Let's face it, we all want due credit for our work. That's why marketers so eagerly embraced the death of the presumptive "last click." That mythology forced a paradigm of clear winners (the
content that ultimately converted) versus losers (everything else). Today, we know that's patently untrue. In fact, Microsoft's Atlas Institute estimates that between 93% to 95% of audience
engagements with online advertising receive no credit at all when advertisers review campaign ROI.
Enter attribution modeling. Perfect science it is not, but it is a welcome departure
from the last-click paradigm. What we're really talking about here is giving credit to what happens before that last click or, more accurately, before the conversion or sale takes place. We don't need
this information to establish credit or place blame; we need data to refine our strategy and maximize each dollar we spend. If an online retailer sells 50,000 pairs of shoes in Q1, the million-dollar
question, literally, is how or how much each marketing strategy contributed to the campaign.
So this brings us to some practical lessons on attribution modeling. There's no universal model,
but there are a few tried-and-true principles:
Lesson #1: Don't fall in love with one or two metrics. An attribution model that looks only at site visitors, for example, may
reveal important data on the value of that last click, but it reveals nothing about the placements upstream that pulled those eventual buyers further down funnel.
Lesson #2: Ad servers
can help, but they're only one tool. On its own, an ad server can only track upper-funnel outcomes such as click-throughs and viewthroughs. This adds a piece to the attribution puzzle,
but doesn't complete it. How do you, for example, determine how many clickers abandoned an ad in favor of a Google search, which would then become their final conversion point? This information is
quite valuable.
Lesson #3: Integration of data sources is worth the investment of time and resources. By integrating data from multiple sources, let's say server and
site data, marketers can create a single map that charts all paid media exposure and their link to site outcomes. More sophisticated advertisers also include other "events" in their maps, like
direct marketing e-mails and organic search referrals, which they can then attribute to specific outcomes (e.g., order size or lifetime value). Yes, it requires some extra thinking to build this
integrated model, but the performance reward is worth it.
So integration isn't easy or inexpensive. In fact, gaps will still remain that you'd be wise to close with other available
techniques and technologies. Panels are one example. More and more, marketers are relying on panels to better assign attribution. Here are four ways panels can help:
- To avoid
confusing cookies with consumers, a panel can provide a constant view of consumers to expose anomalies. You know what happens when you assume.
- A panel can provide
important information on how exposure to "earned media" (favorable blog posts, product reviews, viral videos) impacts downstream conversion.
- A panel can be surveyed to determine the
behavioral impact of exposure to ads on TV, radio, and print. Surveys can go even deeper to associate exposure to offline transactions like grocery purchases.
- Panels can
uncover macro trends in the industry that may explain outcomes. If sales are up for all competitors, for example, a panel may show that a market trend, not your strategy, is the reason
for the sales rise. Likewise, if your ad campaign increases competitor conversions, too, perhaps a demerit is due -- instead of credit.
It may sound clichéd -- but in our
industry, experimentation is key to lasting success. Still, without a clear methodology for attribution, you'll likely be among those who mistake winners for losers. If, on the other hand, you reject
the winner/loser paradigm, an artifact from the days of "last-click" thinking, you'll discover a secret many now realize. Attribution isn't a zero-sum game; it's your best way to learn more and earn
more.