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Risk Adjusted
Marketing
Executive
Summary
This white paper outlines Experian’s vision for a more integrated and
customer-centric application of analytics to risk and marketing decisions
for credit products in retail banking.
The concept of Risk Adjusted Marketing is introduced, building on the idea
of ‘right person; right message; right channel; right time’ by bringing into play
valuable insight from internal and external data sources that give a wider view
of the customer.
Risk Adjusted Marketing is a method to improve the success
of marketing strategies by more actively considering the risk
dimension from a customer rather than product perspective.
Mosaic, the geo-demographic classification tool, is an
important enabler of this approach.
These concepts are discussed and developed in this white paper:
Optimise the value of your customers by tailoring marketing and risk strategies to them, not to your products.
Look at your customers holistically, supplemented by external data sources, rather than through separate
product or marketing silos.
Break the nexus between risk and marketing, by moving towards the Experian vision of an integrated Risk Adjusted
Customer View database.
Focus the use of expensive analytical resources where the biggest returns can be achieved.
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Risk Adjusted Marketing
Contents
2
Section 1 Risk Adjusted Return (RAR)
3
Section 2 Introducing the Concept of Risk Adjusted Marketing
4
Section 3 Risk and Marketing Processes in Banks Today
5
3.1 Risk Process
5
3.2 Marketing Process
5
3.3 Limitations of the Current Approach
6
Section 4 Mosaic: A Core Enabler of Risk Adjusted Marketing
7
4.1 Mosaic
7
4.2 DataPlus
8
4.3 Comprehensive Credit Reporting (CCR)
8
4.4 Bringing it Together: Credit Risk Rating and Mosaic
9
Section 5 Experian’s Vision: A Single Customer View
12
13
5.1 Risk Adjusted Customer View Database in Practice
Section 6References
14
Section 7 About the Author
15
Section 8 About Experian
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Risk Adjusted Marketing
Section 1
Risk Adjusted Return
(RAR)
Risk Adjusted Return (RAR) or Risk Adjusted Return on Capital (RAROC) are
common and widely understood concepts in the financial services industry.
At a basic level, the risk an organisation takes on is balanced
against potential reward in writing a loan or issuing an
insurance policy.
The RAROC metric is usually defined as
RAROC = (Expected Return)/(Economic Capital) or
RAROC = (Expected Return)/(Value at Risk)
Economic capital is the buffer set aside to cover unexpected
losses, ensuring an organisation can survive shocks to the
financial system.
Figure 1: Loss Distribution Curve
Economic capital is made up of market risk, credit risk and
operational risk. For banks in particular, Basel II has led to a
more rigorous definition and estimation of these components
using mathematical and statistical models.
As the Global Financial Crisis has shown, economic capital
is often not an adequate buffer for extreme financial shocks,
increasingly referred to as ‘Black Swan’ events. Extreme
financial shocks cannot be predicted, and often result in
financial services organisations defaulting.
Capital is one of the most important assets for a financial
services organisation. Responsible management ensures
capital is allocated to maximise shareholder return, taking
into account the risk profile of different exposures and
asset classes.
RAROC is a key metric in the capital allocation process.
Probability
of loss
Priced into the product
(risk-based pricing)
Risk based pricing is an example of RAROC at a micro level.
Sophisticated organisations are able to price for risk, for
example adjust interest rate premiums or discounts, based
on the customer’s risk profile and product being applied for.
Covered by capital
reserves (economic
capital)
Bank
default
Expected
(EL)
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Risk Adjusted Marketing
Unexpected
(UL)
Amount
of loss
Section 2
Introducing the Concept
of Risk Adjusted Marketing
The previous section discussed RAROC as a method to optimise capital allocation
by aligning potential return with the risk involved.
In a similar way, Risk Adjusted Marketing (RAM) is a method
to improve the success of marketing strategies by more
actively considering the risk dimension in the process,
aligned more to the customer rather than the product.
Risk Adjusted Marketing builds on the idea of ’right person;
right message; right channel; right time’ by bringing into play
valuable insight from internal and external data sources to
give a wider view of the customer.
