Download Driving Safe Growth in a Fluid Economy

Driving Safe Growth in a Fluid Economy
Identify timely, highly qualified leads to optimize direct
marketing investment in the Card space
Dan Richard
Director, Research & Development Analytical Services
October 2012
Table of Contents
1 Driving Safe Growth in a Fluid Economy
2 Today’s Direct Mail Marketing Campaigns
3 Why Traditional Prescreen Strategies Often Miss the Target
4 The Benefits of Modeling Propensity to Open
6 Case Study: Using Propensity to Open Scores in Direct Marketing
10Conclusion
11References
About Equifax
Equifax is a global leader in consumer and commercial information solutions, providing
businesses of all sizes and consumers with information they can trust. We organize and
assimilate data on more than 500 million consumers and 81 million businesses worldwide,
and use advanced analytics and proprietary technology to create and deliver customized
insights that enrich both the performance of businesses and the lives of consumers. For
more information, please visit Equifax.com.
www.equifax.com
Equifax Inc. | Driving Safe Growth in a Fluid Economy | ii
Driving Safe Growth in a Fluid Economy
As the consumer credit market continues to recover, “Safe Growth” acquisition marketing
has returned as a strong imperative for card issuers. Lending standards remain fairly high
for new originations even while tightening of credit availability has eased [1]. Following
a period of sustained growth in 2010 and 2011, industry-wide bankcard origination
volumes have leveled off in 2012 [2]. Now most lenders are seeing significant headwind
as consumers have “reloaded” with new cards during the recovery. In light of these recent
trends, many issuers have pulled back on direct mail marketing [3].
To safely grow card portfolios in this uncertain economic environment, lenders need
analytical tools that deliver timely, highly qualified leads in an effort to optimize response
and open rates while managing risk more effectively. Potent marketing tools that can be
tuned to fit within risk strategy empower lenders to approach card preapproval acquisitions
intelligently and confidently.
This paper discusses the various preapproval marketing challenges facing card issuers and
introduces the concept and value of integrating specialized scores that predict consumers’
propensity to open within existing targeting strategies.
Less Competition in the Mailbox
"[Credit card] issuers have adopted a more cautious approach due to an
uncertain economic environment.… For credit card issuers this is a great time
to be in the mail. The mailbox is less cluttered and it is easier to get consumers
to notice your message." — Andrew Davidson, SVP Mintel Comperemedia [7]
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Direct mail marketing
campaign managers face
the strategic challenge of
targeting consumers that
are in-the-market, qualified
to pass risk criteria and
likely to respond to an offer.
Today’s Direct Mail Marketing Campaigns
Marketing and Risk Management represent two of the most basic organizational functions
that card issuers must skillfully perform to remain competitive. Marketing departments strive
to maximize response rates and deliver high volumes of applications through meticulous
targeting and messaging efforts. Meanwhile, Risk Management departments maintain
strategically determined levels of portfolio risk by decisioning applicants via carefully
established risk-based criteria. Too often the goals of these two functions are at odds, as
high percentages of applications driven via marketing efforts fail to pass risk criteria and
never become booked accounts. Thus, direct mail marketing campaign managers face the
strategic challenge of targeting consumers who are in-the-market, qualified to pass risk
criteria and likely to respond to an offer.
Furthermore, a recent survey of bank and credit union marketers asked respondents to
indicate their highest priorities in today’s lending environment. Topping the list was the
increase of lending portfolios, mainly by deepening existing relationships through cross-
Channel of Choice
A 2011 Epsilon study [6] on consumer channel preference revealed valuable insights
about direct mail marketing:
• 36% of U.S. consumers chose direct mail as the preferred channel among several
alternatives to receive financial services information.
• Notably, the direct mail preference extends to the 18-34 year old demographic.
• 60% of U.S. consumers reported an emotional boost from receiving direct mail, stating
they “enjoy checking the mail box for postal mail.”
• A rising percentage of U.S. consumers report that they are now receiving more mail
that interests them, while a falling percentage report that they are now receiving more
mail in general. Fewer cluttered mailboxes and more targeted mail pieces contribute to
consumers’ preference for traditional mail.
