Vision 2014: The Evolving Landscape of Customer Management
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Transcript of Vision 2014: The Evolving Landscape of Customer Management
© 2014 Experian Information Solutions, Inc. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of Experian Information Solutions, Inc.
Other product and company names mentioned herein are the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in
any form or manner without the prior written permission of Experian. Experian Public.
The evolving landscape of customer management
Gordon Cameron PNC
Scott Henry Experian
#vision2014
2 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Customer management
Why are we talking about it today?
What is customer management?
Extracting value from customer information
More data … Greater ability to
use it …
Expanding core of
business …
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Customer management elements
1
2
3 4
5
Data
Listen and
detect
Decision
management Offer
presentation
Feedback
loop
Enterprise customer data
Unique ID Payment history
Transactions Eligible offers
Bureau data Response history
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Went into
overdraft
Card application
declined
Today: Student
Customer management use case
Your credit score is
important as you get on
with your life. Use Credit
Tracking to know how
you are doing.
Responding to life events for added value
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Customer management use case
Congratulations, you
now qualify for our
special graduate credit
card offer!
Your credit score is
important as you get on
with your life. Use Credit
Tracking to know how
you are doing.
Responding to life events for added value
Went into
overdraft
Card application
declined
Today: Student
Rents
apartment
Income
increase
Card application
accepted
6 months: College graduate Today: Student
6 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Customer management use case
Your credit score
has increased!
Well done!
Congratulations, you
now qualify for our
special graduate credit
card offer!
Your credit score is
important as you get on
with your life. Use Credit
Tracking to know how
you are doing.
Now that you have
a new job, we’d
like to increase
your credit limit!
Responding to life events for added value
Went into
overdraft
Card application
declined
Today: Student 6 months: College graduate
Rents
apartment
Income
increase
Card application
accepted
Accepts
job offer
New job,
more income
Furnishing
apartment
Limit increase
offer accepted
On time
payments
12 months: Starting career 6 months: College graduate
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Optimize current business and extend customer relationships
Using unique customer insight, grow
outside of your traditional business:
Market external propositions
Enter adjacencies
Increase cross sell revenue through
lifestyle triggers
Optimize offer pricing
Reduce turnover through optimising
attrition models
Enhance pre-delinquency models
Improve current
business
REFINE
Extend customer
relationships
TRANSFORM
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Customer management
in financial services
Gordon Cameron
PNC
Executive Vice President,
Chief Consumer Credit Officer
9 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Bulk mailings
Large, difficult to
access systems
Smaller banks still
relying on paper
Drive to digital
Consolidated
marketing files –
moving toward
integrated customer
information systems
Bank mergers
Integrated systems
Large institutions
working to digest
acquisitions
Big Data talk track
Understanding the
New Normal of
consumers,
competition and
regulation
1990s Early 2000s Late 2000s 2010s
Large scale technology investment as a tier differentiator
Understanding customers is levelling the playing field
Financial services timeline
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The goal is to turn customer data into actionable insight that drives a scalable, sustainable and compliant customer relationship agenda
Harvesting insights and opportunities within the customer base
R A P I D LY E V O LV I N G C O N S U M E R
Macro environmental changes
Household balance sheets
Demographics
The economy
B E S T D AT A I S H A R D T O
A N A LY Z E
Deposit account data
Transaction data online and
credit card
Customer “career path”
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The big picture of consumer balance sheets
Home prices are
stable to increasing
across most markets
Household debt ratios
are returning to long term
trend line
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Demographics will shape the customer landscape profoundly over the next half decade
Estimated Change in Population Distribution w ith Current Spend
Distribution
-20%
-10%
0%
10%
20%
30%
40%
50%
<25 25-34 35-44 45-54 55-64 65-74 >=75
Age Group
Delta 2010/2020
Population
DistributionSpend Distribution
Average Score at Time of Inquiry
620
640
660
680
700
720
740
760
780
Overall Auto Mortgage Bankcard
Asset Class
Sco
re
2006 2010 2013
Demographic changes are
shaping the future of
spending and consumption
Credit seeking customers are
returning to long term trends
after a period of austerity
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Income segmentation Using DDA data to its best potential
Challenge
Identify patterns in transaction data that we did not know before, or we could not see
before, that can help increase customer value
Analytic solution Test and learn approach
Customer behavior analysis
Segmentation analysis of DDA
customers by unique
deposit/income streams
Development of triggers based on
behavioral changes
DDA based models for income
predictions
Outcomes:
Significant predictive value
in analyzing the
transactions and their
associated footprints
Better understanding of
Risk, depth of customer
relationship profile and a
set of tactics to address
changes
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Transaction level data
Point of sale /
payments
Merchant
Geography /
proximity
Amount
Web
Credit cards
Point of sale
ATM
Internet transactions
Debit cards
ACH payments
ACH deposits
Income statements
Purchases
Checking accounts
Open lines of credit
New loan
originations
Payments
Other loans
Teller transactions
Platform visits
Branch
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Transaction level analytic techniques
The periodic nature of deposit and
payment information can be
understood through non traditional
techniques like Fourier series
Borrowing from other disciplines we
can determine appropriate signal
filtering mechanisms (example:
Schmitt triggers and hysteresis)
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Categorization of customer behavior
Comb Shaped Filter
0
200
400
600
800
1000
1200
1400
1600
Auto
Payment
CreditCard Mortgage Credit Card Cable/phone Cell Phone Insurance Transfer to
savings
0
X 4 X 3 X 1 X 2
XX 3 4 XX 2 4 XX 1 4 XX 2 3 XX 1 3 XX 1 2
XXX 1 2 3 XXX 2 3 4
1
XXX 1 2 4 XXX 1 3 4
Comb shaped filtering provides
a baseline of behavior and
information-theoretic
compression opportunities
The customer “career path”
creates something similar to a
Boolean Lattice structure and
provides deep insight in to
profit potential and product
offer cadence
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DNA – The core building blocks
Dynamic Needs-based Attributes (DNA)
Leverages position and momentum
rather than static profiling
Multiple objective functions
Attempts to understand and identify
changes in behavior more rapidly as
well as estimate likely future changes
This is the foundation around which we create strategies
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Putting it all together
1. Algorithms to identify consumer income streams predictive metrics
Number of separate, concurrent streams of deposits (pay schedules), work
related/non work related by customer married to outflows
2. Consumer segmentation based on transaction streams and behavior. Estimations of
impact on liquidity, risk, appetite for new products, depth of relationship
3. Development of triggers based on changes to customers deposit streams
Examples of new learnings
Not all deposit relationships have robust signals
Customers with multiple stable deposit streams
are more likely to have deeper relationship
Detecting changes in income triggers can be
early signals of attrition risk
Customer risk levels strongly correlated to
number of deposit streams into DDA account
DDA income can be predicted via deep consumer income streams analysis
Analytic approach
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
No ACH $ No StableDep Streams
1 2 3+
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