Insights from a Conversant ® ExecutiveBy Scott Eagle, CMO

THE ULTIMATE BARE-KNUCKLE BOXING MATCH: ATTRIBUTION VS. MEASUREMENT

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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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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THE STATE OF MARKETING MEASUREMENT

When digital marketing measurement began, the data available and how it could be collected were limitedby 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 clicksand 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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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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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 diminishingreturn 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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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.

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