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selling loan products like credit cards and mortgages [4]. This priority appears to align with
current trends in consumer spending habits, which have largely shifted out of prerecession
impulsiveness and into longer-term management of spending, saving and debt. Financial
institutions are wise to adopt a relationship-management approach that drives lifetimecustomer value. [5]
In light of major marketing challenges and priorities, direct mail as a marketing channel
plays a key role in deepening relationships with existing customers and pursuing long-term
banking relationships with new customers. A recent Epsilon study revealed that direct mail
continues to be consumers’ most trusted channel of choice for receipt of financial services
information [6]. The findings indicate that lenders should continue to pursue direct mail as
a core channel in marketing campaign strategy.
A recent Epsilon study
revealed that direct mail
continues to be consumers’
most trusted channel
of choice for receipt of
financial services
information [6]
Designing direct mail marketing campaigns with these challenges, priorities and channels
in mind is essential to card issuers’ successful expansion in an uncertain economy. Having
the right analytical strategies and tools will ensure that the right consumers are targeted.
Why Traditional Prescreen Strategies Often
Miss the Target
When designing direct mail marketing campaigns, most lenders today use prescreen
strategies designed to train their marketing crosshairs on the segments of the
consumer population that are most likely to respond to an offer while overlaying
policy- and risk-related standards. Response-based targeting strategies indeed tend
to demonstrate predictive value in generating higher application volumes. However, a
firm’s true return on investment from a direct mail acquisition campaign will be based
on revenues generated from newly booked accounts, not on application volume alone.
If lenders focus solely on response rates as the measure of marketing success, their
targeting efforts may not be aligned to this true ROI. Various factors can contribute to
the misalignment, including:
• Mailing to higher-risk consumers who can’t be booked due to unacceptable risk level
• Mailing to consumers who are not interested in opening new credit
• Not mailing to consumers who are interested in opening new credit
The case of mailing to high-risk consumers results from adverse selection often inherent
in many response-driven analytical solutions. The same consumers that are most likely to
respond to an offer for credit tend to also be the consumers with the riskiest characteristics.
This response-risk contrast can often create dissonance between the marketing and risk
sides of the same lending operation.
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The same consumers that
are most likely to respond
to an offer for credit tend to
also be the consumers with
the riskiest characteristics
This response-risk contrast
can often create dissonance
between the marketing
and risk sides of the same
lending operation
Propensity-to-open is
a metric that frames
consumer desirability
in terms of likelihood of
being in-the-market and
approvable
Secondly, lenders may mail to consumers that share similar profile elements with likely
responders, though in reality these consumers have no interest in opening a new card
with any lender regardless of what offers are made. With lenders’ risk criteria still fairly
tight, many of the consumers that do pass risk criteria are not as likely to respond. A
similar miscalculation occurs when lenders overlook consumers who are indeed highly
likely to open new credit, though they don’t exhibit many of the same characteristics as
likely responders and thus are not mailed. The key to identifying each of these cohorts
of consumers lies in tempering any response-driven assessment with an assessment of
likelihood to actually open new credit.
The Benefits of Modeling Propensity to Open
Even in the absence of
existing response-based
analytical strategies,
lenders that rank prospects
by propensity-to-open
can guide targeting
efforts toward consumers
who contribute most to
maximizing return on
marketing investment
When a new bankcard account appears on a borrower’s credit file, usually two inferences
can be made: the borrower was in the market for a new bankcard and was able to be
approved. Simultaneously the borrower’s demand and a lender’s assent are captured
by the fact that there exists a newly opened account. Using the Equifax National Credit
Database, bankcard-opening behavior can be studied across millions of consumers and
thousands of lending institutions. Through an analysis of the most common characteristics
of in-the-market and approvable consumers, Equifax has the ability to model consumers’
propensity to open new bankcards.
Propensity-to-open is a metric that frames consumer desirability in terms of likelihood of
being in-the-market and approvable. By contrast, propensity-to-respond frames consumer
desirability in terms of likelihood to take action when presented with an offer. Insight into
both dimensions enhances marketing competencies, allowing lenders to focus direct mail
toward consumers that are most likely to respond and open and perform. Lenders also are
empowered to reach out with customized messaging / offers to consumers who are less
likely to respond to offers but who nevertheless are in-the-market and approvable. Even in
the absence of existing response-based analytical strategies, lenders that rank prospects
by propensity-to-open can guide targeting efforts toward consumers who contribute most
to maximizing return on marketing investment.
