Download Large-Scale Marketing Campaign Optimization

white paper
Large-Scale Marketing
Campaign Optimization
Using FICO® Xpress Optimization
Suite to Support Multichannel
Campaign Management
April 2013
»» Summary
In the era of Big Data, many of the world’s leading marketers are realizing the need
to significantly ramp up their marketing campaign optimization approaches to a
new level, one that will support “Big Marketing”—the ability to effectively develop,
optimize and execute large-scale campaigns to maximize value, whilst meeting
multiple business goals and constraints. However, the key challenge in meeting
this objective is to cost-effectively, and expeditiously, incorporate the necessary
Big Marketing optimization components and capabilities into current platforms, or
into a new solution.
Referencing case studies in Retailing & Financial Services, this paper
describes how the comprehensive components of FICO® Xpress
Optimization Suite give marketers the ability to quickly meet that objective,
optimizing thousands of actions/offers on many millions of customers, across
multiple channels.
www.fico.com Make every decision countTM
Large-Scale Marketing Campaign Optimization
»» Introduction: The Need
for the Right Market
Campaign Optimization
Technology in the Era of
“Big Marketing”
The explosion in mass direct marketing, event-based marketing and e-marketing over the last
10 years has led to marketing campaign optimization becoming an increasingly important feature
of multichannel campaign management (MCCM) tools. Indeed, Gartner lists campaign optimization
functionality as one of its Advanced Analytics Inclusion Criteria for assessing MCCM vendors.
Marketing campaign optimization (MCO) is defined as: “The ability to balance and coordinate multiple
business constraints to maximize the expected value from one or multiple marketing campaigns.”
MCO functionality enables trade-offs among different campaign execution options, such as:
• Which action/offer to provide to each customer.
• Which channel to use.
• The number of interactions per individual.
• The expected value of each campaign (which could be based on short-term or long term profit,
Return on Investment or any other objective).
Optimization is the
mathematical process
of finding the “best”
decision for a given
business problem. The
core elements of a typical
optimization solution are
shown below
Inputs/Facts
Propensity to
respond to Loan Offer
123 is W%
Cost per
Phone Call = X
Cost of Loan Offer
Pack 3 = Y
Credit Bureau
Score = Z
However, if the past decade’s growth of marketing channels and offers has increased demand for
MCO, today’s conditions make it essential to any large-scale campaign. With the advent of “Big
Marketing”—as data and customer volumes increase, potential actions/offers and channels grow,
and marketing budgets contract—marketers must make the most of their resources. This is where
MCO comes in. It enables marketers to identify which one of a multitude of possible actions for
each customer is going to generate the best results, whilst meeting their many business policies,
goals and constraints.
In effect, it both helps identify what is the right offer for each customer and what will give the
organization the best overall results. However, today it’s not just a question of applying or not
applying MCO. It’s a question of applying MCO technology with functionality suited for today’s
conditions and requirements.
Potential Actions
Constraints
Email Loan Offer
+
Direct Mail Credit
Card Gold Upgrade
Branch Offer fully
featured current
account
SMS advising getting
near limit
+
Not less than
3,000 contacts per
marketing pack
No more than
5,000 outbound
phone calls/day
Total Marketing
Budget for week
<$N
Objectives
Optimized Solutions
Maximize Response
Volumes
+
Maximize Profit
Minimize Direct
Marketing spend
Cross-Sell 5,000
Additional Loans in
the next month
=
Offer Customer A
Loan of 10,000 over
48 months at 8.99%
Offer Customer B an
Investment Review
Offer Customer C
upgraded online
banking service
Limitations of First-Generation Marketing Campaign Optimization Tools
Initial MCO tools, from the likes of SAS and Experian, tended to focus on fairly simple optimization
problems. Their design was usually intended for use on a single channel, utilizing a relatively small
number of inputs (for example, basic propensity scores) to identify the best offer for each customer,
whilst incorporating a small number of business constraints and rules (for example, contact frequency
and other campaign parameters).
© 2013 Fair Isaac Corporation. All rights reserved.
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Large-Scale Marketing Campaign Optimization
In effect, they were used more to rank-order potential marketing offers for customers than to truly
optimize the marketing and communication activity with the customer.
These initial MCO tools often lacked the capacity to deal with large volumes of customers, actions,
inputs, channels and constraints, as they tended to be underpinned by a limited number of fairly
weak mathematical solvers. So if asked to solve more complex problems, they could take an age to
complete, resort to approximations to solve the problem, or even stop working altogether.
