P Martino, Rich 071408

Copyright U.S. Bank N.A. 2008

TAKING CUSTOMER INSIGHT TO THE BANK

How the Right Consumer Metrics Act as the Backbone of a Successful Marketing Program

Rich MartinoSenior Vice President

Market Information & Research


U.S. Bank - Five Star Service Guaranteed

• 6th largest U.S. commercial bank• Assets: $242 billion• Deposits: $138 billion• Loans: $156 billion• Branches: 2,522• ATM’s: 4,844

• 13 million consumer customers• 1 million business customers

As of March 31, 2008


Moving from thinking to knowing

Analytical competitors use analytics extensively and systematically to out-think and out-execute the competition

Adapted from Competing on Analytics, Davenport & Harris, Harvard Business School Press, 2007


Developing as an Analytical Competitor

NoneGet accurate data to improve operations

Stage 1Analytically impaired

ROI of individual applications

Use analytics to improve one or more functional activities

Stage 2Localized analytics

Future performance and market value

Use analytics to improve a distinctive capability

Stage 3 Analytical aspirations

An important driver of performance and value

Build broad analytic capability – analytics for differentiation

Stage 4Analytical companies

The primary driver of performance and value

Analytical master – fully competing on analytics

Stage 5Analytical competitors

Metrics/Measure/ValueObjectiveStage

Adapted from Competing on Analytics, Davenport & Harris, Harvard Business School Press, 2007


Unique retail relationship management platform

Single View of the Customer

Banker Leads Alerts & Sales Tools

Analytics & Customer Decisions

• Analytics• Campaign Management• Behavior Triggers• Relationship management strategies

Performance Improvement

• Sales force preparation• Sales management routines• Closed-loop feedback

Customer

Banker


One of the first integrated customer decision systems

• Deposit Authorizations

• Sales & Marketing

• Proactive & Reactive Retention

• Risk Management & Credit Underwriting

• Collection Strategies

• Account Management

• Credit Authorizations

• Inputs from all major legacy systems

• Each customer is evaluated monthly

• Enhanced with demographic and credit bureau information

• Daily “triggers” enable more frequent customer evaluations

• Champion-Challenger testing


High Value High Value High Value Medium Value Medium Value Medium Value Low Value Low Value Low ValueHigh Risk High Attrition High Risk High Attrition High Risk High Potential

Champion TC 1 TC 2 TC 3 TC 4 TC 5 TC 6 TC 7 TC 8 TC 9

Challenger 1 TC 10 TC 11 TC 12 TC 13 TC 14 TC 15 TC 16 TC 17 TC 18

Challenger 2 TC 19 TC 20 TC 21 TC 22 TC 23 TC 24 TC 25 TC 26 TC 27

Value

Credit Risk

Credit Risk

Credit Risk

Attrition Risk

Attrition Risk

Potential

Customers segmented based on value, risk & future potential


Rapid strategy evolution drives strategy revisions

Create Hypotheses

Defend Deepen

BuildImprove

Strategic Framework

Design Tests

Observe Behavior

Measure Results

Champion / Challenger Structure

Performance Period Evaluation

Upgrade Treatment Strategy and Create New Hypotheses


The average value of core retail customers has increased

Comparison of CRV by Demi-Deciles 1994 - Current

1994 (Inflation Adjusted) Current

Excludes Credit card only, Mortgage only and Installment Loan only customers

Overall average customer value up 144%


The top 20% of customers deliver over 80% of retail customer value

20%

-4%

60%

20%

20%

84%

Percent of Customers Percent of Value

Desired Customers

Break-even Customers

Costly Customers


20%

18% 19%

13%

30%

Deposit Heavy Credit Heavy Multi-Product Transaction Based All Other HV

High Value customers can be further segmented

Deposit Balance 284Credit Balance 0

CRV 59Products Owned 3.9









Values are indexed


Suite of over 70 predictive models for deeper segmentation

• Consumer Propensity to Buy

• Small Business Propensity to Buy

• Consumer Customer Attrition

• Product Level Attrition

• Consumer Campaign Response Models

• Activation / Utilization

• Expected Next Year Value

• RMS Risk Scores – Account Management

• RMS Risk Scores – Origination


Generalizing insights about financial services consumption

LiSA – Life Stage & Affluence Segments

Households whose financial priorities are focused on making ends meet41%Balancing Act

Retirees and those preparing to retire – this group needs to invest wisely to ensure a sound future

30%Golden Years

Households with families and money to invest and borrow9%Kids First

Younger households without children but with growing financial assets to manage

12%Accumulators

People of any age who have achieved a high degree of financial success4%Upscale Investors

Base: U.S. Households


Multiple segmentation approaches offer rich insights

Value SegmentsCalculating the current value of customer relationships and projecting potential.

Life Stage & Affluence Segments (LiSA)Dimensioning consumer households based on their financial product consumption potential.