For financial services companies, this may be credit
risk. For telecommunications companies, this may be
payment history, and for insurance companies this may
be claims record.
Risk Adjusted Marketing places the risk dimension much
more to the fore in the marketing mix and process.
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Risk Adjusted Marketing
More importantly, Risk Adjusted Marketing:
Views customers holistically, instead of through separate
product or marketing silos.
Helps bridge the perennial divide between competing
drivers of marketing (volume and market share) and risk
(quality and profitability).
This white paper will focus on Risk Adjusted Marketing in
the financial services sector, specifically on credit risk and
marketing for consumer products in retail banking.
The concepts discussed are easily extended to other vertical
markets such as insurance and telecommunications.
Section 3
Risk and Marketing
Processes in Banks Today
3.1 Risk Process
Figure 2: The Virtuous Loop
The vast majority of banks run their risk and marketing
decisions as separate, sequential processes.
Risk decisions support marketing activities, are very
product-centric, and not fully integrated with marketing
activities. The analytics that underpin these activities are
typically product rather than customer driven.
The typical product risk sequence looks like this:
Test
Define
Execute
The broad framework of a bank’s risk appetite and policy
is generated, cascading down from the Board Level Risk
Committee, taking into account a variety of factors such
as Group level regulatory requirements.
In the Australian market, regulatory requirements include
Basel II, governed by APRA, and Responsible Lending,
governed by ASIC.
Portfolio and product level risk policy is generated,
taking into account factors such as:
• Product specific regulatory requirements
• Current and future product profitability
• P
roduct risk and reward trade-offs, for example
Credit Cards are high risk, high reward products
• RAROC
• Current and future bad debt
• G
eneral product drivers such as acquisition costs,
ongoing account management costs, fees, attrition,
collection costs
• Stress testing
Product risk framework parameters are deployed
operationally through decision engines such as
Experian’s PowerCurve Platform for acquisition
and customer management.
5
Improve
Monitor
3.2 Marketing Process
The typical marketing process looks like this:
Marketing analysts, typically working within the product
function, create their marketing universe, a marketing
view of the bank’s customer base.
The marketing universe may already exclude customers
with particular risk criteria, such as a high or not eligible
risk rating.
Either way, once marketing models and risk exclusions are
applied to the universe, the risk team will typically review
the outcome before the campaign is activated.
Marketing campaign is activated and results tracked.
In some cases, marketing results are captured and used
to refine future targeting models, similar to the loop
shown above.
Execution results, such as risk scores, terms of business,
final decision, limits assigned, are stored to a risk
database, which may be product specific or shared across
multiple products.
I t is worthwhile noting that the need to bring the risk
dimension much more into marketing campaigns comes into
sharper focus when risk and collections areas are dealing
with down stream effects of increased bad-debt. This is often
caused by poor customer targeting in the first place and/or
poor decisioning at the point of origination.
Information stored in the database is analysed and informs
changes to risk appetite and policy, starting the sequence
again and closing the virtuous loop.
An element of the ‘ready, fire, aim’ approach to marketing
often contributes to these types of issues.
Risk Adjusted Marketing
Section 3
3.3 Limitations of the Current Approach
Business challenges posed by the siloed risk and
marketing processes in the previous section encompass
these main areas:
1 No single version of the truth
Risk and marketing analytics do not have a
common language.
Data and business definitions are often different, for
example, how to define a customer and how to calculate
revenue or profit per customer.
Data views do not align with each other.
Joined-up decision making at customer level is
not possible.
2 Disconnects between risk and marketing models
Risk models, and risk-reward models, do not typically
incorporate any marketing insights.
For example, if propensity to purchase insight is missing
from risk and product design processes, marketers will be
left with a sub-optimal marketing universe.
It is difficult or impossible to systematically identify
areas of missed marketing opportunity or wasted
analytical effort.
3Sequential decisioning processes hinder optimising
customer value
In the sequential process, a relatively immaterial variable
in the risk analysis may have the effect of excluding a
highly profitable segment of customers from a marketing
perspective. This is because the risk analysis will
implement discretionary exclusions before the marketing
analysis begins.