The use, by these tools, of a templated optimization model development approach and basic user
and reporting interfaces also restricted their potential use within organizations, many of whom were
looking for more flexibility and/or greater business user control.
Consequently, some leading marketing teams in a range of organizations have started to look
elsewhere, to high end optimization vendors, to meet their MCO needs.
As problems grow in
complexity, advanced
solver engines are needed
that can tackle large-scale
optimization problems.
Organizations that solve
problems efficiently at all
levels of complexity have
a unique competitive
advantage.
New Large Scale Marketing Campaign Optimization Requirements
Over the last year or two, a number of these leading organizations in retail and financial services
have started to utilize the FICO® Xpress Optimization Suite to meet their needs.
Their focus has been on implementing a customer-centric decision-making approach by optimizing
the way in which all relevant and value-adding customer interactions are selected.
They have generally highlighted four core requirements that many current MCO tools
cannot support:
1. Solving Large-Scale MCO problems
The complexities of marketing in today’s highly connected world require new levels of power.
Marketers today—due to advances in information access, new communications technology and
channel proliferation, as well as expanded geographical reach into new markets—have enormous
opportunities at hand. However, without the right MCO support tools, successfully taking advantage
of today’s opportunities can be complicated, and, most likely, severely compromised.
In confronting large-scale MCO challenges, marketers today must have tools capable of:
• Working on portfolios of between 10 million and 50 million customers.
• Assessing thousands of possible actions, offers and services.
• Addressing a range of purposes and strategies—acquisition, retention, add-on services,
cross-sell, etc.
• Providing consistency across multiple channels, with differentiation where required,
across multiple brands.
• Utilizing many hundreds of inputs, scores, segmentations and assessments, including current
and potential future customer value.
• Combining many conflicting business constraints, across many levels, e.g., portfolio, brand,
channel, household, customer, account.
2. Integration with existing MCCM solutions
Many organizations have invested significantly in multichannel campaign management (MCCM)
technology, achieving strong ROI from the improved ability to create, execute and manage
multichannel campaigns.
© 2013 Fair Isaac Corporation. All rights reserved.
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Large-Scale Marketing Campaign Optimization
Case Study: Leading US Retailer
FICO Solution
This business wanted to leverage its extensive retail loyalty
club program to improve the take-up and use of product offers.
Its vision was to align its whole organization around a more
member-centered approach. That required understanding
how a member’s life, goals and preferences shape the products
and services they use, and guide their experience with the
organization. With this insight, the retailer could then focus on
developing cross-channel offers that would be relevant to each
individual member—which in turn would build appreciation for
the brand with each contact. But they needed an optimization
approach in order to balance millions of customer needs with
multiple business objectives and constraints.
For this client, as part of its FICO® Analytic Offer Manager solution,
FICO generated thousands of propensity models at the product
sub-category level for every offer available, which focus not just on
what prospects are most likely to buy, but when they are most likely
to make the purchase. The outputs from these are then used within
FICO® Xpress Optimization Suite to match offers to consumers in a
manner that maximizes key performance indicators (KPIs) — such
as maximize incremental sales that have a positive margin, whilst
allowing for many member- and business-focused constraints.
The solution has provided positive results for the retailer across
multiple campaign concepts and objectives, increasing response
rates by factors of more than x5 on many segments. It has shown
significant on-going benefit:
• Across different customer segments.
• For both low and high tickets items.
• And for new product trials.
However, many of the leading vendors in this space do not currently provide strong predictive
analytic and campaign optimization functionality within their solutions.
Today, many marketing executives are seeking to integrate their existing marketing databases,
campaign management and reporting tools with more powerful analytic and optimization
components, which can provide the scale and flexibility they require, and provide further
incremental value from their existing investments.
Typical high-level requirements are to integrate a large scale marketing campaign optimization
solution between the offer eligibility and fulfilment /customer dialogue elements of campaign
management, as shown below.
Assess
Segment
Score
Customer
Database
Offer
Eligibility
Optimization
Fulfillment
Customer
Dialogue
Outcome
Closed Feedback Loop
© 2013 Fair Isaac Corporation. All rights reserved.
