Strategyzer UVP Coach
Value Proposition Coach GPT
Unclear customer value → structures thinking using Strategyzer frameworks → users produce testable, customer-centered value propositions.
01 — The Problem
Teams struggle to clearly define why customers would care about a product or service. Ideas are often framed around features, opinions, or internal assumptions rather than customer jobs, pains, and gains, leading to misalignment and wasted effort. Without a structured approach, conversations become vague, and value propositions remain untested and unclear.
02 — What the AI Does
I guide users through value proposition design using Strategyzer’s Value Proposition Canvas. I structure thinking, prompt for customer jobs, pains, and gains, and help draft value maps (products/services, pain relievers, gain creators). I evaluate and refine value propositions against “fit” criteria, and support hypothesis formulation and testing using concepts like test cards and experiments. I am built on a GPT-5.3 language model with a constrained system prompt that enforces: * Use of Strategyzer methodology as the core framework * Coaching-style responses (first-person, directive, structured) * Reference to embedded knowledge files containing the Value Proposition Design book and workshop materials I do not access external data or live systems; my outputs are generated through structured reasoning over this embedded methodology.
03 — Design Decisions
Narrow scope to Value Proposition Design (Strategyzer)
Ensures depth and consistency instead of generic business advice
Prevents drifting into unrelated strategy, marketing, or execution domains
Embed Strategyzer source material as a knowledge base
Ground outputs in a proven framework rather than improvisation
Forces alignment with concepts like jobs, pains, gains, and fit
Enforce coaching persona (first-person, directive)
[Creator: add rationale]
Outputs are framed as guided thinking rather than passive explanation
Prioritize structure over freeform brainstorming
Strategyzer emphasizes making value propositions “visible and tangible” through structured tools
Responses push users into canvases, lists, and prioritization rather than abstract discussion
Focus on customer-centric framing (jobs, pains, gains)
The methodology explicitly shifts from “what customers want” to “what they are trying to get done”
Rejects feature-first or solution-first thinking
Emphasize testing and hypothesis validation
Strategyzer highlights reducing risk through experiments and evidence
Encourages users to treat ideas as hypotheses, not facts
Avoid claims about outcomes, metrics, or business impact
The system has no access to real-world usage data
Keeps outputs grounded and non-speculative
No tool use beyond embedded knowledge and reasoning
[Creator: add rationale]
Cannot fetch real customer data, run experiments, or validate ideas externally
05 — Key Insight
Well-designed AI isn’t about generating answers—it’s about enforcing a thinking framework that users would otherwise skip.