The risk and opportunity analysis is not viewed side by
side at this critical stage, meaning that relative strengths
and potential synergies are not exploited.
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Risk Adjusted Marketing
4 Time to market
Time to market for a new campaign or new product can
take up to three months because of fragmented data views
and the lack of joined up workflow.
An increasingly competitive market, in combination with
increasing regulatory, political and consumer pressure,
requires the flexibility to significantly reduce time to
market to weeks or days.
5 Resource efficiency
Banks require expensive, hard to find high end analytical
resources and tools for all steps, because all stages of the
process require complex data manipulation and analysis.
Section 4
Mosaic: A Core Enabler
of Risk Adjusted Marketing
Experian’s ultimate vision for a more customer-centric and integrated application
of analytics to risk and marketing decisions in the retail banking sector is dependent
on a single customer view database.
This concept will be developed later on in this white paper.
As a step along the way, Mosaic is a key enabler of Risk
Adjusted Marketing, using existing analytical techniques
and tools.
Section 3.1 outlined the typical product-centric risk
process, which can be categorised as a top-down
approach, cascading from a bank’s macro risk appetite
and policy framework.
Mosaic helps banks move to a more bottom-up approach,
where the customer view becomes more central to risk
and marketing processes.
4.1 Mosaic
Mosaic1 is a geo-demographic profiling tool that uses
aggregated consumer data to provide highly predictive
insight of the Australian population. Mosaic categorises the
Australian population into 11 ‘Groups’ and 47 ‘Types’ (finer
classifications within a Group).
For example, Group C is Young Ambition – Educated and
high-earning young singles and sharers in the inner suburbs.
Figure 3: Group C
There are three Types within Group C:
C09 Bright Futures – Thriving students or professionals
renting flats and terraces.
C10 Graduating Upwards – Young high-earning socialites
in high-rise apartments, often close to water.
C11 Rising Wealth – Educated and affluent young
professional couples in inner city areas.
Each Mosaic Group and Type comprises a demographic
summary, expressed through a number of categories:
People and Skills – information spanning household
age, relationships and education.
Living Space – information spanning housing type, tenure,
property values and size.
Balancing the Books – information spanning household
incomes, rental and loan costs.
Lifestyle and Attitudes – information spanning
favourite hobbies and activities, media consumption,
social attitudes.
A Day in Their Life – A sample portrayal of everyday tasks
that brings the classification to life.
Each category has granular attributes, showing how these
compare against Australian population averages, generating
valuable customer insight which can inform marketing
campaigns e.g. locations to target or communication
medium to use.
1 P
acific Micromarketing Mosaic Australia is an innovative geo-demographic profiling tool that uses aggregated consumer data to provide highly predictive analysis of the
Australian population. Mosaic is used by businesses to help them understand consumer trends and behaviours across Australian neighbourhoods. Mosaic does not use
personal information to provide details about individuals or their addresses. The underlying data used to build Mosaic is privacy compliant.
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Risk Adjusted Marketing
Section 4
4.2 DataPlus
4.3 Comprehensive Credit Reporting (CCR)
Mosaic classifications can be enriched further with other
Australian geo-demographic information, providing an
additional ‘outside looking in’ customer view.
In an ideal world, the introduction of Comprehensive Credit
Reporting would provide a complete credit risk view of
the customer, allowing for more effective Risk Adjusted
Marketing campaigns.
DataPlus2 elements include:
Change of Address
Household Identification Number
Census Collection District (CCD)
Micro Segment
Affluence
Household Income
Length of Residence
Relations
Under the proposed Australian CCR legislation, use of
expanded shared credit information is not allowed for
direct marketing.
The CCR legislation allows pre-screening for direct
marketing, although the provisions are so restrictive the use
of such information may limit the value to a credit provider.
Despite these restrictions, it will be interesting to observe
how organisations use the expanded data for pre-screening,
and whether the business benefits, however potentially
small, are nevertheless still worthwhile.
As a word of caution, the proposed legislation has extremely
punitive sanctions for organisations that breach the direct
marketing and pre-screening provisions. The potential
commercial and reputational damage is very significant.
