Next Generation Marketing Measurement

Building a next
generation marketing
measurement system:
the framework
M er kl e Tho u g hT l eader s hip se r i e s

A Database Marketing Agency

Building a next generation marketing measurement
system: the framework

executive summary:
marketers struggle more than ever to develop accurate, reliable,
and consistent metrics around the performance of marketing
spend. relying on incorrect metrics creates a snowball effect that compounds
measurement errors along the entire marketing decision process. decisions on
how much to spend on marketing in total, how much to spend by media, and how
to coordinate a multi-media program are all directly influenced by the metrics
used. for these reasons, the importance of evaluating your current measurement
framework and building a solid foundation can’t be understated. n

Marketers already employ processes to link a purchase with a marketing activity to create metrics on
marketing performance. These marketing measurement systems fall into one of two camps, each with
its own shortcomings:
• Top-down attribution approaches that provide such a high level view that they fail to be actionable, or
• Bottom-up attribution approaches that present a collection of very detailed information but fail to
integrate into a single view.

Better solutions do exist and can be achieved through sophisticated analytical and technological
techniques. Merkle introduces a next generation measurement system based on Probabilistic
Contribution (PC) scores (see definition on page 5). rather than attempting to link a purchase
to a marketing activity, a contribution score is created, by integrating top-down and bottom-up
measurement systems, for each factor that potentially influenced the purchase. The key advantage is
integration - bridging multiple media/channels and points of time for a single purchase to provide a
comprehensive view of the consumption pattern.

This white paper, the second in a series of four on marketing optimization, addresses the critical
elements needed to build a next generation marketing measurement framework. A third paper will
discuss the data, infrastructure, and software tool requirements. and a fourth and final white paper will
outline how to effectively integrate this system throughout the marketing decision making process.

Building a next generation marketing measurement system: The framework | © 2008 Merkle 2


five factors fuel marketing’s roi dilemma
What makes answering such fundamental questions around the ROI for marketing spend so difficult?
Some of the main reasons for this include:

1. Consumers face daily ad bombardment. studies consistently show that consumers are exposed to
a mind-numbing one to two thousand advertising impressions per day. The sheer volume of
advertising makes drawing a line from messaging to consumer activity all the more difficult.

2. fragmentation of media. The nearly monolithic media channels of previous decades have
splintered in the age of the internet, on-demand television, and satellite radio. For instance, the
number of television channels increased over a hundred times in the last thirty years. The growing
consumer trend of multitasking while accessing multiple media – especially prevalent in young
consumers – throws a wrench into marketers’ ability to attribute action to marketing message.

3. failure to consider the complexity of consumers’ decision process. a purchase is not directly
tied to the most recent advertising exposure, despite the fact that most organizations attribute sales
to marketing spend in this manner today. purchase decisions are exceedingly complex and
consumers are likely to be influenced by multiple advertisements over time and different types of
media.

4. Consumers are in control. Consumers have more choice than ever over when and how they
consume media, making it that much more difficult for marketers to even know if a message
was received.

5. organizational challenges. Most marketing groups organize by media type creating a silo effect
(e.g. brand marketing, direct marketing, and interactive/online marketing). These silos each develop
their own goals, metrics, infrastructures, and partners to maximize the impact within the media they
can control.



Critical elements of marketing measurement
Marketing organizations require the following key elements to effectively build and implement next
generation marketing measurement:

1. Analytical acumen to custom-build a solution to link all marketing activities to consistent
performance metrics (i.e. measurement framework)

2. Data availability: Access to a complete, accurate, and frequently updated marketing data universe

3. Infrastructure (Media management database)

4. Integration into business decision making (belief/buy-in and integration through software tools) and
integration across all lines of business.

