First-touch, last-touch, multi-touch, oh my!
“A full 54% of marketers say multi-touch attribution is one of the biggest gaps in their marketing research.” eMarketer
There are a number of ways to skin the marketing attribution cat—most of them arbitrary. Indeed, most marketers consider it a flawed approach. In part, that is because most marketing attribution models focus on touchpoints—singular events in the customer journey—rather than the introduction of innovative approaches like Account-based Marketing. Especially for B2B marketers who face long sales cycles involving dozens, if not hundreds, of touchpoints, how in the world are we to gauge which particular touchpoint landed the deal? More importantly, should we?
Of course, we want to channel our hard-won marketing dollars to the most effective sources for driving awareness, engagement, and conversion. But these sources can include site experiences, field events, content offers, trade shows, and digital advertising, just to name a few. Because the B2B customer journey is so complex, marketers are often forced to fall back on overly simplistic ways to evaluate success like first- and last-touch models. Just because we first engaged an account at a trade show does not mean the trade show should be credited with the entire deal. Likewise, just because the account downloaded a certain eBook right before they signed a contract does not mean the eBook should be credited with the whole deal. In this way, first- and last-touch models can be simply arbitrary assignments of attribution.
Multi-touch models don’t necessarily solve the problem either. We might say a first touch is 20% responsible for the deal, a last touch 10%, and the rest of the engagements split the remaining 70%.
No attribution model is going to give us a perfect blueprint for how to close a deal; nor is it going to give us a definitive answer for whether our broader strategy is working. The only way to know whether Marketing was truly influential in winning more business is to exclude control groups from particular programs or initiatives.
The only way to know whether your ABM program is moving the needle in a measurable way is to compare it to a control group of accounts that are not part of your ABM program. But even then, if you’re really concerned with definitive direct attribution, you’ll have to ask whether or not it was your targeted advertising, personalized home page or getting sales to act on marketing insights that moved the needle. The problem is, none of these efforts operate in a vacuum, and trying to isolate them such that they do is impractical at best, impossible at worst.
But we can take a page from the pharmaceutical industry playbook and rely on correlational data to make our marketing investment decisions. If a new drug is given to a group of subjects and 70% of them get better, the drug company does not kill the subject and perform an autopsy to see whether the subject’s physiology actually changed as a result of the drug. Besides being, of course, immoral and criminal, it’s also expensive and time-consuming. It would take far longer to actually bring the drug to market if the drug manufacturer sought definitive attribution. A simple correlation between drug use and better health is the deciding factor on whether the manufacturer will market the drug.
The pharmaceutical industry is driven by innovation. Those who innovate faster win the day. Likewise, B2B marketers who innovate faster outperform their competitors. Rather than determining whether a particular program or channel is definitively responsible for success, it’s far more meaningful to baseline a success metric, like closed deals, introduce ABM, and see if more deals are closing. This enables the conversation to shift away from who gets ‘credit’ for the accounts that closed, and instead towards a more constructive conversation about the incremental impact of an integrated approach. B2B marketers would do well to use correlational data to determine what works and what doesn’t, rather than going through the expensive machinations of nailing down complex attribution models.
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