Table of contents
Marketers often get into debates about “vanity metrics.” Pageviews, impressions, likes, follows — all often dismissed as fluff. And LinkedIn certainly used to get lumped into that category.
But what if some metrics like social media engagement actually do correlate with pipeline and revenue?
That’s exactly what Brendan Hufford, founder of Growth Sprints, set out to prove. In this playbook, we’ll unpack his approach to testing LinkedIn as a revenue channel — and how the same thinking applies across SEO, email, and other GTM motions.
In this playbook, we’ll walk through:
- Why marketers lose credibility by focusing on the wrong metrics
- How Brendan proved LinkedIn impressions correlated with revenue
- Why correlation is more practical than causation in GTM measurement
- What the “great traffic panic” teaches us about real vs. vanity growth
- How these lessons apply across every channel — SEO, email, events, gifting, and more
- The foundational practice most companies skip
Watch the full episode
The reputation problem with vanity metrics
Vanity metrics got their name because, for years, they were the easy numbers to grab. They made marketing teams “look good” (on the surface, anyway). It’s simpler to report on huge traffic numbers or impressions than to try to track how those activities impacted pipeline or revenue.
Executives grew cynical (with good reason). If you’ve been on the receiving end of the question, “But did it drive revenue?” you know what we’re talking about.
As Brendan explains, this created a massive marketing credibility problem.
“I think a lot of marketers maybe don’t get the seat at the table that they deserve because they’re so worried about marketing metrics and they’re not thinking about business metrics.”
The problem with this over-course correction is that, especially in this new era of zero-click content, AI overviews, and dark social, those used-to-be “vanity metrics” have become increasingly important.
This is the crux of the problem: impressions and traffic aren’t bad – they’re just incomplete. Brendan says the solution isn’t to ignore them: it’s to connect them to what the business cares about most.
From impressions to revenue: Brendan’s LinkedIn experiment
Coming from an SEO background, Brendan had long been frustrated with marketers “living and dying” by vanity metrics like rankings and traffic. When he launched his own Growth Sprints business, he wanted to test whether those so-called vanity signals had deeper value.
He pulled data from a mix of sources:
- LinkedIn: impressions, engagements, post performance
- Google Analytics + Google Search Console: website traffic and search visibility
- ConvertKit: newsletter subscribers and open rates
- Nutshell CRM: leads, pipeline, closed-won revenue
He lined up the data side by side and looked for patterns. The result?
His LinkedIn impressions and revenue graphs correlated almost perfectly, when accounting for appropriate lag time.
“The impressions came first, and the revenue came after, but they trended exactly together. There’s a strong correlation.”
The data told a clear story: LinkedIn impressions weren’t just vanity – they were a leading indicator of and contributor to pipeline and revenue.
Why correlation beats causation
Traditional attribution promised a world where every touchpoint could be tracked and assigned credit. In reality, modern SaaS buying cycles don’t work that way.
- Sales cycles can be 12–18 months.
- Marketing cycles are often even longer.
- Buyer journeys span dozens of touchpoints: social, ads, content, events, emails, peer referrals.
Trying to prove causation – that one action directly caused a deal to close – is pretty much a fool’s errand.
Instead, Brendan looked for correlation. When impressions rose, revenue soon followed. When subscribers opened more emails, they converted at higher rates. When brand campaigns shortened the sales cycle, the entire business improved.
A correlation mindset gives GTM leaders actionable signals without demanding a level of certainty that doesn’t exist in complex B2B sales.
Running experiments with “upside risk”
Once Brendan saw the LinkedIn correlation, he wanted to test whether more activity would strengthen it.
But instead of pulling back (which could decimate the pipeline), he chose what he calls “upside risk.”
“Downside risk would be like, maybe I don’t post on LinkedIn for a month and see what happens. But if I’m correct, my pipeline goes to zero. That’s very risky. Upside risk is instead of going from one to zero, go from one to two. What if I posted twice a day on LinkedIn?”
It paid off. His impressions grew, the revenue trend held, and he learned that consistency and volume mattered more than he thought.
This same framework can be applied anywhere:
- Send more newsletters → Do subscribers convert faster?
- Host more webinars → Do demo requests rise?
- Increase CEO social activity → Does the sales cycle shorten?
It’s about doubling down where correlation already exists – without gambling the entire business.
Applying the lens across channels
As you can see, Brendan’s framework isn’t just about LinkedIn. It’s a way of thinking that applies across every GTM channel.
- SEO: Are you optimizing for the right keywords, or just the ones that bring vanity clicks?
- Email: Do subscribers who open 4+ newsletters convert more often? If so, should you increase send frequency?
- Brand campaigns: Do buyers close faster when brand awareness is strong?
- Events & gifting: Do accounts that attend webinars or receive gifts have higher close rates?
The goal is the same: find correlations between activity and business outcomes, then double down on the signals that matter.
The foundational work most marketers skip
As powerful as correlation analysis is, Brendan argues it’s not the most important piece.
The true foundation is what he calls Content IP: naming the customer problem.
“People are very quick to jump into attribution and correlation – but we kind of skipped the foundational piece, which is what I call content IP. And that’s naming the problems your customers have. This is a huge lever. Almost nobody is doing it.”
Think of terms like “quiet quitting” or “the great resignation.” People felt the pain, but once the problem was named, it snapped into place – and the brands who named it gained instant credibility.
You can generate impressions, traffic, and clicks all day long – but if you’re not naming the problem, your buyers are likely to scroll right past you.
The bigger picture: leading with metrics that matter
Brendan’s LinkedIn experiment offers SaaS leaders a roadmap for moving past the vanity vs. value debate:
- Don’t dismiss surface metrics – connect them to business outcomes.
- Look for correlation, not causation, to find actionable patterns.
- Run upside risk experiments to scale what’s working.
- Beware of false growth signals, like unqualified traffic.
- Ground everything in Content IP: name the problem before you measure the results.
For GTM leaders under pressure from boards and CFOs, this approach reframes the conversation. Instead of getting stuck defending “vanity metrics,” you can show how early signals connect to real outcomes and use data to guide smarter investment.
What started with LinkedIn impressions turned into a bigger philosophy: metrics are not vanity when they help you understand what drives revenue.
See the data in action
Based on Brendan’s story, here’s an example of how you could track the correlation between LinkedIn impressions and revenue, all in one clear view.
Check it out in Databox below!