Terms Used In This Paper
Standardized language will be used in this paper to provide relevance across different industries. A few
notable cases are highlighted below. Standardized terms are defined as follows:

outcome of interest – This paper will use “purchase” to represent a generic term that could be applied
to any performance measurement a company may use. Throughout the discussion, purchase can easily
be replaced with response, new customer, revenue, profit, LTV, or any other metric that may be used to
calculate desired outcomes. While it is true that some details related to techniques and implementation
may change depending on the metric used, the discussion throughout this paper will be broad enough
to encompass any of these metrics.

media/Channel – A distinction will be made between media and channel, with media representing
communication flowing from the company to the consumer, and channel representing communication
from the consumer to the company. Additionally, media will be discussed in three classes; direct, mass,
and interactive.

top-down and Bottom-up – The top-down and bottom-up approaches are designed to represent
two common ways marketing organizations measure the performance of their media. The top-down
approach starts with all media spend and then breaks the media spend into media, region, and
timeframe. This is the broadest perspective commonly used in media mix analyses, but rarely takes very
detailed information into account (individual level data, creative, quality of advertising, etc.). in contrast,
the bottom-up approach starts with the very detailed level of information. This approach is common in
direct and interactive media analyses (consumer segments, predictive models, decile analyses, etc).



attribution – The term “attribution” represents any process designed to link a marketing activity with
an outcome of interest (i.e. purchase). Typically, an attribution process attempts to link the purchase with
a single marketing activity or campaign. More sophisticated, and less common, attribution processes
apply probabilities across marketing activities to capture cross-media influence more accurately.

This paper outlines a new approach to marketing measurement by:

1. Setting the stage through a discussion of typical attribution processes and the current challenges of
marketing measurement systems.

2. introducing analytical framework for the next generation marketing measurement system. This
paper describes a new metric called Probabilistic Contribution score (PC score) used as the key
metric to link marketing activities with the outcomes of interest.

3. providing guidelines on the analytical approach required to calculate pC scores across all marketing
activities.

Probabilistic Contribution Score:
The Probabilistic Contribution Score (PC Score) is a value assigned to any activity or event (marketing or
otherwise) that influences the consumer toward a desired action (i.e. purchase a product). PC Score derives its
name from:
• Probabilistic – The sum of all the values for any given purchase equals
• Contribution – Each value represents the relative contribution or influence specific activities or events have
on consumers’ decision making process
• Score – The values are designed to be informative within a given purchase or when rolled up to
summarized reports

Current Challenges facing marketing measurement systems
If asked, many organizations claim to have robust and reliable processes to attribute sales to media
spend. Forrester Research conducted a survey in 2008 that concluded that 69% of marketers believe
they are at least somewhat effective at measuring marketing ROI. It has been Merkle’s experience,
however, that after digging beneath the surface, most marketing measurement systems fall far short of
robust or reliable, especially when trying to forecast marketing impact into the future. The 2008 ANA/
MMA Marketing Accountability Survey showed that only 10% of marketers felt they could forecast
the effect of a 10% cut in budget, and just 14% said that senior management in their companies had
confidence in their firms’ marketing forecasts. CFO’s are even more skeptical of the numbers. Ninety
percent of CFO’s don’t use marketing ROI metrics to help set marketing budgets. “They don’t believe
the numbers,” said Jeffrey Marshall, the retired editor in chief of Financial Executive magazine.



Another recent Forrester study highlights some of the top barriers marketing organizations currently
face to improving marketing ROI (see figure 1). Many of the challenges Forrester identifies are directly
linked to the four challenges addressed by this framework (analytic sophistication, data access and
accuracy, technology, common metrics and business integration).

Figure 1
marketers face hurdles in staffing, data, technology, and common metrics

Source: Forrester Research, “Database Marketers Evolve Their ROI Measurement”



two Common approaches to marketing measurement
Marketers already employ processes to link a purchase to a marketing activity to create metrics on
performance. These marketing measurement systems fall into two camps:
• Top-down attribution process - providing such a high level view that they fail to be actionable, or
• Bottom-up attribution process - presenting a collection of very detailed systems that fail to integrate
into a single view.

Overview of typical top-down attribution processes
The top-down approach starts with the entire marketing budget and then looks at how the marketing
budget is split between major categories (media, regions, products, etc). The advantage of this
approach is that it provides a framework to compare the performance of all marketing spend with
common performance metrics to one another. This property is crucial to the building of a robust
marketing measurement system, and is the reason why the approach outlined later in this paper uses
the top-down approach as the starting point. This type of analysis is often termed media mix analysis.

