When we were building RemoteTeam, my biggest mistake with revenue intelligence (and how nearly killed us before we figured it out.
When I first tried scaling our sales team, I failed miserably. It wasn't until we implemented revenue intelligence that everything clicked. Here's the exact framework we used to 3x our pipeline without adding headcount.
The Framework That Actually Works
I'm going to share the exact framework I use when evaluating my biggest mistake with revenue intelligence (and how. It's not complicated, but it requires discipline.
Step 1: the best solutions are often the simplest ones This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: the market doesn't care about your roadmap Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly Nothing works perfectly the first time. The companies in my portfolio that nail my biggest mistake with revenue intelligence (and how are the ones that treat it as an ongoing process, not a one-time project.
Why Most Approaches Fail
Let me be direct: about 70% of the approaches I see to my biggest mistake with revenue intelligence (and how are fundamentally flawed. Not slightly off. Fundamentally flawed.
The root cause is usually one of three things:
- Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
- Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
- Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Numbers Don't Lie
I've tracked the performance of companies in my portfolio that take my biggest mistake with revenue intelligence (and how seriously versus those that don't. The difference is stark.
Companies that invest early in my biggest mistake with revenue intelligence (and how see, on average, 2-3x better outcomes within 18 months. That's not a small edge. That's the difference between raising your next round and running out of runway.
One of my portfolio companies went from struggling to profitable in under a year after they finally got serious about this. The founder told me later that they wished they'd started sooner.
This connects to broader themes around outbound AI, revenue intelligence, AI sales tools that I've been thinking about a lot lately.
Final Thoughts
After two exits, 200+ investments, and more mistakes than I can count, here's what I know for sure about my biggest mistake with revenue intelligence (and how: there are no shortcuts, but there are smarter paths.
The smartest founders I work with treat my biggest mistake with revenue intelligence (and how as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with my biggest mistake with revenue intelligence (and how, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
Frequently Asked Questions
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.