Attribution Modeling ~ Banners, Blondes, & the Bottom Line | Last Click News

Attribution Modeling ~ Banners, Blondes, & the Bottom Line

Published on February 22, 2011 by Mark Hughes   ·   No Comments

C3 Metrics CEO on solving the mystery of your missing profits

Raymond Chandler was the archetype detective storyteller, fond of using hard-boiled heroes, cold-blooded “thugs,” and hot-to-trot “dames” as his characters. One of his favorite quotes was: “I do a great deal of research…particularly in the apartments of tall blondes.”

But solving mysteries with diligent research is also part of our daily regimen in online advertising. Every network, keyword, and overall campaign is a puzzle that sometimes changes week to week. The puzzle is solved with attribution modeling, but we’re just now scratching the surface.

We’d like to think research methods we employ provide facts relevant to “our case.” But, incorrect facts will “never hold up in court.” Today, the facts are wrong.

Despite that Internet advertising (a $70 billion/yr global industry) is the most trackable form of advertising on earth–the facts determining success of those billions of dollars, won’t hold up much longer.

Why? Sadly, today’s outdated online ad tracking systems erroneously give 100% credit to the very last clicked or last viewed ad before a transaction.

Mark Hughes, CEO
C3 Metrics

Example: if four Internet ads contribute to a transaction; today’s outdated systems allocate entire credit to the very last ad, completely ignoring the first three ads which actually drove the revenue.

Zero credit to revenue drivers, and 100% credit to the last ad placed. It’s like discovering the prosecutor put away the wrong guy. Bad facts, bad decisions, bad outcome.

Members of the jury…this is a $20 billion global problem.

Now enter attribution modeling: at C3 Metrics (disclosure, I’m the CEO), a robust attribution model takes an enormous amount of ingredients, and reduces complexity to simplicity.

At a basic level, C3 Metrics’ attribution modeling system assigns credit to Originators, Assists, and Converters within a transaction. An attribution model should capture every online media source from the top of the funnel where sales originate…down to the very bottom of the funnel. So in a $100 transaction, an Originator would receive a fraction of $100 attributed to them—and the Assist and Converter would also receive fractional credit of the $100 attributed respectively.

100% of revenue credit is attributed and split among Originators, Assists, and Converters–accounting for the actual drivers of revenue. Then revenue and respective costs from paid media sources converge in a single, elegant ratio in the attribution model: Attributed Revenue-to-Spend Ratio (ARSR™).

It’s a simple ratio any marketer can grasp: attributed revenue divided by corresponding spend. If you have a 4.0 ratio for a specific keyword, or specific Display campaign–you’re getting $4.00 in revenue for every dollar spent on that particular media source. If you have an ARSR of 1.25 for a particular media buy—you’re getting $1.25 in revenue for every dollar spent there.

For brands that don’t transact dollars on their site, they simply assign a revenue value for: a dealer zip code lookup, configuring a vehicle online, or scheduling an appointment online.

ARSR delivers knowledge ready to act on, versus information barely ready for analysis. The special sauce of the attribution model is the numerator of the ratio (attributed revenue). Media buyers easily identify media sources with high ratios to scale, and low ratios to cut or improve. Instead of taking weeks to analyze, it’s about an hour.

But the jury wants the facts, and here they are: in the longest running attribution modeling study of its kind (2 yrs) the results are enough to get anyone promoted:

a) 25%+ higher revenue on same ad-spend producing millions of dollars in incremental profit
b) Display ROI improvement of 160%
c) Search ROI improvement of 98%
d) Accurate economic model to measure affiliate performance

Case closed. The verdict: millions in profit added to the bottom line.

Are you ready to solve your case with the right facts?

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