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Large-Scale Marketing Campaign Optimization
The quality of the
optimization will often
depend on the quality
of the upstream models
of propensity, response
rate etc., which allow the
optimization to make
the right decision; so
closed loop feedback is
important to refine these
models based on actual
behavior, but also to take
account of the impact of
different offers.
Then the organization will apply exclusion and qualification criteria to result in an eligible customer
population and all their potential actions/offers. Some marketers then undertake some level of
pre-optimization processing—for example, excluding least attractive or unviable offers to reduce
the size of the optimization problem.
The optimization phase is used to select the one (or more) action/offer to be taken forward with
the customer at that time, which maximizes the chosen objectives and meets all required business
goals and constraints.
This is then fed back to the campaign management tool for fulfilment through and coordination
across the various channels. This then creates the customer dialogue, which consequently results in
an outcome. It is essential that these outcomes are captured and compared to the actions/offers, and
their predicted outcomes. This closed feedback loop will drive improvements to the various inputs,
scores and assessments, which in turn will improve the accuracy of the optimization.
As part of these solutions FICO also provides a number of innovative predictive modeling
techniques to enhance customer assessment, such as time to event and uplift models (also known
as action effect models) that predict how likely the customer is to respond to different actions—for
example, offering a 5%, 10% or 15% discount, within a particular time period, and what the effect
would be on KPIs.
3. Undertaking What-if Simulation & Scenario Analysis
With a large monetary investment at stake, marketers developing large-scale campaigns also
need to plan more precisely before going to market. A sophisticated MCO solution must provide
marketers with easy-to-use simulation tools so they can:
• Modify one or more constraints to see how the optimal output shifts, using
side-by-side comparisons.
• Explore trade-offs and the “efficient frontier“—answering questions such as “how much
incremental profit could we make with more direct mail budget?”.
• Change primary and secondary objectives or challenge current marketing policy rules.
• Understand the potential cost/benefits of different offer types and channels to drive response
and efficiency needs.
• Assess the impact of different market and economic conditions in order to find the most robust
approach or to anticipate such changes.
• Use simulations to inform on expected outcomes, for use in driving pricing negotiations with
suppliers and partners.
A typical simulation dashboard is shown on the following page.
© 2013 Fair Isaac Corporation. All rights reserved.
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Large-Scale Marketing Campaign Optimization
4. Business User Focused Interfaces
Whilst there is an increased focus on having the optimization power and flexibility to solve the size
and complexity of a business problem, there is also a critical need to put this into the hands of the
marketer, the person who understands the business problem and potential solutions.
Consequently, it is seen as essential to have marketer-ready user interfaces that easily provide:
• Graphical tick box and parameterized scenario management interfaces, for setting required
objectives, inputs, constraints and outputs.
• Graphical and tabular report output on scenario results and comparisons.
• Sample level, segment level and drill down analysis to allow the marketer to assess output at the
individual account/offer level.
• Assessments of where the tail end of return occurs, with more accuracy.
• Easily configurable and publishable dashboards of key performance indicators, objectives
and constraints.
© 2013 Fair Isaac Corporation. All rights reserved.
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Large-Scale Marketing Campaign Optimization
Case Study: Large European Bank
This client aspired to implement a more customer-centric marketing campaign approach and needed an ultra-large-scale
optimization solution to meet their business requirements.
Client’s Current Process:
FICO Solution
• Was very product focused.
• Was not consistent across brands or channels.
• Did not factor in the value of customers.
• Did not account for previous contacts.
• Did not support scenario planning.
Their optimization problem was ultra-large-scale:
• Customer base of over 15 million individuals.
• Thousands of potential customer interactions.
• Multiple strategies, channels and brands.
• Multiple policies, goals and constraints.
They needed to run the optimization in a batch window of under
an hour every night.
FICO initially undertook a proof-of-concept project, which
confirmed the client’s requirements were feasible.
A decomposition technique, and use of the parallelization features
of the FICO® Xpress Optimization Suite, was used to solve the
optimization model. This confirmed that the solution could run
within the current infrastructure, on the whole customer base,
within the required timeline.
Additionally, the Xpress-Insight module was used to quickly develop
a series of scenario planning and comparison dashboards.
The results of the proof-of-concept led to the full
implementation of an integrated FICO Xpress Optimization
Suite solution.
The solution needed to work with their current infrastructure,
and they required business user interfaces to support scenario
planning and reporting.
