Problem-solution Matcher
Match customer problems with innovative solutions to create products people actually want.
Use This When
Planning, analysis, client strategy sessions, decision support.
Inputs Needed
Business model, goal, constraints, market, competitors, budget, timeline, internal capabilities.
Expected Output
Executive summary, diagnosis, options, risks, recommended path, implementation plan, KPIs.
The Workflow Prompt
You are a business strategist and operator. Objective: Problem-solution Matcher Context: Match customer problems with innovative solutions to create products people actually want. Original task: You are a world-class problem-solution designer who has matched thousands of problems with solutions, creating successful businesses addressing real customer needs. Your expertise spans problem identification, solution design, and customer research validating true demand.Match problems with innovative solutions in [YOUR_DOMAIN]. Deliver:1. **Problem Identification**: Interview 20+ potential customers in your domain; identify top 20 problems and pain points2. **Problem Quantification**: Quantify problem severity (frequency, impact, cost), customer segment size, and willingness to pay3. **Current Solutions Audit**: Identify current solutions addressing problems; analyze limitations and gaps4. **Problem Clustering**: Group related problems into categories; identify themes and patterns5. **High-Value Problem Identification**: Rank problems by customer impact, market size, and solution difficulty6. **Solution Ideation**: For top 10 problems, brainstorm 5+ potential solutions addressing each problem7. **Solution Feasibility Analysis**: Assess feasibility, cost, and time-to-build for each solution8. **Customer Validation**: For top solution-problem matches, validate with customer interviews9. **Competitive Solution Analysis**: Compare proposed solutions to existing competitive offerings10. **Solution Differentiation**: For solutions with competition, identify differentiation factors11. **Business Model Fit**: Assess which solution-problem matches yield sustainable business models12. **Willingness-to-Pay**: Research customer pricing for solutions; assess revenue potential13. **Go-to-Market Strategy**: For top matches, design customer acquisition strategy14. **Prioritized Roadmap**: Rank solution-problem matches by opportunity size and feasibility Inputs I may provide: Business model, goal, constraints, market, competitors, budget, timeline, internal capabilities. Operating instructions: - First, restate the objective in one clear sentence. - If critical information is missing, ask up to 5 focused questions. If there is enough information to proceed, make practical assumptions and label them. - Use a Detailed response style. - Be specific to the business, audience, channel, and constraints provided. - Avoid generic AI advice. Give concrete recommendations, examples, templates, copy, or steps I can use. - When current facts, competitors, laws, prices, policies, or market claims matter, use current research and cite sources. - Do not expose hidden chain-of-thought. Provide a concise rationale or decision summary instead. - End with a short QA checklist that helps me verify the output. Required output: Executive summary, diagnosis, options, risks, recommended path, implementation plan, KPIs. Caution: Use live web research or source documents before finalizing claims.
QA Follow-Up Checklist
After the AI returns its output, verify against:
- Output is specific to the provided business/context.
- Assumptions are clearly labeled.
- No unsupported claims without source checks.
- Next actions are clear and usable.
Follow-Up Prompt
Now turn the result for 'Problem-solution Matcher' into a client-ready version: tighten wording, remove fluff, add missing assumptions, and provide the next 3 actions.
Avoid / Cautions
Use live web research or source documents before finalizing claims.
How Different Verticals Use This Workflow
Restaurant & Hospitality
A restaurant POS startup founder with 30 customer interviews of independent restaurant operators feeds in transcripts, the segment (independent full-service under $2M revenue), and competitive pricing for Toast/Square. Output identifies the top three problems (loyalty programs nobody uses, inventory shrinkage tracking, online ordering margin) and recommends a focused MVP on inventory — the smallest viable opportunity with the highest WTP.
Retail & E-commerce
An indie e-com brand researching whether to launch a new product line for their existing customer base feeds in survey data, repeat purchase patterns, and the brand's actual capability (small-batch fulfillment, no retail). Output identifies five potential extensions, rejects three for feasibility, and ranks two — recommending the lower-revenue but faster-to-market option as the right first move.
Professional Services & B2B
A consultancy considering whether to productize a specific service feeds in their last 20 client engagement notes, the average deal size, and the segment they want to target. Output identifies which of the recurring client problems has actual willingness to pay at scale vs which only sells when bundled into custom strategy — saving them from launching a productized SKU nobody buys.
Beauty & Personal Care
A skincare founder researching her next product (the brand's 4th SKU) feeds in customer support tickets, return reasons, and the three customer interviews she ran last month. Output cross-references stated problems with actual purchase behavior and recommends a SKU that solves a real customer friction (nighttime barrier repair) over the one she was emotionally attached to (a body lotion extension).
Local & Trade Services
A handyman service operator considering whether to launch a productized 'home maintenance subscription' feeds in his last 200 service tickets, repeat customer patterns, and pricing for competing subscriptions in his market. Output identifies the actual repeating-pain pattern (gutters + filter changes + outdoor faucet winterization) and recommends a $39/mo bundle with a 90-day pilot plan.
Frequently Asked
What inputs make this produce a real opportunity vs. a generic ideation dump?
Real customer interview notes (or transcripts), the specific market segment you're willing to commit to for 12 months, and your honest answer on willingness to pay — what have customers actually paid for similar solutions, not what they say they would pay. Without the third input, you'll produce 20 great solutions to problems nobody will fund.
Should I run this on Claude Opus or ChatGPT Thinking?
Claude Opus 4.7 for the full 14-section deliverable — it can hold dozens of problem-solution pairs and rank them coherently. ChatGPT GPT-5.5 Thinking when you want to pressure-test one specific match. Use Perplexity to enrich the competitive solution audit; the model's training data on current SaaS pricing and feature sets is always months stale.
What's the most common failure mode for problem-solution matching?
Falling in love with a problem that's real but tiny. The prompt forces you to quantify segment size and willingness to pay, but only if you feed honest data — most teams inflate both because they want the answer to be yes. The fix: define the smallest viable market and the minimum revenue threshold before you start, and use them as hard cuts.
What does a strong output look like in practice?
Three to five problem-solution pairs ranked by opportunity size and feasibility, each with a quantified market size, a willingness-to-pay number tied to comparable solutions, a feasibility-to-build estimate, and a 90-day validation plan. If the output is a list of 50 unranked ideas, the prompt didn't work — make it rank and cut.