The document discusses how organizations can move from a risk-focused strategy to a more customer-centric strategy using data. It explains that with increased data from sources like social media, customers now have more choices and expect personalized experiences. It recommends that companies gain a multidimensional understanding of customers using data from various sources and statistical models to predict customer behaviors and segment customers. This allows companies to develop customized products and services tailored to individual customer needs and maximize customer satisfaction and profitability.

1. Using data to
understand and act
upon a customer’s
needs and preferences
Moving
beyond risk to
a customer-
centric strategy

2. Moving beyond risk to a customer-centric strategy
1
Expanded Choice, Expanded Insight
1
IBM, “Big Data at the Speed of Business”,
http://www.ibm.com/software/data/bigdata/what-is-big-data.html
Developments in the Internet, social media and the mobile
world have fundamentally changed the way people interact
with one another and how they conduct business. Today’s
consumers are more informed and have access to more choices
than ever before. They can review, compare and contrast
products and services in detail, when they want, in the comfort
of their own home. All consumers now are empowered to
educate themselves before they make a decision on what
product to purchase from which supplier.
With an expanded array of choices at their fingertips, these
highly informed consumers are expecting highly personalized
products and services that focus on their needs and respond
only to them. Consumers no longer tolerate the idea of “one
size fits all.” Why should they, when it’s so easy to find another
supplier whose services are more appropriate to their needs?
Customers expect these products and services to be
delivered how, where and when they want them —
usually immediately.
These more knowledgeable and discerning shoppers are
forcing organizations to step up their game or risk losing
profitable customer relationships to competitors.
The evolution of technology, the Internet and, most recently,
mobile has enabled this large and continuing increase in
customer awareness and education. However, these same
technology enhancements also have provided companies
with an opportunity to deliver more focused and appropriate
products and services to these customers.
Both customers and lenders have access to exponentially
increasing data. The successful lenders now and those of the
future will be able to transform this data into consumer insight
that they then can act upon. This helps them ensure they are
offering the right products and services to the right consumer at
the right time, optimized for the right channel. The unsuccessful
ones either don’t have access to the data or aren’t able to use the
data in a meaningful way to enhance their customer proposition.
To make the most of this opportunity in a highly competitive
marketplace, organizations need effective strategies and
processes in place to capture, manage, understand and
effectively use the new information available to them —
including real-time, mobile and online data. By developing
a multidimensional view of customers and incorporating
this view into their strategy, companies have the ability to
build customized products and services that ensure each
customer receives the products and services they desire
most — and at the same time maximize the organization’s
strategy objectives.
“The promise of big data has ushered in an era
of data intelligence. From machine data to human
thought streams, we are now collecting more
data each day, so much that 90 percent of the
data in the world today has been created in the
last two years alone. In fact, every day, we create
2.5 quintillion bytes of data — by some
estimates that’s one new Google every four days,
and the rate is only increasing….”
— IBM
1
“The goal is
to turn data
into information,
and information
into insight.”
— Carly Fiorina, former
Executive, President and
Chair of Hewlett-Packard Co.

3. Using data to understand and act upon
a customer’s needs and preferences
2
Building a customer-centric strategy starts
with gaining a full view of a customer’s many
attributes. Whether it’s a large, multinational,
full-service bank or a digital lender start-up,
an organization needs to focus on:
Knowing the customer through
data — Gain a complete customer view
by bringing together and enriching data from
internal and external sources.
Understanding the customer with
models — Extract the right information from
the data, and apply statistical models to predict
customer behavior from many different perspectives.
Differentiating the customer by
applying strategies — Develop segmentation
strategies that let you apply personalized decisions
and treatments that best fit each customer profile.
In this connected, data-driven world, businesses need to
enhance their decisioning strategies and product features
to meet customer wants and needs. Otherwise, those
customers will go elsewhere and quickly find a supplier
that can support their needs.
An effective decisioning strategy must go beyond the
traditional dimension of simply evaluating risk alone.
It also must incorporate a dimension for customer
requirements, thereby creating a much more complex
and data-driven customer-centric decision strategy.
Traditionally, increased data access, enhanced decision
capabilities and flexibility often have been associated
with larger organizations that have the infrastructure and
resources to harness and use it. In today’s world, these
more informed consumers no longer associate personalized
products with only large organizations. Sophisticated
consumers expect the same service, instant decisions
and tailored products based on their needs and activities —
Toward a Customer-centric Strategy
Businesses have been successfully applying the above three
core principles for years. Now by applying rich data and
more sophisticated decisioning tools, organizations can take
these basic principles a step further. They can support more
educated, demanding customers and create a foundation
for building new products and services to support future
customer needs.
regardless of whom they are dealing with. More
recently, there are many new, emerging, technology-rich
organizations that are agile enough to harness the data
to embrace a more customer-centric strategy. These new
organizations are dramatically changing the way financial
services industries are looking at and using data.

