Death of a Salesman: Account Acquisition in a New Environment

Death of a Salesman: Account Acquisition in a New Environment

April 2, 2013

Death of a Salesman: Account Acquisition in a New Environment

April 2, 2013

Zoot Enterprises, Inc. Proprietary & Confidential Information.

2

Tom JohnsonVice President, Strategic Alliances

Zoot Enterprises

Ed O’BrienDirector Banking Channels

Mercator Advisory Group

Keith Shields Chief Analytics Officer, Magnify Chief Credit Officer, Loan Science

3

• Industry Overview• More Intelligent Decisions through Analytics• Next Generation Account Acquisition• Q & A

AGENDA

4

CHANGING MARKET CONDITIONS SIGNIFICANTLY

IMPACT FI GROWTH STRATEGIES

• Financial Institutions are under intense pressure to perform, even though the business fundamentals are challenging• Reduced fee income• Increased costs• Reduced revenues, net interest income, and profitability

• FIs are facing intense pressure to increase their financial performance throughout their LOBs and throughout their portfolios

• They need to find ways to profitably grow their portfolios in new and creative ways

5

CONSUMERS ARE CONSOLIDATING THE NUMBER

OF INSTITUTIONS THEY USE

1.41.6

1.21.31.21.4

1.2

2.3

1.4 1.41.41.41.21.21.31.2

2.1

1.6

4.8

2.72.5

2.7

2.12

1.6

2.6

1.6

OtherOnline brokerage/investment

Online only bankBrokerage firms Auto lendersCredit unionsMortage lendersCredit card banksFull service banks

2012 (4.9 mean of all financial institutions)



Mean Number of Financial Institutions Used by Households by Type(Base = Those with FI relationships by type of FI)

6

CONSUMERS ARE MORE LOYAL TO THEIR PRIMARY FI

14%

86%

12%

88%

10%

90%

Yes No

2010

2011

2012

Have You Changed Your Primary Financial Institution in the Past Two Years?(Base = All)

7

MOST PREFER IN-PERSON COMMUNICATION WHEN LEARNING

ABOUT NEW FINANCIAL PRODUCTS

8%

19%

2%

6%

6%

7%

23%

28%

None of the above

Other

Teller-assisted videoconference

Chat online at FI website

Telephone call with account specialist

Electronically at ATM or kiosk

In person with teller or greeter

In person with an account specialist

Preferred Method for Becoming Aware of New Financial Products and Services(Base = All)

8

INDUSTRY OVERVIEW:FI PROFITABILITY SOLUTION

POSITIONING

Database Layer

Application Server Layer

Core Banking System Layer

Integration Layer

Channel Systems

Underlying FI Infrastructure

Customer Analytics Predictive

AnalyticsCRM

Data Cleansing and Quality

Business Intelligence

9

INDUSTRY OVERVIEW:VARYING PROFITABILITY

PERSPECTIVES

FI Profitability

Database/Data

WarehousesLOB and Legacy

Systems

Financials Systems

Operations Systems

Channels Systems

Data Cleansing

and QualityMarketing and CRM Systems

BI, Reports, KPIs, and

Dashboards

Customer Analytics

Profitability Analytics

Best Practices Reviews

Strategy Consulting

FI-Centric Approaches

FI/ISV Partnerships

Consulting Partners

ISV Products and

Consulting Services

10

INDUSTRY OVERVIEW:COMMON CATEGORIES OF

ANALYTICS SYSTEMS

• Metadata

• Master data management

• Data modeling

• Business intelligence

• Dashboards

• Visualization

• Reporting tools

• Querying capabilities

• Branch

• ATM

• Online

• Mobile

• Call centers

• Multichannel

• Databases

• Data warehouses

• Data marts

• Core systems

• CRM

• Web

• Social media

• Predictive analytics• Customer

experience• Profitability models• Risk and compliance

models• Network analytics

• Real-time decisioning

• Content management

• Campaign management

• Event management

BusinessSystems and Data Sources

Data Mgmt

CustomerInsight

DecisioningModels

ChannelMgmtSystems

11

ANALYTICS-DRIVEN DECISIONS

• Why do banks (or any lender) invest in analytics? • Applying analytical techniques, particularly predictive modeling, to

customer data gives forward-looking insight into customer behavior.

• Understanding future customer behavior is integral to making better decisions and driving lender profitability from two primary perspectives:1. Marketing / Pricing – What loan parameters (APR in particular)

acquire the customer’s business?2. Credit Risk Management – Will the customer default on the loan? Is

his business worth having?

• Death of a Salesman? Possibly. • The renewed appetite for profitable growth (note Ed’s

presentation), combined with the explosion of available customer data, make the time right for automatic, realtime, analytically-informed lending to customers.

12

MARKETING AND CREDIT RISK APPLICATIONS

• The need for analytics within the Marketing and Credit Risk Management disciplines is pervasive.

• A recent survey of business technology professionals (see below) indicates that much of the interest in Big Data and Analytics is driven by (or at least correlated with) Marketing or Risk Management 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

MARKETING NEEDS

13

MARKETING ANALYTICS & CREDIT RISK ANALYTICS

• So lenders can make better decisions and drive profitability through “Credit Risk Analytics” and “Marketing Analytics” (not exclusively of course).