Age
Life stage
Adults at Address
Gender at Address
Children at Address
Mosaic and DataPlus elements can be attached
automatically at the point of origination using the Experian
QAS suite of products. Enriched data elements are appended
by matching against the Postal Address File.
Many Experian Decision Analytics clients use QAS solutions
at origination, although most do not append Mosaic or
DataPlus. Where Mosaic is used, it is generally exclusive
to marketing, with no link to back-end credit risk systems
and databases.
2 Pacific Micromarketing Ltd supply the data for all the DataPlus sets apart from Australia Barcode Sort Plan Number, Australia Change of Address and Customer Barcode.
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Risk Adjusted Marketing
Section 4
4.4 Bringing it Together: Credit Risk Rating and Mosaic
Most sophisticated banks have customer credit scoring models
which in turn map into customer risk grades.
Customer scoring models generate a relative risk ranking i.e.
how risky is the customer compared to all other customers in
the portfolio?
Figure 6: Mosaic Segment B08 Professional Knowledge
A
verage propensity score
Customer scoring models take into account behavioural
patterns e.g. repayment history across all lending products
held by the customer. The models are often supplemented
by behavioural information for non-lending products, such
as savings accounts or term deposits.
A
nnual value ($)
No. of customers
5000
4000
Organisations without full customer scoring often substitute
this with a customer risk grade driven from Basel II loss
metrics (Probability of Default, Exposure at Default and Loss
Given Default).
3000
2000
1000
Improved data infrastructure is a significant by-product
of banks’ investments in Basel II compliance.
No. of customers
0
Within the credit risk function, most banks have
sophisticated data warehouses, capturing rich customer
transactional and behavioural data.
In the absence of an integrated, common, customer-centric,
credit risk and marketing database, Mosaic and DataPlus
elements can be retrospectively appended to each customer
record to provide an enriched, external view of the customer.
Customer
Mosaic
Risk Grade
Other
C0344897
B08
16
Transactor
This complete customer profile provides a rich and powerful
risk adjusted view, which supports more informed and
intelligent marketing strategies through segmentation
analysis and predictive modelling.
Importantly, customer risk is factored in before a marketing
campaign is launched, helping to break down one aspect of
the typical sequential, siloed process discussed in Section 3
of this white paper.
Risk Adjusted Marketing
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Recommended steps in the process are:
1
Profile
customer base
on Mosaic
Figure 5: Mosaic Profile
9
ry
Ve
Annual value ($)
2
Mosaic profile analysis
Customer
analysis
Product
portfolio
analysis
Product
channel
analysis
3
4
5
Review
customer
management
relationship,
performance
Embed
Mosaic
segmentation
into customer
management
process
Further
application,
eg. scorecards,
optimisation,
etc.
Step 1
Append Mosaic and DataPlus elements to every
customer record.
Step 2
Understand and analyse the customer base with respect
to Mosaic segment, customer risk grade, and any other
combination of segmentation variables that will drive the
marketing campaign.
Section 4
Figure 7: Sample Customer Population Profile by Mosaic Segment
Analyse each Mosaic segment to help answer the
following typical business and marketing questions:
Figure 8: Mosaic Population Distribution for Revolvers
and Transactors in a Credit Card Portfolio
Is the Mosaic group under or over-represented in the
customer base?
Who are the most profitable customers?
What is the customer risk profile?
What is their propensity to respond or purchase?
What is the typical product mix of customers in this
segment, and what opportunities for up-sell or cross-sell
does this present?
Do I want to market to these customers, and if so, what is
the best communication channel?
Should I bother marketing to these customers if their
profile is associated with high risk and I am unlikely to
approve any new credit applications?
How can I be smarter in meeting my Responsible
Lending obligations, by factoring in customer risk
in my marketing campaign?
Which Mosaic segments do I want to match against the
prospect universe to actively market to?
Standard analytical techniques (regression analysis,
cluster analysis etc.) in combination with Experian Decision
Analytics and Experian Marketing Analytics toolkits can be
used to perform analysis to help answer these questions.
