©2014 Experian Information Solutions, Inc. All rights reserved. Experian and the marks used herein are service marks or re...
2©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Customer management
Why are we talking a...
3©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Customer management elements
1
2
34
5
Da...
4©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Went into
overdraft
Card application
dec...
5©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Customer management use case
Congratulat...
6©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Customer management use case
Your credit...
7©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Optimize current business and extend
cus...
8©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Customer management
in financial service...
9©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
 Bulk mailings
 Large, difficult to
ac...
10©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
The goal is to turn customer data into ...
11©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
The big picture of consumer balance she...
12©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Demographics will shape the customer la...
13©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Income segmentation
Using DDA data to i...
14©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Transaction level data
 Point of sale ...
15©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Transaction level analytic techniques
T...
16©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Categorization of customer behavior
Com...
17©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
DNA – The core building blocks
Dynamic ...
18©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Putting it all together
1. Algorithms t...
19©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
For additional information, please cont...
20©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Visit the Experian Expert Bar to learn ...
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Vision 2014: The evolving landscape of customer management

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Expanding data and the need to identify and act on the value of greater customer information have significantly affected customer decisioning. Cross-selling, exposure management, risk-based pricing and relevancy scoring can all be refined with the power of greater customer information — and new revenue streams can be created.
However, effectively harnessing this information requires an integrated approach to data, customer and analytics processes.
This session will provide Experian’s point of view on the direction many of our key accounts are looking to in next-generation decisioning.

Published in: Economy & Finance, Business
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Vision 2014: The evolving landscape of customer management

  1. 1. ©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. 2. 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 …
  3. 3. 3©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. Customer management elements 1 2 34 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
  4. 4. 4©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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
  5. 5. 5©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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 graduateToday: Student
  6. 6. 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 career6 months: College graduate
  7. 7. 7©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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
  8. 8. 8©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. Customer management in financial services Gordon Cameron PNC Executive Vice President, Chief Consumer Credit Officer
  9. 9. 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
  10. 10. 10©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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 ATA 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”
  11. 11. 11©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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
  12. 12. 12©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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 Distribution Spend Distribution Average Score at Time of Inquiry 620 640 660 680 700 720 740 760 780 Overall Auto Mortgage Bankcard Asset Class Score 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
  13. 13. 13©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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
  14. 14. 14©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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
  15. 15. 15©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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)
  16. 16. 16©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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 X4 X3 X1X2 XX3 4 XX2 4 XX1 4 XX2 3 XX1 3 XX1 2 XXX1 2 3XXX2 3 4 1 XXX1 2 4XXX1 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
  17. 17. 17©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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
  18. 18. 18©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. 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 Stable Dep Streams 1 2 3+
  19. 19. 19©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. For additional information, please contact: Scott.Henry@experian.com Hear the latest from Vision 2014 in the Daily Roundup: www.experian.com/vision/blog @ExperianVision | #vision2014 Follow us on Twitter
  20. 20. 20©2014 Experian Information Solutions, Inc. All rights reserved. Experian Public. Visit the Experian Expert Bar to learn more about the topics and products covered in this presentation.

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