Brand Building with B2B Advertising
Every marketer knows that there are few more enduring competitive advantages than a strong brand. This is the same in B2B as in anything else.
“Brand” as most people think about is a combination of three factors:
Awareness of your company in your target market
The reputation of your company in your target market
Visual Identity
It makes sense then that when people want to allocate budget to brand building they turn to advertising. After all, where else can you reliably get your company in front of more people in a favorable light, emphasizing your key USPs and positioning?
This sounds good. But there are major pitfalls that come with Brand Advertising for B2B, and these pitfalls mean that most properly measured brand advertising campaigns fail.
The default for B2B brand advertising
To know the pitfalls of a B2B brand campaign, you first have to know what one typically looks like.
This is what these campaigns generally look like. The company:
Picks channels with enough volume to reach users (primarily Facebook and Youtube, sometimes LinkedIn).
Picks targeting that makes sense for their ICP.
Runs ads to them optimizing for impressions, video views, or a similar top-of-funnel metric.
Performance is measured in one of three ways:
Simple top-of-funnel metrics - How many views, clicks, and video watches your campaign got.
Survey before and after - If we survey users before and after the Brand spend, does it show improved survey results after the spend?
Lift test - Did we see a lift in company metrics in the time/place we ran the test?
Where do B2B brand campaigns go wrong?
The fundamental problem is with targeting.
Companies that run brand campaigns want to unlock new consumers that their direct response campaigns are not reaching, so they want large audiences to serve brand ads to.
On platforms with large audiences - Facebook, Instagram, TikTok, Youtube, etc - the targeting you can select is not good enough to work for B2B on its own. What makes it work is when you can set up an algorithmic feedback loop on a conversion event.
What is an algorithmic feedback loop? Here’s an example. A company makes a campaign targeting HR managers, optimizing for lead form fill. The platform tries to serve people who are most likely to fill out a lead form. Someone is much more likely to fill out a lead form from an ad targeting HR managers if they are, in fact, an HR manager, so the algorithm gets trained to find HR managers.
If you’re optimizing towards top-of-funnel metrics like views or clicks, which almost all brand campaigns do, there is a much weaker algorithmic feedback loop. This means even though you’re targeting nominally good audiences, your spend is largely wasted on the least qualified members of your audience.
It’s even worse than it sounds because ad platforms use auctions to determine the price to reach a given user.
The users that you normally reach when optimizing for leads are much more expensive per impression because more people try to get into the auction to target them.
Users that no one normally wants to reach are cheaper because fewer advertisers are in that auction.
Since you’re optimizing for top-of-funnel metrics, the algorithm will try to get them as cheap as possible - which means targeting the least qualified portion of your audience.
What does work for B2B brand advertising?
There are two ways to make brand advertising work.
1) Precise Audiences
If you have a small, proven audience - on LinkedIn this can be a carefully crafted target audience, on any other platform this can be an uploaded/targeted list - you’re not relying on an algorithmic feedback loop nearly as much.
What do I mean by proven? Ideally, it is an audience that worked well for you for direct response campaigns, or worked well for targeting with your sales team.
If this sounds familiar to you, it’s because this is basically the same as an ABM strategy. Get a precise list of accounts, hit them with a series of ads, and measure the overall impact at the end. It’s not as flashy as brand advertising but it avoids the main targeting pitfall.
2) Top of Funnel Content
If you’re going to promote to a large audience, instead optimizing for a top-of-funnel event where there’s no feedback loop, optimize for an event where you can still qualify the user. Content, eBooks, tools, templates, all work so long as there is a qualification step you can tag. As a bonus, this will also get you some direct response results too. You can still measure this campaign like a Brand campaign to make sure you’re seeing the full funnel impact of your work.
How do you measure brand campaign results?
At the top of this article, I mentioned that usually performance is measured in one of three ways:
Simple top-of-funnel metrics - How many views, clicks, and video watches your campaign got.
This is not a useful metric because we don’t have any way to verify that these views and clicks are really from our target ICP.
Survey before and after - If we survey users before and after the Brand spend, does it show improved survey results after the spend?
Surveys are not great for B2B for the same reason - the algorithms are even worse at determining that survey participants are in your ICP than they are at selecting them as ad clickers.
Lift test - Did we see a lift in company metrics in the time/place we ran the test?
This is the golden metric for measuring brand campaigns. Specifically, geography-based lift tests.
You need to make sure you have enough data in your test geographies to get statistically significant conclusions.
Here’s a video showing a hack to estimate how much data you’ll need based on geography size.
Exceptions
If you have a non-algorithmic/non-digital ad channel with good enough targeting, you can get qualified impressions cheap enough to make it all net out positively. For instance, I’ve seen data showing strong results from OOH billboard ads at airports for enterprise SaaS companies. The cost was low enough and the impressions for business travelers high enough that it had an obvious positive impact on the business.
In Summary - B2B Brand Building with Advertising
1) Don’t optimize for clicks, views, or video views if you are targeting a large audience, it will lead to too many non-qualified views.
2) Instead use narrow, proven audiences OR optimize on top-of-funnel content downloads that you can qualify.
3) Make sure to estimate the sample size you’ll need for stat sig results before you set a budget and geo combination. You usually need more geographies/budgets than you think.
That’s it, we hope you found this helpful!
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