I’ll admit it: I’m something of a late joiner to the attribution management bandwagon. Over the last year or so, though, I’ve come around, and I attribute that (pun totally intended) to some great clients and some vigorous discussions with both platform providers and practitioners.
What I’ve seen — and was a victim of myself — was confusion about what true and meaningful attribution actually is. While it’s widely understood (and pined for!) that attribution management is intended to get beyond the “last click” — factoring in the contribution of each of multiple consumer touchpoints that lead up to a conversion — there are fundamentally two different approaches to actually achieving that goal…and one of them has some pretty major flaws.
There seems to be limited awareness and very little acknowledgment or discussion of these two different approaches, which is a problem. Marketers know what they want — accurate and meaningful attribution of credit to each of their channels and initiatives — but the conversation gets murky in a hurry when it comes to how to best achieve that goal.
Basic Attribution Is Flawed
Basic attribution is the approach that web analytics vendors tend to promote as “attribution management.” It’s more about cross-session visitor tracking — tracking which channels drove a visitor to the web site over time, and then enabling the marketer or analyst to choose how they want to distribute the credit among those channels (e.g., 40% to the first touchpoint, 40% to the last touchpoint, and the remaining 20% distributed between all touches that occurred in between). This is attribution, but it’s attribution with a couple of non-trivial shortcomings:
- In most cases, this attribution is limited to “clickthrough” traffic — it tends to ignore the impact of impressions, because impression data is not something that is readily available within many web analytics implementations
- The marketer has to choose how to distribute credit among the tracked channels based on their own opinion and instincts. This is reminiscent of medieval medicine: “We believe this disease is caused by an excess of blood, so we’re going to bleed the patient. If he still dies, we either didn’t bleed him early enough, or we didn’t bleed him enough” is really not all that different from, “We believe that the last touchpoint contributes 3 times as much to the ultimate conversion as all prior touchpoints combined (a ‘last touch = 75%’ model).” In modern times, marketers don’t inadvertently kill consumers with misguided attribution, but the scenarios are similar in that they start with non-fact-based assumptions.
Both of these gaps are troubling. In some cases, various platforms — including web analytics platforms — tackle the first issue through integration with DSPs or other media-based data sources, but the second issue almost always remains.
Advanced attribution, almost by definition, addresses the first issue above. Advanced attribution providers use various techniques for data capture beyond the data available when a visitor clicks through to the site. It’s the second issue, and how advanced attribution can address it, that gets really interesting. True advanced attribution removes “assumption” and “instinct” (i.e., “picking an attribution model”) as the starting point.
One approach to do this is, in a sense, multivariate testing in the absence of the ability to define a control group at the outset. As a simplistic example, think of a series of marketing touchpoints (impressions and clickthroughs) you are tracking at the user level: A, B, C, and D. If you have a group of users for whom you tracked their touches as “A –> B –> C –> D –> <conversion>,” and another group of users for whom you tracked their touches as “A –> B –> D –> <conversion>” then, without choosing how much proportional credit any individual step should get, you can assign (attribute) the value delivered by “C” in this sequence: it’s the incremental lift between the first sequence and the second.
Obviously, this has to be done for hundreds (or thousands) of touchpoint-series. But, even without fully understanding the math and modeling that goes into that, this is obviously a more robust approach to the problem.
And…It’s Not Just Technology
Shifting gears a bit, there’s another wrinkle when it comes to successfully implementing an advanced attribution management program: the technology is just one part of the equation. In this sense, attribution management is no different than web analytics, a data warehouse, or a CRM platform: if you overly rely on the technical implementation on its own to deliver results, you’re liable to stumble on multiple process, communication, and organizational hurdles along the way. With luck, you will be able to recover, but why not eliminate those hurdles altogether? It’s doable, but it requires flexing your soft skills throughout the course of the implementation: establishing realistic and clear measures of program success up front, ensuring your stakeholders understand what advanced attribution is (and isn’t), involving IT early and often to minimize last-minute technical surprises, and developing and rehearsing your process for converting results from the program into action.
I’ve been fortunate to get to partner with Adometry to write a white paper on this very subject: “10 Tactics for Building an Effective Attribution Management Program.” I’ll be reviewing these tactics in a webinar on Thursday, March 20th, at 1:00 PM EDT, and all attendees will receive a copy of the paper. Interested? Sign up!