Mosaic D & E:
Revolving credit
Mosaic A, B & C:
Transactor
For example, Figure 8 is an example of segmentation
analysis for a Credit Card portfolio, using Mosaic as a key
segmentation variable.
The entire customer base was categorised by ‘active’
card holders, for example, customers who have had
a transaction in excess of $100 in any of the last
three months.
These customers were then split by their utilisation
behaviour, namely Revolver or Transactor:
• Revolvers pay their minimum balance each month, are
extremely profitable (due to interest paid) and a higher
credit risk.
• T
ransactors pay their balance in full each month,
are not very profitable and a lower credit risk.
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Risk Adjusted Marketing
Section 4
For the Revolver vs Transactor segments, the diagram
shows how each Mosaic code is represented in the ‘active’
card population relative to the overall population average.
Overlaying the credit risk dimension, this information can
inform a more intelligent, risk adjusted, customer focused
marketing campaign.
Steps 3, 4 and 5
Analysis created in Step 2 for marketing will also
create complementary insights for ongoing credit risk
management.
To close the loop, these can be deployed operationally
where Mosaic is available in a production environment.
Existing credit risk strategies for origination and customer
management can be refined, taking into account the
Mosaic profile.
These strategies are easily deployed using a decision
engine like Experian Decision Analytics Strategy
Management Generation 3.
Figure 9: Mosaic Strategy Management Generation 3
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Risk Adjusted Marketing
Mosaic also opens up other analytical opportunities to
better manage credit risk in the portfolio. In the wake of
the Global Financial Crisis, Experian Business Strategies
have improved the ability to forecast portfolio losses using
Mosaic as a key portfolio segmentation variable.
In summary, different Mosaic profiles respond
differently to macro-economic shocks, improving
the accuracy of forward-looking loss forecasting
and stress testing models.
Section 5
Experian’s Vision:
A Single Customer View
Experian’s vision for a more customer-centric view of analytics to drive
marketing and risk strategies is built on a single customer view database.
Financial institutions hold many separate databases and can
find it hard to achieve a single customer view. For example,
an individual may hold a credit card, transaction account and
loan or car and household insurance.
An effective way of creating a single customer view
is to utilise tools that:
First standardise customer details against a known source
of data;
Identify like records through phonetic and other
sophisticated matching based on defined criteria; and
Finally execute data integration through database
technologies and applied business rules.
Experian provides solutions which can bring an integrated
approach to this single customer view creation process.
These solutions can validate and standardise data, like
customer address, against a known source (typically
the Postal Address File), and match records across
databases and enhance data with relevant additional
profiling information.
There are a number of major benefits of adding the validation
and standardisation step to the customer data management
strategy, including:
Accuracy of data
Standardisation of data
Ability to append a unique identifier, which ultimately
assists with matching and de-duplication required to
create a single customer view
Correct Mosaic code appended
The previous section touched on Mosaic as a bridging
step to introduce a wider risk adjusted customer view
to marketing campaigns.
Mosaic remains a key analytical element of the
single customer view database and inaccurate
data can cause issues.
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Risk Adjusted Marketing
For example, if a customer or prospect believes they live in
Richmond but in fact they reside in a neighbouring suburb,
say Burnley or Cremorne, this could result in the wrong
Mosaic profile being applied to the customer and in-turn an
incorrect Risk Adjusted Marketing strategy applied.
Ultimately, Experian’s single customer view database, which
we can call a Risk Adjusted Customer View Database:
Is a solid foundation to address limitations of the typically
disjoint and sequential risk and marketing processes.
Helps align risk and marketing strategies to the customer
rather than the product.
Key benefits of the integrated database include:
Data views aligned with each other, allowing risk and
marketing analytics to define data in the same way where
possible, and differently where the two domains require it.
Reduced data latency.
Enabling joined-up decision making at customer level,
as opposed to the current sequential process.
Improved speed to market, as data views are no longer
fragmented and processes between risk and marketing
are joined-up.
More efficient use of expensive, high-end
analytical resources.
