In 1983, David Ogilvy finished his most impactful work, Ogilvy on Advertising. The book was a culmination of the decades of advertising experience Ogilvy had accumulated during his reign at the world's largest ad agency, Ogilvy & Mather.
His book is a pseudo-textbook, written like a manual that discusses how to make great advertising. In the first paragraph of the book, he plainly states, "The point of advertising is to drive sales." This same mantra is how most of our bosses see our roles. Our jobs are to drive revenue, not just create pretty pictures or fun stories. So how do we prove we are driving revenue?
Many marketers have found the best way to show our impact to sales is through attribution modeling. The theory suggests that buyers behave in a linear fashion, which makes it possible to attach a value to every marketing touch point. This helps marketers prove their value by showing their direct impact on revenue in the form of ROI metrics.
Despite its wide adoption and good intentions, attribution modeling has not done marketers any favors. Let's explore why attribution modeling devalues your role, the major flaws in its assumptions, and other ways you should consider looking at your data.
Attribution Modeling Assumes Too Much
Attribution modeling uses a linear progression of events to attribute value to each campaign in succession. If you saw this campaign, then bought the product, it must have been a result of your previous marketing engagement, right?
This is the basic premise of attribution modeling. It assumes that all other events are held constant and that the only impact on your decision was the marketing campaigns you touched. This is its first major flaw.
The attribution model doesn't account for social perceptions, society's norms, personal beliefs, or any of the thousands of other influences we have in our lives. We cannot--and should not--assume that our single campaign is the only reason a person engages.
This has been proven with multiple bits of research. A famous example you may remember is the "theory of seven touches," which stated that after being exposed to an idea seven times, it becomes a part of your evoke set. This means that you would know, understand, and remember the stimulus or idea the next time you were exposed to it. However, if a person sees campaign number eight, and we attribute all revenue to this last campaign, we are neglecting the other seven campaigns, which did the majority of the work for us.
Attribution modeling does take multiple campaigns into account by suggesting that ROI should be shared across all of them. This is another giant fallacy because it assumes that each campaign had an effect on you, or that you even noticed it at all. There is no easy way to tell what the effect of an ad was on a person without asking them, and since even then you never know, we are making a lot of big assumptions on things that can never be proven.
Attribution Modeling Overemphasizes ROI
ROI allows you to prove the direct return on dollar spent. In the marketing world, this means showing the value of any campaign by tracking its direct impact on revenue. This brings us to the second big problem with attribution modeling. The model's focus on ROI forces a marketer to look only at spending, which is an unactionable, lagging indicator that is not tied to any core business goals or representative of any real marketing value.
ROI can be useful in planning, but only to the extent that it allows you to ask, "Do we do this again?"
It does not tell a marketer what they should change within their campaign. It does not tell a marketer why it failed, and it does not tell the business anything about its future. It only tells a company about its past. Companies' main goals are usually tied to their future rather than their past, because they value things that can make them money faster, help them better predict future outcomes, or maximize efficiencies. ROI is not used to help accomplish any of these goals, yet it is the way we prove our value, and the only number attribution modeling focuses on.
When attribution modeling attempts to break up revenue and attach it to a campaign to prove value to a company, it is only doing so by showing how well the money was spent, not how well it helped accomplish business goals. This type of reporting is keeping marketers from proving their real business value because it doesn't account for all of the value marketing is actually producing, only how well they spent their budget in the past.
There Is A Better Way
There is a much better way to show the value of your efforts--you just have to look to find it.
Consider matching your efforts to core business goals. This alignment will help you to truly be indispensible, rather than being viewed as a cost center. Here are a few things to measure and consider when it comes to proving the value of your marketing campaigns:
At its core, attribution modeling tries to answer some very complex questions quickly and easily. If you are subscribed to the attribution model, make sure you are aware of its shortcomings and consider investigating other ways to prove your value.
Otherwise, you'll always just be looked at as a cost center, and not a business driver.
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Many of ClickZ's leading expert contributors will be at ClickZ Live, the new online and digital marketing event kicking off in New York (March 31-April 3). Hear from the likes of: Jeremy Hull, Lisa Raehsler, Andrew Goodman, Bryan Eisenberg, Mathew Sweezey, Aaron Kahlow, Stephanie Miller, Simms Jenkins, Jeanne S. Jennings, Dave Hendricks and more!
Mathew is the head of thought leadership for B2B marketing at Pardot, a Salesforce.com Company. A consummate writer, he has been featured in numerous publications such as Marketing Automation Times, DemandGen Report, Marketing Sherpa, ZDNet, and is the author of Marketing Automation for Dummeis (Published by Wiley February 2014). As a speaker Mathew speaks around the world at events such as Conversion Conference, Dreamforce, SugarCon, and to companies including Microsoft, Investec, NetJets, and Restaurants.com, to name a few.
March 19, 2014