• Let’s define these terms that we’ll use colloquially throughout the presentation:• Credit Risk Analytics: empirically-based quantitative

techniques (e.g. statistical models) aimed at understanding, predicting, and controlling the level of credit risk associated with a consumer loan applicant and/or portfolio

• Marketing Analytics: empirically-based quantitative and qualitative techniques (e.g. statistical models, segmentation) aimed at understanding, predicting, and classifying the likely purchase behavior of a consumer or group of consumers

14

THE IMPORTANCE OF CREDIT RISK ANALYTICS

• Let’s show the importance Credit Risk Analytics with an example:• If a lender makes a $100 profit on a paying loan and loses $400 on a

defaulting loan, then it has to book 4 paying loans for every defaulting loan just to break even.

• Another way to state the above bullet is this: a loan applicant should have at least an 80% chance (4:1) of paying as agreed to be considered for approval.

• How do we determine if an applicant has at least an 80% chance of paying as agreed? Empirically-derived, demonstrably and statistically sound models of course. Almost all lenders use these in some form…• Generic credit bureau scores -and/or-• Custom scores derived from contract attributes (LTV, PTI) and credit

bureau attribute libraries (from Zoot of course)

15

RISK MODELS

• Continuing with the example…the importance of robust, predictive “risk models”:• So what if a lender is drawing from a population that is inherently

85% good (85% will pay off a standard loan) and 15% bad (15% will default on a standard loan)? Shouldn’t that lender always be profitable?

• It is crucial that the statistical model (or something equivalent) used by the lender to predict the likelihood of default be able to SEPARATE the good from the bad. Example below:

• If the model is incapable of any separation whatsoever, it will issue a 15% probability of default (PD…in bank terminology) for every proposed contract. This is less than a 20% cutoff, so we approve everything.

• Thus the lender’s profitability is easy to calculate…suppose 1,000 contracts will be booked: P = 850*$100 – 150*$400 = $25,000 … is it that easy to make money? By always saying yes to loan applications? That would be Death of a Credit Analyst. But it’s not that easy…

16

“GOOD AND BAD I DEFINE THESE TERMS, QUITE CLEAR, NO DOUBT, SOMEHOW” --

BOB DYLAN• Risk models must, first and foremost, distinguish between good

and bad (i.e. rank order the risk):• “All models are wrong, but some are useful”. -- George E.P. Box

• No individual customer has a 15% chance of default. All individual customers effectively have a 0% chance or 100% chance of default (they either do or they don’t).

• Profitability is far greater if the model is able to issue higher PD predictions for defaults than for payers. This is what happens when a model is able to RANK ORDER the risk. See the next bullet.

• Back to our example: Suppose the model predicts a PD of 25% for half the defaults, and 10% for the other half. In turn it issues a PD prediction of 25% for ¼ of the payers, and 10% for ¾ of the payers.• Before we calculate profitability we’ll note that the profitability on all

applicants with a predicted PD of greater than 20% is $0. They are declined based on the breakeven calculation on the previous slide. So…

• P = [850*.25*$0 + 850*.75*$100] – [150*.5*$0 + 150*.5*$400] = $33,750

• This is a 35% increase in profitability due to having a better predictive model.

17

MARKETING ANALYTICS

• We’ll continue the discussion with Marketing Analytics, which has become a staple in the retail industry (Target for example)…• The analytical techniques used to predict how “in-market” a

customer is for clothes, diapers, etc… can be the same ones used to predict how “in-market” a customer is for a loan.

• The advantage of using predictive analytics to identify the customers most likely to take up a loan is that it “expands the base of incrementality” associated with a loan offer. • In other words, identifying the groups of customers that are most likely to

buy (take up a loan) is tantamount to identifying the groups that contain the majority of the “incremental” sales (contracts).

• If I can get the most of the incremental sales by making an offer to only a small fraction of the population, then I can squeeze most of the benefit from the offer at a fraction of the cost. Example next slide.

18

ADDING BUSINESS VALUE THROUGH MARKETING ANALYTICS: AN

EXAMPLE• Intelligent use of Marketing Analytics enables lenders to generate

incremental loans cost-effectively and efficiently. An example with assumptions:• An untargeted (no model), incentivized loan offer to 100,000 customers

increases the “take rate” by 10%.• The revenue per incremental loan is $250.• The cost of communicating the offer to 100,000 consumers is $20,000.• The cost of the incentive is $20 per loan.• When we apply a predictive model, we split the population into 4 groups

(1=most likely to take…4=least likely to take)

No Model

Use a Model

Profit = $10,000

Profit = $14,00040% improvement

Population OrganicTake Rate

OfferTake Rate

Incremental “Takers”

100,000 10% 11% 1,000

Incremental Revenue

$250,000

Model Rank Population Organic Take

RateOffer

Take RateIncremental

“Takers”

1 25,000 18% 19.80% 4502 25,000 14% 15.40% 3503 25,000 6% 6.60% 1504 25,000 2% 2.20% 50

IncrementalCost

Incremental Profit

$240,000 $10,000

Incremental Revenue

$112,500 $87,500 $37,500 $12,500

Incremental Cost

Incremental Profit

$104,000 $8,500 $82,000 $5,500 $38,000 ($500)$16,000 ($3,500)

19

THE INTERSECTION OF MARKETING AND CREDIT RISK

• Practically we should not deploy Marketing Analytics in a lending environment without doing sound Credit Risk Analytics at the same time.• Marketing analytic efforts are typically aimed at increasing response (and thus

sales). Doing so can also increase credit risk, which means credit losses can easily wipe out the gains had by improvements in targeted marketing efforts.

