With digital marketing, there’s no shortage of data to evaluate. But all of that information brings with it the real challenge of identifying both the metrics we should care about and the best way to measure them. Brands are in the metaphorical seats of a great ROI assessment boxing match: a fight between attribution – focused on assigning credit for sales – and causal measurement – focused on measuring sales actually caused by a program. Sound the same? Nope. They’re as different as Tyson and Ali.

1. Insights from a Conversant® Executive
By Scott Eagle, CMO
THE ULTIMATE BARE-KNUCKLE BOXING MATCH: ATTRIBUTION VS. MEASUREMENT

2. INTRODUCTION
THE STATE OF MARKETING MEASUREMENT
IN THIS CORNER: ATTRIBUTION MODELING
AND IN THIS CORNER: CAUSALITY MEASUREMENT
CONCLUSION
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Table of Contents
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3. With digital marketing, there’s no shortage of data to evaluate. But all of that information brings with it the
real challenge of identifying both the metrics we should care about and the best way to measure them.
Brands are in the metaphorical seats of a great ROI assessment boxing match: a fight between attribution
– focused on assigning credit for sales – and causal measurement – focused on measuring sales actually
caused by a program. Sound the same? Nope. They’re as different as Tyson and Ali.
INTRODUCTION
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4. THE STATE OF MARKETING
MEASUREMENT
When digital marketing measurement
began, the data available and how it
could be collected were limited
by available technology.
Thanks to technological advances, that’s no longer the case. Unfortunately,
many brands haven’t caught up with the very real advances that have been
made in this area. The most common approaches to performance assessment
today have the following flaws:
• They focus primarily on justifying past actions rather than
determining the best courses for the future.
• They count easy-to-measure metrics, such as clicks
and visits, rather than sales effects.
• They assess online impacts only versus impacts across all channels.
• They focus on assigning credit for sales to tactics according to
predetermined percentages, rather than determining which sales
were actually caused by the marketing program.
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5. ATTRIBUTION MODELING
Consider a simple approach to performance assessment: last-click attribution,
for which the credit for a sale goes to the last marketing touch point.
Last-click is easy to measure and allows digital marketers to puff out their chests because all credit for sales goes to digital. Last-click is a
good strategy for measuring some tactics, such as affiliate marketing. But in a cross-channel, cross-device world, does last-click really make
sense for most of what you’re doing? Does the final Google search for a store nearby, for example, deserve all of the credit for driving
conversion? If I spend two hours consuming content to evaluate alternatives on my phone, but switch to my PC to buy, does the last PC
impression deserve the credit when it had nothing to do with my decision?
The problem is that too many people are worrying about who gets credit
for a sale instead of figuring out what actually caused the sale. Attribution
modeling – wait, let me correct that – bad
attribution modeling focuses on a “rules-based”
method of crediting a particular
marketing program with a sale.
Most rules-based attribution
platforms have more complicated
credit formulas than last-click.
A common approach involves giving one-third credit for a sale to the first marketing touch
point, one-third divided among all of the interim touch points, and one-third for the last touch
point. Others have different, rules-based approaches. Intuitively, they seem simple, but not as
“simplistic” as last-click.
Simple attribution is the “showman” boxer with heaps of bravado. People bet on him because
he seems like a winner. But here’s the thing: The flashiest boxers often lose, regardless of their
magnetism, because boxing is about the fight, not which guy has the better publicity team.
Allocating credit via a formula is much easier than determining what actually caused a sale.
But doing so doesn’t get you any closer to knowing what ROI your programs are driving.
IN THIS CORNER:
Allocating credit via a formula is
much easier than determining what
actually caused a sale. But doing so
doesn’t get you any closer to knowing
what ROI your programs are driving.
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6. Our research has shown that as many as 80+%
of the sales that correlate to some retargeting
campaigns would have occurred anyway.
CAUSALITY MEASUREMENT
Fortunately, another kind of boxer is out there to bet on – a
fighter with a three-punch combination of accuracy,
actionability, and comprehensiveness.
We call that boxer “causal measurement.” Its grappling strategy focuses on what incremental
revenue a program actually caused (causation), rather than what sales happened at the same
time that the program ran (correlation). Why is this distinction so important? One common
situation would be crediting a retargeting campaign with credit for any sale it touched, even
though many of those sales would have happened anyway, without advertising.
Our research has shown that as many as 80+% of the sales that correlate to some retargeting
campaigns would have occurred anyway. That doesn’t discredit retargeting as a strategy
– it may still be very efficient. Just not as efficient as it appears to be at first glance.
Further, if we erroneously conclude that retargeting drives four times more sales than it
actually caused, we might decide to pour money into it, relentlessly blasting banners at the
relatively small number of people that visit our site. Thus surpassing the point of diminishing
return and squandering resources instead of reaching out to other potential buyers.
There are a number of methods available to understand
causality. Some companies use algorithmic attribution
models to precisely connect the dots between a given
tactic and the results it caused. But the cleanest approach
to causality, in my view, is to conduct scientifically based
A/B testing using matched samples. One group sees
the marketing messages while the other sees control cell
ads (e.g., public service announcement ads) instead. By calculating the sales made to the anonymized IDs in each group, and then
subtracting the “control” cell sales from the “test” cell sales, you can get a precise measure of the sales that were actually caused by the
program.
This method may not be flashy, but it has a perfect win/loss record.
AND IN THIS CORNER:
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7. CONCLUSION
For the brands in the stands, the prize fight between attribution and causal measurement is still in its early stages. The
betting window is still open. One boxer looks and talks big. The other is a lot quieter, but focuses on his fundamentals.
Which fighter are you putting your money on?
ABOUT THE AUTHOR
Scott Eagle leads Conversant’s global marketing function, including strategy and the integration of marketing
programs across our solutions groups. An accomplished senior executive with a strong background in client-side
digital marketing and consumer brand management, Mr. Eagle has over 25 years of experience as a marketing
leader managing major Fortune 500 brands and building successful new companies. Mr. Eagle has served as CMO
for Empowered Careers, eHarmony and Claria Corporation, and he has held management positions at Concentric
Network Corporation, MFS Communications and P&G. Mr. Eagle holds a B.S. in economics from The Wharton School,
University of Pennsylvania, and serves on the board of Akademos, Inc.
ABOUT CONVERSANT, INC.
Conversant, Inc. (Nasdaq: CNVR) is the leader in personalized digital marketing. Conversant helps the world’s biggest
companies grow by creating personalized experiences that deliver higher returns for brands and greater satisfaction
for people. We offer a fully integrated personalization platform, personalized media programs and the world’s largest
affiliate marketing network - all fueled by a deep understanding of what motivates people to engage, connect and buy.
For more information, please visit: www.conversantmedia.com
Thanks to CMO.com for publishing portions of this content first.
Copyright 2014 Conversant, Inc. All Rights Reserved. Conversant is a trademark of Conversant, Inc.
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