; enticement is measured cross-sectionally. • Quantify the impact of “mix shift” on outcomes of interest. • Establish “regulatory-like” rigor around model validation. • Understand that the two share not only a brain, but also a nervous system. Next slide…
  • 19. ENOUGH CONCEPTUAL NONSENSE FROM THE SCIENTIST. NOW SOME PRACTICAL STUFF THE MARKETER…
  • 20. Does This Change the Way We Practice CRM? 17 We think so, especially in the following areas: Measuring Success Metrics should be more bottom-line oriented and exact Shift from basing success solely on campaign performance to understanding performance of the portfolio Predicting Outcomes Predictions should go beyond the “next transaction” All available data should be leveraged to proactively manage customers throughout the lifecycle to desired business objectives Influencing Behavior CRM becomes our “sand box” for going beyond understanding just correlations; to understanding causation as a way to change customer behavior
  • 21. Predicting Outcomes 18 Transaction vs. Portfolio Management approach to predicting outcomes…. % In-Market Short-Term: Optimizing campaign performance to campaign objectives Segment Size Opportunity Longer-Term: Enables management of entire portfolio to business objectives (i.e. increasing CLV)
  • 22. Influencing Behavior Test and learn approach will determine how we influence and change the long term health of our customer portfolio… Monitor drivers across the portfolio… Design treatments, messaging and investment based on customer value, individual customer drivers and predicted outcome Understand Drivers of Desired Outcomes Every CRM treatment should be analytically driven…ensuring that every CRM dollar spent is working to move the customer into a more valuable state 19
  • 23. Measuring Success Strategic Operational Tactical Portfolio Health What is the value of my customer portfolio? What is the mix and risk of my customer portfolio? Performance and Forecasting Do I understand both rear-ward and forward-looking performance? What is the aggregate impact of our CRM initiatives on improving sales? Dashboard and Diagnostics Which champion vs. challenger campaign performs best? Which actions influence customer outcomes both positive and negative? 20
  • 24. Thus Ends the Prepared Remarks… • 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. 21 • 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.
  • 25. “JUDGE A MAN BY HIS QUESTIONS RATHER THAN HIS ANSWERS.” -- VOLTAIRE