Monte Carlo’s Framework for Testing Startup Ideas: How They Found Their $1B+ Opportunity

Monte Carlo’s Framework for Testing Startup Ideas: How They Found Their $1B+ Opportunity

Most startup origin stories get polished into neat narratives of instant clarity and conviction. The reality is usually messier. In a recent episode of Category Visionaries, Monte Carlo CEO Barr Moses revealed their systematic approach to testing multiple startup ideas in parallel – and how this methodical process led them to discover the massive opportunity in data observability.

The Parallel Testing Framework

After leaving Gainsight, Barr didn’t immediately start Monte Carlo. Instead, she developed a systematic approach to testing multiple ideas simultaneously. “I actually worked on a couple of different ideas and kind of tested out different ideas in parallel. And the idea was to see which idea has traction,” she explains.

The Cold Call Validation Method

Rather than building MVPs or running surveys, Barr used direct customer outreach. “I would literally cold call people and say like, hey, do you have this issue? Hey, is this a problem for you?” This direct approach quickly separated promising ideas from dead ends. “Some of the ideas that I worked on were terrible and nobody cared about it. They would just hang up on the phone.”

Identifying Signal Through Noise

Among various ideas tested, one problem consistently resonated: “The idea of, hey, the data is wrong, what can I do about this? Or why am I always the last person to hear about this? Why am I hearing from downstream consumers that the data is wrong?” The strength of reaction to this problem stood out from other concepts tested.

Three-Part Validation Framework

Barr developed a three-question framework to evaluate opportunities:

1. “Is there a real problem to solve here?”
2. “Do we think this is a problem that many teams have day and it’ll get worse?”
3. “Is there a way to describe the solution today?”

For data observability, the answers were: Yes, Yes, and No – indicating both a real problem and room for innovation.

Looking Beyond Current Pain

A crucial element of Monte Carlo’s validation process was assessing future market potential. “It became clear that it’s going to be a problem that’s going to be worse over time because people are going to be using data, more data is going to become more critical to companies operations, more critical to companies products.”

The Category Creation Signal

The validation process revealed not just a problem, but a category creation opportunity. When the answer to “Is there a way to describe the solution today?” was “No,” it signaled potential for innovation. As Barr notes, “Following these sort of three observations, we did realize we need to create the category here.”

Validation Through Customer Language

Instead of forcing their own terminology, Monte Carlo let customer language guide category definition. “I think oftentimes it’s easy to market or create content with yourself, with your own company in mind… It’s very different to come from that angle versus what is the language that our customers are using? What words do they use to describe their own problem?”

Framework for B2B Founders

Monte Carlo’s approach offers a replicable framework for B2B founders:

1. Test multiple ideas in parallel rather than committing to one
2. Use direct customer outreach for rapid validation
3. Look for problems that will grow more acute over time
4. Assess both current pain and future market potential
5. Listen for how customers naturally describe their problems
6. Watch for gaps in existing solution descriptions

The conventional wisdom often pushes founders to commit to an idea quickly and start building. Monte Carlo’s experience suggests that systematic testing of multiple ideas, with a focus on future market potential, might be a more reliable path to identifying truly massive opportunities.

For founders seeking their next opportunity, this framework offers a structured approach to idea validation that goes beyond traditional MVP testing – one that helped Monte Carlo identify and capture a billion-dollar category creation opportunity in data observability.