4. Moving beyond risk to a customer-centric strategy
3
Customer data once was limited to understanding an
individual’s level of risk. Businesses have long had access
to customers’ credit activity and performance data, such
as payment performance, exposure, collection activities
and more. This type of credit reporting agency information
has been a cornerstone of risk decisioning for years and will
continue to play a critical role.
Today, the scope and scale of available data are increasing
exponentially, providing a more complete understanding
of customers. For example, relationship data is providing
improved insight into how firms have interacted with
customers in the past. It’s also showing how customers
behave and are performing with the products they have
purchased. By harnessing existing relationship data fully,
companies can increase their understanding of a customer’s
preferences and behavior dramatically.
Gaining a Deeper View of Customers
As the variety of customer interactions grows, organizations
also are taking a closer look at customers’ channel
engagement preferences. They can monitor which channels
customers use and determine which ones are most effective
for specific activities. For example, a firm could determine
whether a letter, a phone call, SMS or an email is most
effective in generating customer contact. Organizations
also can monitor how and when customers use particular
channels. All of this information can be captured, analyzed
and used to enhance how and when products and services
are positioned to customers.
This multidimensional view of customers, if harnessed
properly, can help organizations gain greater insight about a
customer compared to the traditional approach of assessing
whether a customer can afford a product or service. It
can enable them to really understand and predict what
customers want, why they want it, when they want it and
how they want it delivered.
As the variety of customer
interactions grows, organizations also
are taking a closer look at customers’
channel engagement preferences.

5. Using data to understand and act upon
a customer’s needs and preferences
4
It’s clear that companies have access to a vast array of
data that can give them a more complete perspective on
customers. The next challenge is putting all that data to use
to help understand and predict customer actions. Statistical
models are a powerful tool that helps organizations explain
how a customer is behaving today — and how they are likely
to behave in the future.
Statistically developed models have been used successfully
for years to evaluate the single dimension of risk. Assembled
primarily from data available from credit reporting agencies,
at one time these types of traditional models were the only
dimension lenders considered.
Today, decisions are no longer one-dimensional and
risk-focused. With a wealth of data available from internal
and external sources, companies are developing models
that can be used to predict much more than just risk.
They are applying models to predict everything from
credit card use, to which types of rewards are most
appealing and a customer’s potential profitability and
lifetime usage value. All these then can be used to
define the products and terms that will be provided
to that customer.
The more predictive the data and modeling capabilities
are, the better an organization can understand and
predict a potential customer’s needs and future behavior.
The better the organization can appropriately tailor its
products, services and communications to fully meet
each customer’s needs.
Take-up
Response
Channel Lifestyle
Loyalty
Usage
Risk
Value
Fraud
A suite of models, each
predicting a different
consumer behavior,
trait or preference,
can be developed
and deployed to truly
understand your customers.
Extending Understanding With Models
Customer models

6. Moving beyond risk to a customer-centric strategy
5
With a rich range of data knowledge distilled into an in-depth,
multidimensional understanding of customers through models,
organizations can move forward, building a strategy to differentiate
their customers. With the right strategy, firms can segment customers
into focused homogeneous groups and then match the right product terms
to the groups. The strategy can define product terms such as credit limits,
APRs, and applicant acceptance and declines. Agility is important in
a fast-changing environment, so firms need to be able to roll out these
strategies quickly and effectively.
In the past, a decisioning strategy could be based on limited data, such
as a customer’s credit score. Today’s decisioning is more complicated
because of the additional data available and the ability to use that data
to segment customers. An effective strategy should support:
Constant testing and learning, to continually refine
the strategy while maintaining business agility
Flexibility and agility to change product terms and
introduce new features in a highly dynamic marketplace
Minimizing risk exposure while maximizing insight into customers
Robust monitoring of decisions and performance in the
short term and the long term focusing on KPIs
While statistical modeling and analytics have become more sophisticated
and accurate over the past 20 years, the final proof of whether a new strategy
is better comes only from deploying it into a live production environment.
Thorough testing and robust monitoring are essential to making a final
decision before rolling out a new strategy. It starts with a detailed definition
of a winning strategy. Organizations need to define key KPIs, such as profit,
take-up rates, acceptable delinquency levels and other variables in advance.
They should determine which level for each KPI represents success or
failure. Using consistent data and carefully defined testing, firms can fully
develop, refine and optimize their strategy before making it operational.
Creating a Strategy to
Differentiate Customers
Today’s decisioning
is more complicated
because of the additional
data available and the
ability to use that data
to segment customers.

