AI won’t take your job. But someone who knows AI might.
That’s been the dominant narrative for the past two years. So instead of adding to the speculation, we pulled real data. We analyzed 1,000 US-based marketing job listings on LinkedIn to see what companies are actually hiring for, what they’re paying, and where AI fits in.
If there’s one thing recent SEO and GEO studies have confirmed, it’s that YouTube is now one of the top domains referenced by LLMs.
People would rather watch answers than read them, and according to Datos, YouTube searches aren’t plateauing. They’re growing significantly, steadily pulling search intent away from text-first platforms.
So why are so many marketing professionals avoiding this opportunity for YouTube growth?
A lot of companies say they believe in experimentation. They run A/B tests, analyze data, and make incremental tweaks. But the uncomfortable truth is that most of them aren’t actually innovating.
Marketers love to talk about growth, but the terms used often blur together. Experimentation, optimization, A/B testing—they sound like variations of the same thing.
They’re not.
Sure, each practice promises pipeline improvement and leans on similar tools and metrics. But, when you treat optimization as experimentation, you risk playing too small.
This isn’t just semantic nitpicking. Using the wrong approach at the wrong time leads to invalid data, wasted effort, and missed growth opportunities.
Pricing is often overlooked as a growth lever. When building growth strategies, teams typically focus on acquisition, retention, and engagement – rarely considering how pricing strategy can drive significant business growth.
In this comprehensive guide, we’ll explore how pricing fits into your growth model, uncover the many opportunities beyond simple price increases or decreases, and show you how to effectively evaluate pricing experiments as part of a complete growth strategy.
Traditional marketing fails because it’s built on assumptions rather than evidence.
A staggering 95% of product launches fail within their first year. The problem isn’t bad products or weak teams; it’s guessing instead of testing.
B2B companies that adopt experimentation-led go-to-market (GTM) strategies gain a competitive edge by systematically testing hypotheses, measuring outcomes, and scaling what works. This data-driven approach removes subjective decision-making from marketing and focuses entirely on results.
Traditional B2B marketing approaches often fail because they’re too siloed. Companies run ads without content support, create content without distribution strategy, or attempt outbound without proper targeting. The result? Wasted budget and missed opportunities.
All-bound marketing solves this problem by integrating multiple marketing channels into a cohesive system that generates qualified leads more efficiently. This approach combines the best of inbound, outbound, and content marketing to create a comprehensive growth engine.
Your SEO strategy is probably failing. Not because you’re not working hard enough, but because you’re working on the wrong things.
Most companies spend months creating content, optimizing pages, and building links without knowing if any of it actually works. They implement random tactics based on “best practices” from 2018 and hope Google rewards them eventually. This is madness.
Your landing page is where curiosity turns into intent—or gets buried in a tab you’ll never see again. Buyers lose interest in seconds. And once that happens, it’s pretty hard to get them back.
You’ve seen the numbers. A prospect clicks your $50 LinkedIn ad, lands on your page, but nothing happens, leaving you with a smaller budget and zero insight into why.
Experimentation is changing. Fast.
What was once a human-led process—running A/B tests, tweaking User Interface (UI) elements, and optimizing conversion paths—will increasingly be handed over to AI.
With AI automating tests and variations, humans now have the opportunity to focus on big bets—experiments that don’t just tweak conversions but fundamentally impact business strategy and growth. This shift marks a new era in AI Conversion Rate Optimization (CRO), where automation enables teams to think strategically rather than tactically.
However, it does raise some critical questions: What happens to experimentation teams when AI runs more tests? How does AI-driven testing change the economics of experimentation?