Near-term Opportunity Segments

Employing predictive models to score customers on their propensity to buy or attrite.

Treatment SegmentsGrouping customers into segments for managing day-to-day business processes


Using insights to shape the customer experience

Internet Banking (New!)

Contact Center

Personal Bankers

Tellers U.S. Bank ATM (New!)

Behavioral Insights Predictive Analytics Relationship Strategies

Identifying out-of-pattern behaviors that may signal

need needs

Evaluating over 13 million consumers

Converting insights into decisions

Prioritized offers and consistent treatment for each customer

“Conversational Data”about customers’

financial objectives & existing relationships


BLAST enables a shift to relationship-based selling

Delivers timely information on consumer and small business customers

Delivers Banker Alerts (trigger and event detection) and targeted lead lists.

Online financial profiles allow bankers to work with customers to identify their financial objectives


Blending marketing analytics with needs-based sales skills

Model score: 248LiSA Segment:

“Kids First”

Need: Reduce Debt Service

The AndersonsModel score: 249LiSA Segment:

“Upscale Investor”

Need: Smooth Cash Flow

Michael M.

“Conversational Data”allows us to tailor the product benefits and

selling process

Mr. Anderson, I’d like to show you how we

can use a home equity loan to reduce your monthly payments.

Michael, since nearly half of your income

comes from bonuses, a home equity line could improve your

cash flow.

Banker


Banker use of BLAST is still trending upward

Monthly BLAST Contacts All Lead Types

20,7

83

19,8

33 28,2

24

26,2

02

62,8

48

50,3

57

42,2

00

43,9

30

48,1

64

75,8

44

72,3

19

90,6

84

82,3

75

65,2

96

86,0

48

117,

057

131,

318 15

5,05

9

150,

089

146,

428

148,

321 16

9,50

9

150,

816

193,

323

166,

433

178,

532

219,

060

193,

881

224,

480

234,

865

NovDec Ja

nFeb Mar AprMay Ju

n Jul

AugSep

tOctNovDec Ja

nFeb

Mar AprMay Ju

n Jul

AugSep OctNovDec Ja

nFeb Mar Apr


Every execution is a learning opportunity

Methodology

• New Accounts are tracked for Targeted Marketing lists and Reactive Marketing (“pops”). 60 day observation period.

• Balance changes are tracked for Banker Alerts, Retention Leads and Relationship Leads. Results are refreshed monthly.

• Contacted customers are compared to control groups to identify performance differences.

• Significance testing eliminates “noise”

• Only incremental differences that are statistically significant are included in benefit stream.

• Results are input to financial model and Cap Ex system for accountability


Source of Revenue by Contact Strategy

Results are measured with focus on incremental gains

BLAST Banker Alert Contacts by Month

Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

Banker Alerts 32%

Targeted Marketing

28%

Reactive Marketing

17%

Sources of Incremental Revenue

Business Deposits22%

Business Loans9%

Consumer Deposits31%

Consumer Loans22%

Credit Cards17%

Relationship Marketing

23%

Adjusted for NEA, etc

Incremental Revenue Forecast

2004 2005 2006 2007 2008 2009 2010

Dol

lars

in M

illio

ns


Treatment Strategies

Marketing Actions

Retention Tactics

Capital Allocation

Measures & Metrics

Approve this loan?

Pay this overdraft?

Authorize this transaction?

Next product to market?

Out of pattern transactions?

Activate or deepen relationship?

Likely to attrite?

Appropriate concessions?

Meaningful potential?

Expand or contract?

Need a new branch?

Channel investments?

Increasing return on customer?

Champion or challenger?

Achieving strategic objectives?

“Conversational Data”

Marketing Analytics Data

Leveraging customer data for multiple uses


Creating new metrics - Predicting branch performance

Predicted branch sales potential for 6 products explained by driver category

24%14% 17%

10% 12%

22%

20% 14%

6%13%

13%

20%

20% 25%29%

25%

9%

11%

22%

6%

8%

25%35%

48%37%

58%

31%

23%

Product A

(Units)

2%

3%

Other

Competition

Bankstrategy

Branchattributes

Wealth/Demographic

Product B

(Units)

Product C

(Dollars)

Product D

(Dollars)

Product E

(Dollars)

Product F

(Dollars)


Multiple factors used to group branches into clusters based on market opportunity and sales potential

ILLUSTRATIVE

• Clusters will be used to create to set targets based on market opportunity

• Like-branches can share best practices

• Branches can be compared with other peer branches within the Bank as well as Industry peers

• Ultimately clusters can be used in experimental design


A few closing thoughts…

• Analytical competitors try to improve the odds

• With scale, small incremental improvements can yield significant financial gains

• Look for answers across your analytical portfolio

• The stochastic process is, by definition, backward looking – don’t get trapped in the past

• Long-term winners create value for their customers… the ultimate accountability in marketing


Questions ?

P Martino, Rich 071408

Business

Transcript of P Martino, Rich 071408