Mix the feature cocktail
Delighters first, then performance, then must-haves. If a feature doesn't trace back to your interviews, it's a guess.
Your job: turn 20+ interviews into the evidence-backed feature mix that causes a switch.
Assemble my job requirements Read `business_model://current` and my recent interviews. Assemble job requirements — hiring criteria, firing criteria, tradeoffs — per consideration set, ranked by frequency. Map them onto the Kano model and propose my feature cocktail: one delighter, the performance dimensions that matter, the non-negotiable must-haves. Flag any feature I've mentioned that traces to none of them. claude mcp add --transport http leanspark https://leanspark.ai/mcp Connect first →
What you get back
Sweeps your Customer Forces Stories, extracts hiring criteria, firing criteria, and tradeoffs for every alternative in each consideration set, ranks them by frequency, then maps them onto the Kano model — must-haves you can't fumble, performance dimensions worth 3x, and the tolerated tradeoff that's your best delighter candidate.
Ranked job-requirement tables and a proposed feature cocktail, each line traced to the interviews that back it. You choose the delighter to lead with.
Before drafting the five Ps, you need to know which features actually cause a switch. In the 1980s, quality researcher Noriaki Kano plotted customer satisfaction against investment in a feature and found something most roadmaps still ignore: features don’t all behave the same way, and investing more in the wrong kind buys you nothing.
| Feature type | How satisfaction responds | Car example |
|---|---|---|
| Must-have | Absence makes customers unhappy; presence is merely expected. | Brakes |
| Performance | Linear: more is better, less is worse. Customers compare on these. | Horsepower |
| Delighter | Unexpected. Presence creates off-scale happiness; absence costs nothing. | Park-assist |
| Indifferent | No effect either way. | The cabin air filter’s color |
| Reverse | Negative: more is worse. | Emissions |
Delighters decay. A feature’s ability to delight has a finite lifetime. Multi-touch was a delighter when the iPhone revealed it; today it’s a must-have on any phone. This cuts both ways: copying the incumbent’s old wow-features buys you nothing, and your own delighters won’t stay delighters forever. One more reason speed matters.
A startup is constrained on speed, spend, and scope, so the cocktail runs opposite to most founders’ instincts: delighters first, then performance, then must-haves. Must-haves alone lose to the incumbent, because their presence is expected and the incumbent has them plus all the inertia of staying put. Performance features cause a switch only at 3x to 10x better, an expensive head-on race against a better-resourced player. Delighters are the startup’s edge: they don’t exist in the incumbent’s offering, so they’re unique by definition, and unique plus off-scale happiness is a textbook unique value proposition.
How do you pick the right ones? Many founders guess. You don’t have to, because your interviews already produced the evidence: the job requirements your prospects revealed every time they hired or fired a solution.
- Firing criteria (why alternatives lost) mark the must-haves your solution cannot fumble.
- Hiring criteria (why the winner won) mark the performance dimensions your segment actively compares. Compete here only where you can be dramatically better.
- Tradeoffs (shortfalls prospects accepted anyway) mark the whitespace. A solution that removes a tradeoff everyone else forces is unexpected by definition: a delighter candidate, grounded in evidence instead of imagination.
If a feature on your list doesn’t trace back to a hiring criterion, a firing criterion, or a tradeoff, it’s a guess. Cut it or park it.
New to this? Each card gives you two ways to run it. Type the In LEANSpark line into the chat, or use the Via MCP prompt inside your own AI assistant. LEANSpark does the assembly and the scoring; the design judgment stays yours.