Consumers’ demand for bankcard credit and their general credit risk level combine in a
complex manner to describe a consumer’s propensity to open a new bankcard account.
Bankcard openers are found throughout the credit risk spectrum, with open rates tending
to vary across risk bands. As demonstrated in Figure 1, propensity-to-open tends to decline
among consumers with the worst general credit risk. Experience with response scores
suggests that this decline is not due to lack of demand for credit. Rather, these consumers
commonly experience greater difficulty getting approved, and so open rates in these risk
bands are diminished by the influence of lenders’ underwriting standards. Lenders that
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grant bankcard credit in this risk range will generally be meeting the needs of their clientele
by offering credit that may otherwise be difficult to come by.
Interestingly, consumers on the opposite end of the risk spectrum with the best general credit
risk can also exhibit a leveling-off or slight reduction in propensity-to-open, depending on
the risk score being studied. Gradually leveling or decreasing demand for bankcard credit,
not difficulty in approvability, is most likely the driving force behind these circumstances.
Lenders that grant bankcard credit in this risk range will generally be meeting the wants
of their clientele by offering special perks or rewards to encourage initial adoption and
continued usage. The peak in open rate tends to occur near the upper-middle of the risk
spectrum, where consumers exhibit strong demand for bankcard credit while enjoying
fewer underwriting obstacles.
Bankcard Open Rates by Generic Risk Score Deciles
Beacon 5.0
Beacon 09
Equifax Risk Score 3.0
Vantage 1.0
Bankcard Open Rate (2-4 Months After Scoring)
6%
• Lenders meeting borrowers’ credit needs
• Open rates dampened by lower approvability
5%
4%
3%
• Lenders meeting borrowers’ credit wants
• Open rates level off or decline based on demand
2%
1%
0%
1
2
3
4
Worst Risk
5
6
7
Generic Risk Score Decile
Figure 1: Bankcard Open Rates by Generic Risk Score Deciles.
As Figure 1 indicates, a soft risk element is embedded within the measure of a consumer’s
propensity-to-open. The collective impact of lenders’ underwriting policies shapes the
propensity to open new credit among consumers with worsening levels of credit risk. Thus,
a subtle dimension of likelihood to default exists within a consumer’s likelihood to open
new credit. A propensity-to-open model cannot replace a credit risk model in underwriting
practices, nor should one be used in isolation without a baseline measure of credit risk.
However, a propensity-to-open model can help an organization better align targeting efforts
with risk management policies. Since individuals with the worst credit risk will by nature
tend to have less success at opening new credit, such individuals will not score in the
highest ranges of a propensity-to-open model as often as they might in a response model.
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8
9
10
Best Risk
Propensity to open marries
consumer demand with
consumer credit risk
in such a manner that
different demand-related
consumer characteristics
emerge throughout the risk
spectrum
Along with the impact of lender underwriting standards, consumer demand is the other major
influencer of whether a consumer will open a new bankcard. Propensity-to-open marries
consumer demand with consumer credit risk in such a manner that different demand-related
consumer characteristics emerge throughout the risk spectrum. Many of these characteristics
describe demand in ways that are not directly risk-related in nature. Thus, risk-related
elements of a prescreen strategy would fail to deliver maximum open rates in the absence of
a propensity-to-open score due to leaving out this crucial consumer demand element.
Case Study: Using Propensity to Open Scores
in Direct Marketing
The True-in-Market Propensity Score (TIP Score) for Bankcard Openers is an Equifax
solution that helps marketers model consumers’ propensity to open so they can quickly
recognize and connect with consumers who are likely to open bankcard credit products in
the near future. In numerous validations on generic samples and on customer portfolios
this score delivers strong ranking of consumers by open rate while simultaneously ranking
lower bad rates.