Figure 2
top-down and Bottom-up approaches



Certain industries, like consumer product goods and pharmaceutical companies, have a long history of
using media mix modeling to help determine marketing spend. These industries tend to have in-direct
relationship to their consumers and involve purchasing of less expensive products conducive to a short
purchase consideration time (see figure 3). These industries developed media mix modeling capabilities
early on partly because it was one of the few ways to understand marketing performance when there
is no direct to consumer relationship. These industries also benefit from standardized data sources
(like pos data) which have allowed standardized media mix modeling methodologies to develop. as
relationships move more towards direct-to-consumer and the purchase consideration increases, the data
and methodology required for media mix modeling becomes more customized for each company. This
customization requires more investment to develop, and therefore it is typically the companies with
significant marketing spend that can justify the modeling work.

Figure 3
Choosing the measurement approach
PurChase Consideration time

most established top-down
measurement systems

non-direCt direCt to Consumer

*Note – the company placements above are for illustration purposes only. In some cases a company can be put in multiple areas due to different
product offerings. For example, a Dell desktop computer would be in the top right (as shown) whereas a replacement ink cartridge for Dell printers
would be in the lower right.



However, there are some significant weaknesses to the top-down approach, including:

1. it takes too long. it is not uncommon for this type of analysis to take over six months to complete.
The deliverable is often a presentation of how media performed for the analysis time period along
with recommended changes. By the time the recommendations are presented, the marketing
landscape may have already changed, making the analysis outdated before it is even presented.
Another way to view this pitfall is that media mix work is often managed on a project basis, as
opposed to an ongoing system.

2. it fails to take into account the specifics within each media. There are a number of options and
choices when spending dollars within a media. If the budget for that media is being cut back
due to historical poor performance, then perhaps the allocation of spend within the
media should be changed, not necessarily the size of the budget. For example, there are many
decisions and analyse within a direct mail program that are not adequately summarized
by simply when just total dollars spent.

3. lack of integration with media execution. Media mix analyses often conclude with
recommendations on how to shift marketing dollars between available media. It is rare for these
analyses to address the complexities associated with the execution of each media. of particular
importance are inventory and cost constraints. For example, a recommendation to increase spend
in spot TV may require introducing new day parts or networks which typically changes the price to
purchase the media. as cost per impression increases, the relative effectiveness of that media is
likely going to decrease.

These weaknesses can be summarized by the project vs. process nature of the analyses. These analyses
are often conducted ad-hoc without becoming part of the marketing measurements systems. By not
being fully implemented into the marketing culture, these types of results rank as merely interesting –
but not relevant enough to change marketing strategy and spend. Additionally, because the top-down
measurement approach fails to take specifics into account there is a need for the more detailed bottom-
up approaches.

Overview of bottom-up typical attribution processes
The bottom-up approach starts with detailed information within a media and then establishes a system
to measure performance of segments or campaigns within the media. The nature of the data and
the type of metrics calculated vary depending on the media discussed (which is part of the problem).
Some metrics are efficiency based (cost per impression) while others are performance based (cost per
purchase, ROI). It is more common to see performance based metrics available where it is easier to
connect media spend directly to individual level purchases.



Direct and indirect attribution are two commonly used practices as the foundation for bottom-up type
metrics. A brief overview along with some examples and pitfalls are highlighted below.

direct attribution is often classified as an attribution process that uses some kind of unique identifier
to link a purchase to a specific marketing activity. examples include asking customers to provide a
specific code from the back of their catalog , a unique 1-800 number that is only used for a particular
run of a television advertisement, or tracking a click on a banner ad by directing to a specific landing
page. While each of the examples above has their own merit, they ultimately fail in producing a
reliable attribution process. They fail because the direct attribution is only recording the last marketing
exposure before the purchase, but not necessarily what is driving the purchase intent in the first place.
A common example is unique 1-800’s on TV ads. A TV ad may drive desire to purchase, but people
don’t use the unique 1-800 number on the ad, but rather search for the product on Google, go to a
landing page, and then use that unique 1-800 number. The TV ad is given less credit than it deserves,
and search engine marketing gets more credit than it deserves.

indirect attribution, or business rule attribution, uses some assumptions to link a responder to a
marketing effort. For example, a unique 1-800 number is not used but we know that the consumer
received a catalog two weeks prior to the purchase. In this case we use indirect attribution to assume
the catalog drove the purchase. This process has some advantages and disadvantages over direct
attribution.