• The right loan offer needs to be defined as the one that maximizes incremental profit…after incremental credit losses are factored in.• Making the right loan offer is an analytical exercise that requires the

intersection of Marketing and Credit Risk Analytics. Through the Magnify-Loan Science partnership, we specialize in this type of exercise…and we deploy through Zoot.

• We see pre-approval models as being the perfect example of this intersection. And we will show the work we’ve done in auto…• Common with credit cards…interest rates and credit limits are tested to

determine the impact on response and yearly interest revenue.• Pre-approval models for auto are more complex, because the presence of

collateral means we have to solve for very important variables like loan-to-value and term.

20

DEATH OF REDEFINING A SALESMAN: USE ANALYTICS TO TARGET OFFERS

• The tyranny with almost all pre-approval programs is that the customers who respond to them are the ones you least want to give credit to.

• Customers with low credit bureau scores are generally the ones that respond to pre-approval offers, and the more exposure the lender is willing to take, the better they respond.• See the example below from an auto captive…the data are doctored

somewhat but not to the point where the message is changed:

Population: Existing Customers and Prospects

FICO Score and Control Buy Rate

Target Buy Rate “Incrementality” LiftPre-Approval Amount

FICO <= 680 and Pre-Approval >= $30,000 1.93% 2.17% 0.23% 12.14%

FICO <= 680 and Pre-Approval < $30,000 0.93% 1.52% 0.60% 64.27%

FICO > 680 and Pre-Approval < $30,000 2.34% 2.32% -0.01% -0.46%

FICO > 680 and Pre-Approval >= $30,000 2.64% 2.56% -0.08% -2.85%

21

MAKE THE RIGHT OFFER RESPONSIBLY…

• Can we have the high lift associated with high-risk customers AND control the risk of the pre-approved portfolio? Yes, probably so. Consider turning the traditional PD (probability of default) model on its head:

• Traditional: PD = f(credit score, LTV, PTI, term,…)

• Pre-approval: LTV = f(PD, credit score, PTI, term,…)• See the auto captive example below, where we control for PD and solve for

LTV

Will yield Tier B performance.Tier B rate can be guaranteed in the pre-approval offer.

Will yield Tier C performance.Tier C rate can be guaranteed in the pre-approval offer.

Will yield Tier D performance.Tier D rate can be guaranteed in the pre-approval offer.

Credit Score PD Term LTV Limit581-600 4.0% 60 60%601-620 4.0% 60 75%621-640 4.0% 60 85%641-660 4.0% 60 95%661-680 4.0% 60 100%



22

REDEFINING SALES OCCURS WHEN THE BENEFITS OF ANALYTICS AND TECHNOLOGY

ARE TANGIBLE…

• In the example on the previous slide we achieved two important outcomes:

1. We confined our targeting to the customers who, according to our best Marketing Analytics, would respond to our offer.

2. We confined our offers to those that, according to our best Credit Risk Analytics, would be profitable at a controlled level of risk and price.

• We also achieved a third very important outcome, which I’ll offer as a conclusion: we generated incremental loans that subsequently contributed an additional $8 mils profit per year.

• But what is missing from this good story? DEPLOYMENT. Analytic tools and technologies must be made available to operational systems when:• Credit decisions are made, or when • A list of targeted customers is generated, or when • The parameters of pre-approval offers are specified

• ... and this is where Zoot fits in: realtime deployment of analytic tools so that interactions with the customer are informed, targeted, and profitable…

23

NEXT GENERATION ACCOUNT ACQUISITION

24

CONSISTENT EXPERIENCE ACROSS CHANNELS

25

TYING IT ALL TOGETHER: OFFERS REPOSITORY

26

OPTIMIZED OFFERS

27

BETTER DATA, BETTER DECISIONS

28

QUICKER TIME TO MARKET

29

SUMMARY

“The only thing you got in this world is what you can sell. And the funny thing is that you're a

salesman, and you don't know that.”

~Arthur MillerDeath of a Salesman

• Sales in retail banking isn’t dead, but it has changed.

• New channels and more interactions across all channels.

• Analytics available to make more intelligent decisions.

• Underlying technology must support next generation account acquisition techniques.

30

Ed O’Brien, Director Banking Channels, Mercator Advisory [email protected]

Keith Shields, Chief Analytics Officer at Magnify Analytic Solutions and Chief Credit Officer at Loan [email protected]

Tom Johnson, Vice President, Strategic Alliances, Zoot [email protected]

QUESTIONS ?

Documents

Death of a Salesman: Account Acquisition in a New Environment