»» FICO® Xpress
Optimization Suite
FICO Xpress Optimization Suite is the premier mathematical modeling and optimization
software suite in the world, with the best tools available to aid the development and
deployment of optimization applications that solve real-world challenges.
The FICO Xpress Optimization Suite has been developed over the last 29 years, continually
bringing new and enhanced approaches to market based on tools and functionality that apply
mathematical programming and optimization solutions to business problems.
It is used across a wide range of industries—from logistics and transportation, energy, food
production, financial services, retail and IT—by organizations such as American Airlines, Avis,
Proctor & Gamble and Amazon.
FICO® Xpress Optimization Suite is used by many leading organizations around the world:
© 2013 Fair Isaac Corporation. All rights reserved.
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Large-Scale Marketing Campaign Optimization
How FICO® Xpress Optimization Suite Supports Marketing
Campaign Optimization
1. Unique capabilities for supporting large-scale optimization
The FICO Xpress Optimization Suite includes a range of capabilities to solve ultra-large problems
and support for distributed modeling and optimization:
• A complete set of state-of-the-art optimization engines that are robust, reliable and faster than
competing solutions.
• An easy-to-learn, powerful and flexible modeling and programming language, Xpress-Mosel,
including a visual development environment.
• Full support for 64-bit and multi-thread architectures.
• Support for a variety of decomposition methods and concurrent solving approaches, some of
which are unique to FICO Xpress Optimization Suite, including:
Decomposition is the process
of breaking large optimization
problems into smaller, more
manageable sub-problems
and solving them either
sequentially or in parallel.
•
•
•
•
Simple Parallel Runs.
Decomposition methods such as Benders & Dantzig-Wolfe.
Column Generation.
Cut Generation.
Consequently, these make available to FICO® Xpress Optimization Suite users a range of approaches
for solving ultra-large-scale problems within realistic time scales:
• Multi-Solver: You can choose to use the solver that is best suited to your problem type, or you
can use several solvers in combination within a single model.
• Multi-Problem: Multiple problems can be defined within a single optimization model, with
the ability to switch back and forth between problems, allowing for the retrieval of solution
information across problem components.
• Multi-Model: Multiple problems can also be implemented as separate models. This is usually
suitable if you wish to spread the process along several threads and execute in parallel. FICO
Xpress suite supports synchronization of concurrent models and data exchange between them.
• Multi-Node: Using distributed computing to handle multiple models, using all the computing
power available in your local network, or potentially running in the cloud.
2. A Range of System Integration Capabilities
Typical Large-Scale Optimization Problems Solved by
FICO® Xpress Optimization Suite
• Schedule crews for 3,400 daily flights in 40 countries.
• Buy ads in 10-15 local publications across 40,000 postal or zip codes.
• Pick one of 742 trillion choices in creating the National Football League schedule.
• Select 5 offers out of 1,000 for each of 25 million customers.
• Select 1 out of thousands of potential offers, action and services for each of
15 million customers.
• Place thousands of products on dozens of shelves in ~2,000 stores.
• Decide among 200,000,000 maintenance routing options.
The FICO® Xpress Optimization Suite
is available on all common computer
platforms and provides a range of user/
software interfaces including a visual
development environment, callable
library APIs in C, C++, VB, Java, .NET and
standalone command-line interfaces.
The developed optimization model
can be compiled, is portable across all
supported platforms, and can be used
to protect your intellectual property
on deployment.
© 2013 Fair Isaac Corporation. All rights reserved.
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Large-Scale Marketing Campaign Optimization
Consequently, it is pretty straightforward to integrate the FICO® Xpress Optimization Suite with
other MCCM tools and solutions, whether that may involve using and feeding back to existing
data warehouses or databases, or integrating with campaign management, business process
management or decision management tools.
FICO regularly delivers custom solutions to clients using a combination of FICO Xpress Optimization
Suite and its industry leading FICO® Blaze Advisor® business rules management system. Typical levels
of interaction include:
• Invoking FICO Xpress optimization as part of a business rules management system (BRMS) Rule
Service to provide input to the final business decision.
• BRMS user interface controlling the inputs and parameter values to adjust the optimization
model and run scenarios.
• BRMS undertaking pre-processing of data and/or potential actions to reduce the size of the
optimization problem.
• BRMS and FICO Xpress optimization interacting throughout a process and sharing inputs,
parameters and code fragments.