Indirect attribution allows a marketer to attribute a purchase when direct attribution is not possible.
However, indirect attribution rules come down to little more than an educated guess. How long should
the time window be? What if we know the consumer received a mail piece one week ago? One month
ago? Six months ago? This decision will drastically impact the perceived performance of the direct mail
campaigns. even worse, the pattern feeds on itself (up to a point). if it looks like direct mail performs
well then a company will do more direct mail, which leads to an even higher likelihood that a consumer
who purchases your product received a mail piece within the time window. This assumption inflates
direct mail performance metrics even further. eventually the direct mail performance metrics will drop
off as volume increases, leading to a perceived equilibrium relative to other marketing options. However
this equilibrium is typically far from the true equilibrium.

a common pitfall for organizations is the sense of sophistication associated with very complex direct
and indirect attribution rules. The attribution rules continually become more refined and complicated,
leading the marketer to believe they have a world-class marketing attribution system and therefore are
doing very well at linking purchases to marketing activities.



Most of the problems associated with direct and indirect attribution systems can be placed into two
general categories: cross-media/channel tracking and effects over time.

1. Cross-media/channel tracking – Most attribution systems attempt to link a purchase to a single
marketing communication, whereas in reality consumers do not just use one source of information to
make a purchasing decision.

2. effects over time – multiple advertising exposures may contribute to the ultimate purchase even
within a single media, but most attribution systems will just attribute the purchase to a single
instance.

Current marketing measurement systems ultimately leave CMOs with two problems:

• an inability to see the whole picture. Today’s systems force CMOs to look at either broad
brush metrics that fail to consider specifics of individual media or at detailed metrics within a
media that fail to translate into metrics that can be compared across media.

• a disconnect between budget planning and execution. Typically, the budget planning
process has two steps: first, each media or channel is allocated a certain budget to spend and
second, each media or channel attempts to gain the best performance it can from the
allocated budget. This process effectively creates a major planning constraint where the
budget by media is considered fixed. Each media may be doing the best it can given the
budget, but the process is sub-optimal when considering all marketing spend together.
The optimal process would enable a fluid budget allocation by media depending on
performance, opportunity, and cross-media interactions. Of course the budget allocated to
each media can be changed over time but the process to do so is typically arduous, requiring
an effort to prove the performance benefit of a media with respect to other media, which
becomes futile since there are no common metrics to compare.



introducing a next generation measurement system
The next generation measurement system forgoes the classical approach of attempting to link a
purchase to a marketing activity. Instead, a contribution score is created for each factor that potentially
influenced that purchase. The key advantage of the probabilistic contribution score (PC Score) is that
it bridges multiple media/channels and points of time for a single purchase. While the scores have
little bearing on measuring media for a single purchase, the reporting makes perfect sense when
summarized.

Determining Probabilistic Contributions
The goal of the next-generation system is to assign a PC Score for every marketing communication that
a consumer may have been exposed to. The sum of the PC Scores for any given consumer purchase will
equal 100%. Depending on the scope of the measurement system, PC Scores can be computed for the
following categories and non-marketing activities (the list below is not exclusive):
• Mass media
• Direct media
• Pricing
• Promotion (i.e. sales or other special offers)
• Creative, messaging, versioning
• Brand awareness or baseline effect
• Natural and economic environmental factors
• Competitive actions and spend
• Legislative or regulatory changes
• Service levels and customer satisfaction ratings

The scope of categories a particular organization will want to tackle depends on the purpose, amount
of available data, sophistication of analytical skill set, and organizational readiness. Media mix modeling
would be an accurate description if this approach is limited to mass and direct media. Marketing mix
modeling is a more suitable descriptor if pricing, promotion, and environmental factors are considered.
In our experience, just using the model above to incorporate mass and direct media alone will produce
a system more advanced than the majority of measurement systems today. Merkle would recommend
adding a number of other factors deemed to have the most impact. These components usually include a
combination of pricing, promotion, brand/baseline effect, and competitive spend.

assuming a system is in place to assign the pC score for every purchase, then extracting meaningful
information from the system is a matter of rolling the data up to the appropriate level. For example, if
we just want to know what media provided the best ROI, we would roll up the PC Scores associated
with every purchase over a specific period of time. The result will show total number of units sold for
each media used. once the total cost for each media is factored in we can get the roi for each media.
A similar roll up logic can be applied to geographies, customer segments, products, etc.