7. Using data to understand and act upon
a customer’s needs and preferences
6
Case Study:
A large direct-to-consumer lender had an outdated new loan
origination process in place to select mail customers from
an existing database. The prior selection process (due to
restrictions in data access and modeling skills) was based
solely on performance of existing loans. Due to the lack of
data insights, new loan origination campaigns experienced
extremely low response rates, approval rates and conversion
rates, which resulted in a high cost per acquisition (CPA).
In addition, the lender received numerous complaints from
existing loan customers who were mailed a new loan offer,
applied and then subsequently were declined.
A Customer-centric Solution
With guidance from Experian’s Global Consultancy, the
lender was able to implement a new customer-centric
solution that increased customer satisfaction, improved
KPIs and produced a lower CPA. The new strategy consisted
of an investment to overhaul the current process with
new software, data and modeling capabilities. The new
data sources, including bureau-based information to link
mailing decision with approval decision, customer attitudinal
and surveyed preference data. The lender also moved
from single-dimension risk scoring to include models for
likelihood to respond, likelihood to convert (postresponse)
and likelihood to retain the loan. The improved customer
profiling enabled the lender to introduce varying customer
offers, including APRs, loan amounts and application
incentives. Following a rigorous testing process and
small-scale live trials, a full program rollout occurred.
Since the launch of the new customer-centric solution,
the lender experienced an initial double-digit improvement
in response and conversion rates. The lender subsequently
introduced an ongoing program of continual refinement
and review.
Consumer lender focuses on a customer-centric
marketing approach to improve loan origination KPIs
Developing an effective customer strategy is not a single
one-off initiative, but an ongoing process. The traditional
approaches to knowing, understanding, differentiating
and interacting with customers still are valid and pivotal
to a good strategy. Many organizations still are basing their
decisions on risk alone, since that approach has worked for
them in the past. However, in a competitive, fast-changing
environment, it’s time to put the rich variety of available
customer data to use to develop customer-centric strategies
that provide products and services that educated customers
want and are profitable for the organization.
By developing a full understanding of customers based on
multiple data sources, organizations gain the insight they
need to make better decisions about their products and
services. They can match new and innovative products
and services to the right customer, at the right time, via
the right medium, creating benefits all around.
Customers will be more satisfied and loyal, because
they are using the products and services they desire and
are most suitable for them. The firms that serve them gain
improved growth and profitability, increased retention and
positive word of mouth. By moving from a one-dimensional,
risk-based focus toward a more multidimensional view of
customers, organizations can build a business that’s better
aligned with their customers — now and in the future.
Conclusion: Understand Fully,
Precisely Target, Consistently Execute

8. About Experian Global
Consulting Services
Experian’s business consultants provide clients with exceptional
strategic credit risk-management insight, detailed enhancement
opportunities and deployment strategies. They ensure consistency
through deep business subject matter expertise, client familiarity
and a proven client engagement methodology.
Experian
®
builds a strong partnership with clients at all levels,
delivering a balanced portfolio of improvements and needs close to
the relationship to ensure implementation plans are delivered and
benefits are realized. Clients are left fully equipped and empowered
to enhance and sustain profitability and focus in their business.
Experian serves clients in more than 50 countries and offices in more
than 15 countries, delivering more than 700 engagements worldwide
with a rigorous focus on quantifiable benefits.
© 2015 Experian Information Solutions, Inc. • All rights reserved
Experian and the Experian marks used herein are trademarks or
registered trademarks of Experian Information Solutions, Inc.
Other product and company names mentioned herein are the
property of their respective owners.
02/15 •2000/1203 • 7103-CS
Experian
475 Anton Blvd.
Costa Mesa, CA 92626
1 888 414 1120
www.experian.com
Jon Hudson
Senior Business Consultant
Experian Decision Analytics’ Global
Consulting Practice
Jon Hudson provides senior-level
risk-management consulting services
for financial services organizations.
With more than 20 years’ experience in credit risk management,
Hudson offers Experian clients consulting services that
mitigate consumer credit risk by managing everything
from customer acquisition and account management to
predelinquency and collections.