Not too long ago, I shared how your revenue team can measure lift — meaning the difference in your business results — from its advertising efforts. This methodology is the best way to see the overall impact of advertising on your business.
However, this approach isn’t suitable for measuring and optimizing day-to-day advertising performance. Measuring leads isn’t the answer, either.
In fact, any measurement that is based on a specific user misses the impact of B2B advertising. The goal is to influence a buying team in a company. We need to measure the behavior of that team, not an individual.
So how do we measure success? We need to make two changes to effectively measure B2B advertising performance.
Change #1: Move Measurement from the Individual to Account
Today, digital advertising performance is based on the individual: Did the person we reached come to our site? Did the person who clicked also watch the video? But in B2B, we need to know if the behavior of the buying team changed. Aggregating individual-level behavior doesn’t get us there.
Consider this set of actions:
- Your advertising reaches four individuals
- All four visit your website
- Two watch an explainer video
- The other two reach the pricing page
If these four buyers are all part of the same buying team, that’s a big deal! But if they’re four unrelated people from four different companies, it may make only a minor impact. Here’s the problem: The traditional ways of measuring digital advertising can’t differentiate between these two scenarios.
That’s why the first change to make is to measure account-level reach, spend, and outcomes.
How 6sense Can Help
Fortunately, 6sense makes this easy with account reach, spend, and a wide range of account-level outcome metrics that enable performance measurement and optimization. This includes:
- Account-level click rate (aCTR)
- View-through rate (aVTR)
- Impact on account-level engagement
- And more
Change #2: Adopt Influence-Based Measurements
We know that one ad, one email, or even one phone call won’t be the sole source of a new opportunity. And when we expand our view to the entire buying team and all the interactions they have with our marketing, the shortcomings of traditional attribution approaches become even more apparent.
Instead of a solution built around an attribution model, you must switch to influence-based measurement. In other words, you need to answer the question: Are the accounts we reached progressing with us in ways we care about? After all, the goal is to impact these accounts.
Traditional digital advertising measurements simply cannot answer these important questions:
- Are the accounts we reached with our advertising more engaged?
- Are the accounts we reach more likely to have new open opportunities?
- Do we have higher win rates in the accounts we reach in our advertising campaigns?
You can’t rely on attribution methodology to clearly see these outcomes. You need an unadulterated view that lets you see which accounts you have reached and how the outcome for those accounts has changed.
If you hear, “Well, those accounts would’ve progressed anyway because of everything else we are doing,” that’s true. Some would progress, but not all. That’s true for traditional user-level measurement as well — and across all of your marketing activity.
This is why measuring overall lift is the best way to measure the business impact of your advertising investment. It directly addresses this objection. Once you know that advertising drives your core metrics, you can focus on maximizing your impact measured based on influence.
How 6Sense Can Help
6sense makes this easy to measure by reporting on increased engagement, new opportunities and closed won deals for the accounts you reach in every single campaign.
To measure and optimize the impact of B2B advertising, make certain you’re able to report across the buying team in a company, and that you aren’t missing the impact because your measurement is built around each individual.
Instead, adopt an account-centric method for measuring every part of your advertising campaign and use influence-based metrics — rather than an attribution model — to make the best optimization decisions.