I’m as big a fan of marketing metrics as just about anyone. Plunk me in front of a spreadsheet, and I’ll tinker away happily for hours on end. That said, I’m also aware that misguided metrics can drive misguided behaviors. Tell a marketer she’s being judged—or worse, paid—by the number of booth visits during a trade show and watch that metric skyrocket, even while quality diminishes and conversion rates plummet.
The good news is that marketing leaders are increasingly tying themselves to business outcomes. In fact, according to Forrester, 82% of CMOs say their goals align with revenue targets. However, the bad news is that they don’t have a way—at least not one that’s transparent and trusted across the organization—to measure progress against those goals.
At first, it seemed as though multi-touch attribution would provide the answer. With a multi-touch approach, marketers have the ability to map every touch on every account and opportunity and then dictate which touches are most influential in driving any given deal. All of this sounds well and good in theory; however, when it comes to putting a multi-touch approach into practice, marketers are finding themselves running into the following challenges:
Marketing skepticism: Modeling decisions have a dramatic impact on channel performance metrics. If, for example, you switch from a first touch to a last touch model, nothing at all has changed about what happened to an account during the course of its sales cycle, but the new model gives credit for that revenue to a different channel. As a marketing analyst, the switch might be completely justified; as a channel owner, it can be frustrating and demoralizing.
Sales skepticism: Sales has a hard enough time ceding that Marketing has any impact on their deals, so rolling out a complex model that takes credit any time a prospect happens to bump into a marketing program doesn’t do much for our credibility. And even if Sales agrees with the data— say the prospect did, in fact, attend a webinar—they disagree with the conclusion that it mattered.
Channel rivalries: Attribution examines a finite number of dollars and splits them across the channels that happened to influence the deals involved. Channel owners measured this way necessarily try to prove they own the biggest slice of the revenue generated, but the real question should be how much more revenue was generated because of their efforts.
Conflicts with an account-based strategy: Instead of focusing only on the highest quality accounts, marketers are tempted to hedge their bets and blanket ALL accounts in order to get at least some of the credit for as many opportunities as possible. This, of course, runs counter to the whole point of the analysis, which is supposed to help you figure out how to be more efficient with your marketing efforts.
We all aspire to understand what drives revenue, but unfortunately, that understanding isn’t as simple as looking at existing revenue numbers and doling out credit to the various channels that happened to influence each opportunity.
Attribution models miss the glaring question of what would happen if you DIDN’T invest marketing resources against those accounts. Simply overlaying an attribution model on top of an existing funnel can be just as dangerous and misinformed as not measuring anything at all.
So how do you think about performance metrics that will help you link your efforts to business outcomes? Whether you are looking to gauge the impact of your overall Marketing strategy, a particular channel, an orchestrated campaign or an individual tactic, the question remains the same: What incremental impact does your investment have on the accounts that were targeted?
In order to answer this question, your analytics should:
Start at the audience level: Whether you’re running a campaign, testing a new tactic or tracking your broad target account list, almost everything you do starts with a group of accounts. Fundamentally, your combined marketing efforts are designed to drive these accounts through the funnel and your analytics should at least show where these accounts stand: Are they visiting your website? Engaging with your content? Converting into opportunities and closed/won deals?
Compare to a control group: This might be the most critical—and overlooked —component of all. Without a control group, you have no way of knowing whether a particular set of accounts was impacted at all by your investment. You can develop and execute a plan against a set of accounts, and you can report on the number of those accounts that turned into closed/won deals, but would they have converted anyway? Setting aside a group of accounts,ideally ones that are firmographically similar to your target accounts, enables you to understand not just how the audience performed, but how much better it performed than a group of accounts that didn’t get the same level of investment.
Consider opportunity rate: What percentage of your target accounts are converting into sales opportunities? This critical stage bridges Sales and Marketing and serves as a meaningful mid-funnel metric that helps with forecasting. While revenue is certainly powerful, and should be tracked, it can be a challenge to have to wait 6-18 months for those results. Start with the percentage of your target accounts that are building qualified pipeline.
Data is incredibly powerful but can be dangerous if interpreted in a way that doesn’t acknowledge the realities of complex B2B sales cycles. Attribution works fairly well in a B2C model, where individual tactics really can be linked to specific deals because an entire buying cycle might boil down to one click on a shoe ad. Meanwhile, in B2B, there are almost always multiple channels and multiple buyers and influencers involved in a deal.
Forcing an arbitrary split of revenue ultimately confuses the organization, breeds counterproductive rivalries and undermines an account-based marketing strategy. Instead, you should embrace the importance of cross-channel collaboration by analyzing the incremental impact of your investments into specific sets of accounts.
You can learn more about how we approach ABM Analytics here.