Section 5
5.1 Risk Adjusted Customer View Database in Practice
The following steps articulate how the Risk Adjusted Customer View
database would work in practice, supporting an integrated, risk adjusted
customer view to drive marketing campaigns.
13
1
3
Apply mandatory regulatory constraints to the
database, for example exclude customers under
18, or apply rules to help meet Responsible
Lending requirements.
Factor in customer risk profiles and Mosaic so that risk
adjusted models generate the marketing universe for
individual campaigns. The marketing universe can be
for existing customers, or to profile against a prospect
database to better target new customers.
2
4
Use risk and marketing models for business line
or product applied to entire customer universe,
both generated using the wide set of common
data in the database.
Feed campaign history back into the risk adjusted
models for regular review and refinement of analytical
risk and marketing models and governance frameworks
e.g. organisational risk appetite.
Risk Adjusted Marketing
Section 6
References
1 Mosaic
2 DataPlus
Pacific Micromarketing Mosaic Australia is an innovative
geo-demographic profiling tool that uses aggregated
consumer data to provide highly predictive analysis of the
Australian population. Mosaic is used by businesses to help
them understand consumer trends and behaviours across
Australian neighbourhoods.
Pacific Micromarketing Ltd supply the data for all
the DataPlus sets apart from Australia Barcode
Sort Plan Number, Australia Change of Address
and Customer Barcode.
Mosaic does not use personal information to provide
details about individuals or their addresses. The underlying
data used to build to Mosaic is privacy compliant.
For more information about Mosaic and Pacific
Micromarketing visit www.mosaicaustralia.com.au
and www.pacmicro.com.au
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Risk Adjusted Marketing
For more information visit the Pacific Micromarketing
website at: www.pacmicro.com.au
Section 7
About the Author
Jean Abraham
Business Consultant, Experian Decision Analytics
Jean has worked for Experian Decision Analytics for
13 years, initially from 1998 to 2005, and rejoining in
2006. The year in between was spent at Group Risk
at ANZ. In his first 7 years with Experian, his main
areas of focus were Analytics, Portfolio Monitoring and
Basel II. Since returning to Experian, he has worked
on Strategy Manager design for customer acquisition,
Strategy Simulation and Strategy Tree Optimisation.
His most recent consulting engagements have focused
on impacts of the introduction of comprehensive credit
reporting in the Australian marketplace.
Jean has worked for an extensive number of Tier 1
financial services clients in the Asia/Pacific region
and is currently a Business Consultant in Experian’s
Customer Analytics and Consultancy Team.
Author Acknowledgements
Thank you to my Experian Decision Analytics
and Marketing Services colleagues for their ideas
and input to this white paper:
Dave Audley
Jerry Ying
Michael Morris
Manu Panda
Oliver Moore
Simon Trilsbach
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Risk Adjusted Marketing
Jean has been involved in the delivery of major
projects. These include Basel II modelling and design
of application and behavioural credit risk management
decision solutions that enable Basel II compliance,
design and implementation of Strategy Manager
for the Mortgage portfolio of a major regional bank,
and introducing automated decisioning tools into a
previously manual SME environment.
About
Experian
Experian is the leading global information services company,
providing data and analytical tools to clients around the
world. The Group helps businesses to manage credit risk,
prevent fraud, target marketing offers and automate decision
making. Experian also helps individuals to check their credit
report and credit score, and protect against identity theft.
Experian plc is listed on the London Stock Exchange (EXPN)
and is a constituent of the FTSE 100 index. Total revenue for
the year ended 31 March 2012 was US$4.5 billion. Experian
employs approximately 17,000 people in 44 countries and
has its corporate headquarters in Dublin, Ireland, with
operational headquarters in Nottingham, UK; California,
US; and São Paulo, Brazil.
To learn more about how Experian can help improve the success of your
marketing strategies by integrating a Risk Adjusted Marketing approach
email [email protected] or call +61 (03) 8699 0100
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Risk Adjusted Marketing
Experian Australia
Level 6, 580 St Kilda Road,
Melbourne, VIC, 3004
T+61 (03) 8699 0100
F +61 (03) 9600 4676
[email protected]
www.experian.com.au