0-to-1 thinking: real PM pain points, ruthless feature prioritization, and a pricing model built on evidence, not guesswork.
In a crowded AI-tools market, the risk wasn't "can we build this," it was "will anyone pay for this, and why us." Without rigorous discovery, it would have been easy to build a generic AI wrapper indistinguishable from a dozen competitors, and to price it based on guesswork instead of evidence.
Getting the pricing model wrong would have been just as fatal as getting the feature set wrong: undervaluing the product would starve it of revenue to grow, while overpricing it would kill early adoption before word-of-mouth could build.
I analyzed insights from 50+ product managers and benchmarked 15 competitors to map real PM workflows, pain points, and where a purpose-built assistant could actually win versus generic tools. I used that research to define the 10 highest-impact features for the initial product launch, rather than trying to build everything at once.
I then developed a three-tiered subscription pricing model based on consumer willingness-to-pay, designed to optimize both revenue generation and product-market fit from day one.
Three monetization paths were viable. The decision came down to matching price structure to how and when value was actually realized.
| Model | Alignment to Value | Adoption Friction | Revenue Predictability | Verdict |
|---|---|---|---|---|
| Flat Single-Tier Fee | Low, doesn't scale with usage | Low | High | Rejected, underprices power users |
| Pure Usage-Based | High | Higher, unpredictable bills | Low | Rejected, hurts early trust |
| Three-Tiered Subscription | High, tiers map to real segments | Low, predictable entry price | High | Selected |
We landed on a three-tiered subscription model based on consumer willingness-to-pay, giving individual PMs a predictable low-friction entry point while capturing more value from power users and teams.
Analyzed insights from 50+ product managers to map real workflows, tools, and pain points.
Benchmarked 15 competitors to identify differentiators and where a purpose-built assistant could win.
Defined the 10 highest-impact features for the initial launch, rather than a broad feature wishlist.
Developed a three-tiered subscription pricing model grounded in willingness-to-pay data.
Shaped go-to-market thinking around the tier and segment with the clearest, fastest path to value.
Pricing is a product decision, not a finance afterthought. It shapes who adopts and how they perceive value.
Specificity beats breadth in 0-to-1. A narrow, deeply-understood segment beats a vague "PMs in general" audience.
Willingness-to-pay signals from real conversations are worth more than any competitor benchmarking spreadsheet alone.
The pricing debate wasn't unanimous - there was a real pull toward pure usage-based pricing for its upside on power users. I modeled expected revenue and adoption friction for all three structures side-by-side before we converged on tiers, rather than defaulting to the most common SaaS pattern.