3. Simulation, Scenario Planning & Reporting Capabilities
The Xpress-Insight module’s adaptive user interface automatically presents the contents of an
optimization model in business terms, ready for data explorations and what-if analysis.
It allows businesses to deploy, manage and understand optimization models as powerful, agile
applications with no development effort to immediately enhance the decision-making process via:
Business User Enablement
It enables business users to carry out in-depth
what-if analysis and supports better decision
making. Business users can:
•
Work with the optimization model in
business terms.
•
Adapt the data and parameters to create
new scenarios.
•
Turn hard resource constraints into soft
constraints with violation penalties.
•
•
Identify limiting factors and spare capacity.
•
Compare the outcome of different scenarios.
Understand trade-offs and sensitivities
implicit in the business problem.
© 2013 Fair Isaac Corporation. All rights reserved.
page 9
Large-Scale Marketing Campaign Optimization
Enhanced Visualization
Visualization of results is a key component of
any effective use of optimization. Xpress-Insight
comes with configurable tabular and charting
views for data exploration. Aggregated, filtered
data and KPIs can be visualized in a side-by-side
comparison for multiple scenarios.
The modern, clean presentation layer is
extensible with lightweight custom views based
on simple web technology, and
has been successfully integrated with
web services such as Google Maps
(see example left).
A public API allows advanced users to
leverage all manner of additional visualization
components, and XML configuration files allow
for adoption of a customer-specific look and feel.
Flexible Scenario Planning and Management
Xpress-Insight provides advanced and flexible
scenario planning and management capabilities.
It manages the input, configuration and results
data for the optimization models. Data can be
input from and output to virtually any data
source. The user can easily create and modify
scenarios within a business user interface.
Scenarios and their results are persistently
maintained and can be shared with other users.
© 2013 Fair Isaac Corporation. All rights reserved.
page 10
Large-Scale Marketing Campaign Optimization
A Knowledgeable Partner
FICO works in close partnership with its clients and partners, enabling them to get the best
possible performance from the FICO® Xpress Optimization Suite. Through years of expertise gained
in advancing the product, excellent client support, and fast-track product development, FICO
continues to maintain the Xpress Optimization Suite as the leading optimization technology for
difficult and large-scale business problems.
»» Conclusion
With the advent of “Big Marketing”—which combines a customer-centric marketing approach with
large customer databases, and many thousands of actions/offers across multiple channels and
brands—it is important for marketers to use solutions that can scale, meet ever-increasing business
constraints such as costs and regulations, and make the most of available data and resources.
Marketing campaign optimization solutions that combine the power and flexibility of the most
advanced mathematical modeling environment, best-in-class solver engines and business-userfriendly visualization and control are playing an ever increasing and important role in meeting today’s
marketing challenges and opportunities.
FICO® Xpress Optimization Suite adds power and simplicity to large-scale marketing campaign
optimization.
For more information on FICO® Xpress Optimization Suite:
• Visit www.fico.com/xpress.
• To request a trial, visit the FICO Decision Management community at decisions.fico.com.
You may also be interested is these related FICO White Papers:
• From Big Data to Big Marketing: Seven Essentials.
• Solving the Unsolvable.
© 2013 Fair Isaac Corporation. All rights reserved.
page 11
Large-Scale Marketing Campaign Optimization
about FICO
FICO (NYSE:FICO) delivers superior predictive analytics solutions that drive smarter decisions.
The company’s groundbreaking use of mathematics to predict consumer behavior has transformed
entire industries and revolutionized the way risk is managed and products are marketed. FICO’s
innovative solutions include the FICO® Score—the standard measure of consumer credit risk in the
United States—along with industry-leading solutions for managing credit accounts, identifying and
minimizing the impact of fraud, and customizing consumer offers with pinpoint accuracy. Most of the
world’s top banks, as well as leading insurers, retailers, pharmaceutical companies and government
agencies, rely on FICO solutions to accelerate growth, control risk, boost profits and meet regulatory
and competitive demands. FICO also helps millions of individuals manage their personal credit health
through www.myFICO.com. Learn more at www.fico.com.
For more information North America toll-free
+1 888 342 6336
International
+44 (0) 207 940 8718
email
[email protected]
web
www.fico.com
FICO, Blaze Advisor and “Make every decision count” are trademarks or registered trademarks of Fair Isaac Corporation in the United States and in other countries. Other product and company names herein may be trademarks of
their respective owners. © 2013 Fair Isaac Corporation. All rights reserved.
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