True-in-Market Propensity
Scores (TIP Scores) for
Bankcard Openers, an
Equifax solution that models
a consumer's propensity
to open, delivers strong
ranking of consumers
by open rate while
simultaneously ranking
lower bad rates
A recent validation of TIP Scores was performed on a random sample from the Equifax
National Credit Database. Within that sample, Equifax created a simulated customer
portfolio at observation by combining the portfolios of a select group of several leading
card issuers. Observation status was defined as follows:
Market: All consumers in the sample
In Portfolio: Subset of Market in which the consumer currently holds a bankcard with a
lender in the select group
Not in Portfolio: Subset of Market that represents the mutually exclusive counterpart to
“In Portfolio”
The outcome performance statuses were defined as follows:
Open: Consumer opens new bankcard 2 to 4 months1 after observation
Open “On-Us”: Subset of Open in which the consumer opens a new bankcard with one of
the select group of card issuers
Open “Off-Us”: Subset of Open that represents the mutually exclusive counterpart to
Open “On-Us”
Consumers that opened a new bankcard within the first month after observation were not classified as
desirable “Openers”. In a live campaign, a direct mail marketer is often too late in getting an offer to a consumer
that opens new credit within a few weeks after scoring. Such consumers have often already opened a new
account elsewhere.
1
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Bad: Subset of Open in which the newly opened bankcard becomes sixty or more days
past due or the consumer declares bankruptcy within twelve months after the open date
Additionally, a handful of common policy rules and risk prescreen criteria elements were
applied in an effort to simulate a generalized prescreen waterfall. Criteria elements were
intentionally kept simple to ensure that the validation results would be applicable to a wide
range of audiences with various targeting strategies. The prescreen elements were as
follows:
A sign that consumer
spending is returning:
US Bankcard originations
are increasing, and
write-offs are decreasing.
—Equifax National Consumer
Credit Trends Report May 2012
Fail prescreen criteria if:
• Consumer is deceased
• Consumer is under age 21
• Consumer has “Do Not Solicit” flag on credit file
• Consumer has Beacon (FICO) score less than 640
In the results that follow, “prescreen fail rate” indicates the rate at which consumers failed
to pass these minimum prescreen criteria.
Achieve Agreement with Prescreen Policy
Rules and Risk Criteria
While consumers who do not pass prescreen criteria would not actually be mailed in
a direct marketing campaign, Equifax studied the relationship between TIP Scores and
prescreen fail rates to assess how in sync a score that predicts consumers’ propensity to
open is with common prescreen strategies.
Figure 2 below demonstrates that as TIP scores increase, not only do open rates increase
dramatically, but prescreen fail rates also decrease dramatically. The trends result from the
way TIP Scores are designed to favor in-the-market and approvable consumers. The one
exception to the trends appears in the third decile which captures a cohort of relatively
lower-risk consumers with a low propensity to open. The good risk quality of this group
resulted in a dip in prescreen fail rate, but these consumers are nevertheless unlikely to
open new credit due to low demand. With its blend of demand- and risk-related attributes,
the TIP Score ranked them among the lower deciles.
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Open Rate and Prescreen Fail Rate by TIP Score Decile
Open Rate
Prescreen Fail Rate
13%
80%
12%
70%
Declining risk and
increasing demand
11%
10%
50%
Open Rate
8%
7%
40%
6%
Even with a relatively good risk profile,
this low-demand cohort demonstrates
small open rates, and hence low TIP Scores
5%
4%
30%
20%
3%
2%
10%
1%
0%
Prescreen Fail Rate
60%
9%
1
2
3
Lowest Propensity to Open
4
5
6
TIP Score Decile
7
8
9
10
0%
Highest Propensity to Open
Figure 2: Open Rate and Prescreen Fail Rate by TIP Score Decile.
Powerfully and Safely Rank Open Rates
The prescreen criteria were applied to the sample, and consumers who failed the criteria
were excluded from the simulated mailed population. With the remaining consumers who
were eligible to mail, Equifax studied On-Us and Off-Us open rates, as well as bad rates,
in the months that followed observation. Additionally, Equifax studied the aforementioned
segmentation of “In Portfolio” versus “Not in Portfolio” as of observation.
Figures 3, 4 and 5 below demonstrate that the TIP Score powerfully ranks open rates
within the “In Portfolio”, “Not in Portfolio” and “Market” populations, which illustrates that
the scores are useful in both cross-sell and new acquisition scenarios. After applying the
prescreen criteria, the resulting bad rates are mostly flat across the TIP Score distribution.