Probabilistic Contribution Requires an Integrated Approach
The process to assign PC Scores requires a combined top-down and bottom-up approach. The process
is a two-stage process, with the top-down approach laying the rough baseline and the bottom-up
approach refining the weights where possible.

The process of integrating the top-down and bottom-up approaches is best understood through the
following simple example. For simplicity, assume the following events take place:

1. The marketer uses only two media to sell their product, TV and direct mail.

2. This company places three spot TV advertisements in a particular region at weeks 1, 3, and 8.

3. Additionally, the company executes a direct mail campaign at week 6.

4. A purchase is made at week 9.

5. The company uses unique 1-800 numbers to track the performance of every marketing activity.

6. The consumer who made the purchase did not use one of the unique 1-800 numbers available but
was mailed a dM piece 2 weeks prior to purchase.

The top-down and bottom-up approaches ultimately produce very different answers to the question of
what marketing activity drove that purchase.

Bottom-up approach scenario
The bottom-up approach would likely use business rules around the 1-800 numbers. For example, the
following rules could be applied:

1. If the consumer uses one of the unique 1-800 numbers to make the purchase, then attribute that
purchase to the marketing activity using that 1-800 number.

2. If a number other than one of the unique 1-800 numbers is used, then check to see if the customer
was mailed a Direct Mail piece within the last three months. If they were mailed then attribute the
response to the mail piece via indirect attribution process.

3. If neither 1 or 2 apply, then allocate the purchase to a brand awareness bucket.

Since the consumer did not use a unique 1-800 number but was mailed within the three month time
period leading up to the purchase, we could conclude that the purchase was due to the direct mail
piece.



top-down approach scenario
Another way to approach this process is to use the top-down or media mix modeling approach. In this
approach, we estimate the effective media exposure through an ad-stock transformation (see figure
4). The concept is that a marketing activity can produce purchases over an extended period of time.
For example, a TV advertisement could cause a consumer to call and purchase immediately, or could
lead to the consumer purchasing weeks or months later. Using ad-stock transformations we create the
effective media exposures for each media at any given time. Figure 4 shows this process, where the
red line represents the effective TV exposure over time and the blue line represents the effective direct
mail exposure over time. Since the purchase occurred at week 9 we can multiply the effective media
exposure by their coefficients estimated through the media mix models to get probabilities that the
purchase was due to each possible marketing activity. In this case we may conclude that the purchase
was 60% likely due to the TV exposures, 10% due to direct mail exposure, and 30% due to neither (i.e.
brand awareness).

Figure 4
top-down or media mix modeling approach

PurChase made
effeCtiVe exPosure

week

The two approaches listed above produce nearly opposite answers. The top-down concludes that TV
had the biggest influence in the purchase, whereas the bottom-up concludes that the purchase was due
to the direct mail piece. The top-down approach more effectively integrates the influence of multiple
media over multiple time points, but fails to take into account some detail-level information (i.e. if that



consumer received a mail piece or not). On the other hand, the bottom-up approach takes the detail-
level information into account but uses rigid business rules to conclude an all or nothing answer.

an integrated approach to measurement
Merkle suggests an approach that utilizes the best of both methodologies (see figure 5). The top-down
approach is used as the baseline since it has the desirable properties of a consistent performance
metric across media and naturally takes into account cross-media impact and impacts over time. But the
result of the top-down approach is modified based on the known, detail-level information available. In
the given example, we would start with the top-down solution but then adjust the probabilities by the
detail-level information known. For example, we know two important pieces of information about the
consumer: first, we know that the consumer did receive a mail piece two weeks prior to purchase, and,
we know that the consumer was scored as a decile two name using a likelihood to purchase predictive
model. Given this information, the integrated outcome is 50% likelihood due to DM, 20% due to TV,
and 30% due to general brand awareness.



