I am shocked. S-H-O-C-K-E-D.
If you haven’t read our benchmark report, here’s the average CPO across LinkedIn and Facebook:
CPO on LinkedIn (Left) vs. Facebook (Right) Pre-April 2022
My initial thought was, Why’s this shocking? Why don’t demand generation marketers know how much they pay for opportunities, especially in a down economy? Are they too focused on leads alone and not pipeline?
I concluded the likely reason is they’re too far away from their data. Either they’re always waiting for their Marketing Ops team or agency to turn around reports, or they’re relying on metrics in native ad channels that aren’t tied to pipeline and revenue.
Either way, there’s a divide between demand gen marketers and the data they need to build, launch, experiment, and optimize their campaigns.
This is a solvable problem: The future of excellent demand generation hinges on data independence.
Let’s take a step back. I have a hunch you’re asking yourself, Isn’t demand generation all about collaboration, processes, and moving pieces across teams?
You’re spot on, so I’ll preface this section by saying this:
Demand generation is a team sport. Everyone, including Sales, Marketing Ops, and Customer Success, must work together to keep campaigns flowing.
My point about data independence is less about marketers isolating themselves, and more about them taking the reins of the tools (read: data). To build quality campaigns, experiment at scale, and maximize their budget, demand gen pros need better access to performance metrics. Data is their lifeblood, and nothing should stand in their way of it.
It’s time for marketers to break through roadblocks (and the ensuing disconnect) to shorten the time between insight and action.
I’ve come up with four characteristics of a data-independent demand generation marketer. Imagine a future like this:
Demand gen marketers typically access data in one of two ways (or a mix):
Both avenues have their pros and cons. For example, native ad channels are fairly intuitive and reduce the barrier to entry for paid social advertising. But at the same time, they don’t give marketers access to metrics that tie back to pipeline and revenue.
Meanwhile, tapping a Marketing Ops team or agency on the shoulder is excellent for bandwidth, but you don’t always get answers quickly. They have other priorities—trust me, I used to work in Marketing Ops—and this creates a costly lag between the ask and when marketers actually execute on that data.
In either scenario, marketers are left with an under-equipped toolbox that makes their job tough. That’s a red flag during normal times, but when leadership teams are cutting marketing budgets, the flag is flying a little higher than usual.
This is why the best demand gen marketers will soon be the ones who’ve reduced their reliance on other teams from a data standpoint and taken full ownership of their data. It’s 2023, and it’s time for them to steer the ship without waiting for everyone else to board.
If a B2B marketing playbook existed, “How to drive a low cost per lead (CPL)” would be the title of the first chapter.
I’m fine with that since leads have historically been a B2B marketer’s north star. But demand gen marketers need to read beyond that chapter because chasing leads is fruitless and expensive.
Although I admit CPL and similar metrics are solid leading indicators of paid social success, they’re not close enough to the dotted line to be the primary proxies.
What demand generation marketers need today, tomorrow, and every day in the future is an uninterrupted view of performance from the first impression to closed-won business.
Instead of looking only at CPL, marketers should focus on metrics like upsell revenue and cost per opportunity (CPO). These metrics are closer to actual revenue.
That said, don’t stop tracking leading indicators, like demo requests and meetings booked. They’re still helpful because they ensure you’re on track. Pipeline and revenue are lagging indicators, but board members may not be keen to wait around to see how they play out.
One last note about flipping the switch to pipeline-level metrics: It won’t happen overnight. It took us about two quarters to fully make the move, and it would have been longer had we not set our sights on the downstream impact early on. Marketers making the move should sync with their Marketing Ops and Rev Ops teams ASAP to determine what they’re tracking now vs. what they need to track moving forward.
The only way demand gen marketers can do that is with a well-thought-out experimentation strategy built on a foundation of the 3 As (and an O):
I’ll include “offer” on this list, too, even though it doesn’t start with an A. Think of the offer as the hook, the “what’s in it for me?” incentive that gets people to click and convert. Some people use gift cards, ad credits, discounted pricing, and free consultations.
Jason Widup, Metadata’s former Head of Marketing, outlined his experiment framework in this article—check it out here.
So, how does data independence fit into the experimentation equation?
Marketers need access to data to inform their experiments, but more importantly, to take action based on their results.
Marketers can no longer wait for their Ops team or agency to supply answers. Time is money, and without instant access to data, they can’t make quick and informed decisions in the best interest of their campaigns.
The native ad channels aren’t much better. While marketers can quickly dive into them, the structure of these channels makes the experiments less impactful. This is because budgets sit at the campaign level, meaning marketers can’t isolate variables and allocate spend toward the highest-performing ads. Basically, they can access data, but the experiments are nothing to write home about.
Demand gen marketers ask themselves a lot of questions:
But without automation, there’s no time to answer them, build audiences, craft engaging creative, assemble experiments, and then launch everything across channels. Oh, and then marketers still have to optimize everything daily to ensure they’re pacing correctly and heading toward their goals. I’m stressed just writing that.
These manual, repetitive, albeit necessary tasks eat an unimaginable amount of bandwidth, which is why demand gen marketers must embrace automation. (Metadata certainly does.)
Now imagine an automation-enabled marketer with ownership and access to their data. They build audiences quickly, launch complex experiments at scale, and act instantly on optimization opportunities. That’s a fancy way of saying, they get stuff done.
This is how demand generation should be—and how it will be once marketers embrace automation and take ownership of their data.
Let’s recap how demand generation marketers currently access the data and insights they need to build, launch, and optimize paid social campaigns:
Again, both options have good and bad aspects, and at the end of the day, marketers should trek down whichever path is available to them.
But there’s one more option I have to mention—and it’s all about data independence: Metadata.
With Metadata, demand gen marketers eliminate manual tasks related to audience building, campaign launchings, and reporting. This gives them more time, control, and flexibility back in their day.
Marketers, you could literally build audiences and launch campaigns in one place. You could drill down into post-conversion metrics, like opportunities created, CPO, and revenue created, to truly understand where your efforts are best spent. With Metadata, campaigns are optimized automatically using data from marketing automation platforms, so say goodbye to guesswork.