A System To Find Your Own SaaS Ideas

By Patrick on 8/7/2024

Arthur C. Clarke noted, “The only way of discovering the limits of the possible is to venture a little way past them into the impossible.”

The difference between a good idea and a transformative one often lies in the approach. I’m sharing my system for generating high-value SaaS ideas, grounded in data and cross-disciplinary thinking.

1. Idea Legos

Great ideas rarely emerge fully formed. Instead, they’re often combinations of existing concepts – what we might call “idea legos.”

To build your collection: Read voraciously across disciplines. The intersection of biology and computer science spawned genetic algorithms; what other fields are ripe for cross-pollination?

Study historical analogs. How did the postal service solve logistics problems that might apply to data routing?

Engage with diverse thinkers. A conversation with an urban planner might spark ideas for network optimization.

2. High-Friction Workflows

SaaS opportunities hide in plain sight as high-friction processes. To uncover them:

Analyze time-use studies in target industries. Where are knowledge workers spending disproportionate time on low-value tasks?

Conduct ethnographic research. Observe users in their natural environment – you’ll often spot inefficiencies they’ve normalized.

Look for “duct tape” solutions. Temporary fixes that have become permanent are gold mines for SaaS ideas.

3. Quantify the Opportunity

Intuition is a starting point, but data should drive decisions. For each potential idea:

Calculate the total addressable market (TAM). Be conservative, but don’t ignore adjacent markets that could be disrupted.

Estimate the customer lifetime value (CLV) to customer acquisition cost (CAC) ratio. Aim for 3:1 or higher for long-term sustainability.

Model potential network effects. How does the value proposition change as you scale from 100 to 1,000,000 users?

4. Technological Inflection Points

Timing is crucial. Identify technologies on the cusp of mainstream adoption:

Track compute cost trends. When did cloud GPU instances become cheap enough to make large-scale ML applications viable for startups?

Monitor open-source projects. Rising stars in the GitHub ecosystem often signal emerging platforms ripe for commercial tools.

Study patent filings. What technologies are large tech companies betting on 3-5 years out?

5. Design for Extensibility

The most valuable SaaS products become platforms. From day one, consider:

API-first architecture. How can you make every feature of your product programmable?

Embedded ecosystems. Can you create a marketplace within your product, à la Salesforce’s AppExchange?

Data network effects. How can user interactions generate proprietary datasets that improve the product for everyone?

These are the phenomena that create large moats.

6. Rapid Prototyping and Validated Learning

Ideas are hypotheses. Test them quickly:

Build minimum viable products (MVPs) in weeks, not months.

Use techniques from the Lean Startup methodology to define and test your riskiest assumptions.

Embrace quantitative AND qualitative feedback. Usage metrics tell you what’s happening; user interviews tell you why.

7. Optimize for Learning Velocity

In the early stages, the speed of learning outweighs perfection:

Implement continuous deployment. How quickly can you go from idea to production?

Build feedback loops into your product. Can users effortlessly report issues or suggest features?

Create a culture of experimentation. What’s your process for running and analyzing A/B tests?

The system I laid out here is certainly not easy, but it’s what it takes.

Here’s a tool to help you build faster. If you decide to give slimsaas.com a shot, please reach out! Would love to connect and hear what you are building.