Figure 5
an integrated approach utilizes the Best of Both methodologies

aPProaCh logiC result

Top-Down Approach PurChase made
Likely responded to TV (60%)
effeCtiVe exPosure

May have responded to DM (10%)
• Media mix modeling Neither TV nor DM (30%, i.e.
• Aggregated data Brand)

week
Quality

Integrated Approach Media exposure detail above AND Likely responded to DM (50%)
Customer received mail piece at time point 6 May have responded to TV (20%)
• Top-Down enhanced Customer was decile 2 name Neither TV nor DM (30%, i.e. Brand)
with Bottom-up ….
….
integrated
Quality

Bottom-up Approach If used unique 1-800 number then direct Responded to DM (100%)
attribution (TV or DM)
• Business rules Else indirect attribution (known exposure
• Atomic data to DM)
Else ….

• Did receive mail piece within response
window
• Did not use unique 1-800 number
• So attributed to DM through indirect
attribution (match-back)

This integrated approach retains the key advantage of both the top-down (consistent metric) and
bottom-up (use of detailed information) approaches.



Guidelines for creating PC Scores
The specifics to create the pC scores vary widely depending on the situation, so a description of
a detailed process to create the scores will not be attempted here. Some general guidelines are
described below, listed by category.

top-down influence (media mix modeling)
• Media mix models should be fit at the smallest geographic level possible. DMA-level model
is ideal.

• Caution should be used with respect to small sample size for models at a small geographic level.
Bayesian Shrinkage or other adjustment factors should be considered.

• It is important to keep the media mix models based on as recent data as possible. Consider having a
semi-automated model building process if many models are used (geographic models, product
models, channel models, etc).

Bottom-up influence (Qualitative data)
• The adjustments to the PC Scores based on qualitative data may be analytically driven or decided
based on industry or company experts. Remember that creation of PC Scores is part art and part
science

• The impact of qualitative data varies depending on the media. Knowing that somebody opened an
e-mail and clicked through to your website is very concrete and so would have more impact than
what commercial ran in a dMa (since we don’t even know if that individual saw the commercial).

• Because the qualitative data can change quickly, to have an easy method to integrate new types of
qualitative data into the models (new creative, for example).

testing and Validation
• Because there is no ‘right’ answer, assessing accuracy can be a challenging process.

• When possible, use controlled tests to validate influence of each media. For example, a direct mail
campaign with a random holdout sample will provide the ‘true’ influence of that direct mail piece
since all other factors are controlled for through the randomization.

• Creditability is established over time. Evidence can be gathered multiple ways, the best of which
being when an in market test design produces the results forecast by the media mix models.



summary and Conclusions
Marketers have an unprecedented opportunity to increase the ROI on marketing programs by
implementing a next generation measurement system. By utilizing the best elements of both the
high-level, top-down approach and the detailed, bottom-up approach, marketers achieve a much more
accurate view of their customers purchasing behavior.

The top-down approach is used as the baseline since it has the desirable properties of a consistent
performance metric across media and naturally takes into account cross-media impact and impacts over
time. But the result of the top-down approach is modified based on the known, detail-level information
gleaned form the bottom-up approach. The powerful combination of these two measurement systems
and probabilistic contribution scores provides a much clearer and reliable marketing metrics.

our next paper will focus on the data, infrastructure, and software toolset required to operationalize the
integrated measurement system. The fourth and final paper will focus on integrating the measurement
framework into the business decision making and marketing execution process to ensure value is driven
from the solution.

How Merkle Can Help
Merkle works with several clients to develop the infrastructure and analytics to enable the quantification
of brand engagement across their prospect and customer base and make information-based decisions
on their brand equity.

Merkle specializes in information-based marketing strategies and is one of the nation’s leading database
marketing firms. With a proven track record in developing winning strategies based on information
insight for large consumer-focused organizations, Merkle works with many of the nation’s leading
businesses, including Procter & Gamble, Dell, Capital One, GEICO, and DIRECTV.

Building a next generation marketing measurement system: The framework | © 2008 Merkle


about the author
scott nuernberger is senior director, Quantitative solutions. scott has over eight years of experience
in developing and implementing analytical solutions to marketing programs for many different
companies, including GEICO, AEGON, Nationwide Insurance, MBNA, Fidelity, and Dell. Prior to joining
Merkle, scott worked for american express as a statistician and modeler and taught graduate students
statistical methods and experimental design at Cornell university. scott has dual Bs degrees in Brain
and Cognitive sciences and statistics from The university of rochester, a Ms degree in statistics from
Cornell University, and an MBA from Johns Hopkins University.


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