Even with prescreen risk criteria applied, a response score would typically demonstrate
higher bad rates in the top score ranges. Thus, the risk-mitigating nature of TIP Scores
helps lenders more safely target in-the-market consumers.
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Open and Bad Rates by TIP Score for the Market (combined) Populations
Open Rate (On-Us)
Open Rate (Off-Us)
Open Rate (Overall)
Bad Rate
16%
Open Rates and Bad Rate
14%
12%
10%
8%
6%
4%
2%
0%
1
2
3
4
Lowest Propensity to Open
5
6
7
TIP Score Decile
(After Applying Prescreen Criteria)
8
9
10
Highest Propensity to Open
Figure 3: Open and Bad Rates by TIP Score for the Market (combined) Populations.
Open and Bad Rates by TIP Score Decile for “In Portfolio” Population
Open Rate (On-Us)
Open Rate (Off-Us)
Open Rate (Overall)
Bad Rate
16%
Open Rates and Bad Rate
14%
12%
10%
8%
6%
4%
2%
0%
1
2
3
Lowest Propensity to Open
4
5
6
7
TIP Score Decile
(After Applying Prescreen Criteria)
Figure 4: Open and Bad Rates by TIP Score Decile for “In Portfolio” Population.
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8
9
Highest Propensity to Open
10
Open and Bad Rates by TIP Score Decile for “Not in Portfolio” Population
Open Rate (On-Us)
Open Rate (Off-Us)
Open Rate (Overall)
Bad Rate
16%
Open Rates and Bad Rate
14%
12%
10%
8%
6%
4%
2%
0%
1
2
3
Lowest Propensity to Open
4
5
6
7
TIP Score Decile
(After Applying Prescreen Criteria)
8
9
10
Highest Propensity to Open
Figure 5: Open and Bad Rates by TIP Score Decile for “Not in Portfolio” Population.
Conclusion
In this uncertain economic environment, lenders seeking to safely grow portfolios need
marketing analytics tools that can be easily tuned to fit within existing risk strategies.
Using analytical tools that target the propensity to open for in-the-market and approvable
consumers improves ROI of direct mail campaigns by delivering leads that are likely to actually
open new credit—not just respond to an offer. As the Equifax case study demonstrates,
integrating specialized scores that predict propensity to open within an existing prescreen
strategy can empower card issuers to focus cross-sell and new acquisition marketing
efforts toward consumers that are most likely to open a new bankcard account in the near
future.
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References
[1] A. R. Johnson, "Loan Write-Offs Edge Up For Some Card Issuers But Outlook Remains
Strong," 16 April 2012. [Online]. Available: www.dowjones.com/djnewswires.asp.
[Accessed 5 August 2012].
[2] Equifax.
[3] M. Rice, "Credit Card Mail Volume Summary: July 2012," Mintel International Group
Ltd., 2012.
[4] TheFinancialBrand.com, "State of Bank & Credit Union Marketing in 2012," 17 January
2012. [Online]. Available: http://thefinancialbrand.com/21384/2012-bank-creditunion-marketing-study-results/. [Accessed 29 August 2012].
[5] P. Iyer, "Smarter Spending and Saving: Evolution in U.S. Consumer Behavior," June 2012.
[Online]. Available: http://insights.mastercard.com/wp-content/uploads/2012/08/
MCW_Smarter_Spending_Saving-SINGLE-final.pdf. [Accessed 30 August 2012].
[6] Epsilon Targeting, "The Formula for Success: Preference and Trust. Consumer Channel
Preference Study," December 2011. [Online]. Available: http://www.marketingpower.
com/ResourceLibrar y/Documents/Content%20Partner%20Documents/
Epsilon/2011/1112_eps_channelprefstudy2011.pdf. [Accessed 29 August 2012].
[7] PR Newswire, "April credit card direct mail volume hits 25-month low, reports Mintel
Comperemedia," 12 June 2012. [Online]. Available: http://www.prnewswire.com/
news-releases/april-credit-card-direct-mail-volume-hits-25-month-low-reportsmintel-comperemedia-158589225.html?utm_expid=43414375-18. [Accessed 29
August 2012].
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