90% of Feedback Is Crap – How to Find the Next Big Startup Idea

Begin with a 21‑day sprint: validate a concept with live input and short demos to reveal scalable signals. Collect input from real users quickly, count patterns across flows, and clearly judge need by actions rather than words.

In practice, findings show what matters: real engagement over opinions. Such developments emerge when you communicate with a person, not a persona, during a sequence of short demos. A third‑party advisor, wendy from edelberg, becomes a practical proxy for customer reality; her advice is realized as concrete changes in flows, which also refine prioritization for investors and teams.

Convert signals into a crisp number of validated investments; if multiple demos fail to transfer across groups of users, pivot quickly. Maintain a health check via retention and activation metrics; if a concept does not grow across units, discard it before large commitments. This discipline keeps growth path unique and defensible.

During times when multiple teams collaborate, document unique value propositions that emerge from user journeys. Use structured demos to compare against trusted benchmarks; communicate progress to stakeholders via concise updates. This approach reduces noise, clarifies priorities, and helps a person grow into a founder with robust readiness.

Practical framework to extract valuable ideas from noisy feedback inside a trusted network

Start by mapping signals from trusted peers into a single interpretation layer, then run a 30‑day test to separate something actionable from crap.

Interpretation must blend comments, observation, and drivers to locate market opportunities. Alone, impressions drift; a determined team connects large trends across platforms to craft a strong, correct plan that scales easily.

Collect comments inside a closed circle; hear signals, note unusual trends, convert to observation.

Craft a theory linking drivers, platforms, and market segments to economic outcomes.

Run lightweight experiments to tweak assumptions, measure income potential, and weed out risky bets.

Document opportunities with clear hypotheses; rank by potential income, competition, and risk.

Iterate quickly; when findings align with trends and opportunities, invest; otherwise pause.

Only observations backed by data deserve focus; if some opportunities werent amazing, drop them and move on.

Define the Circle: who to include and why trust matters

Start by selecting compact, trusted circle: fellow operators, economists, and domain experts who can challenge assumptions and stay grounded, realized that trust matters to move from insight to traction.

Within circle, include ones who combine practical task discipline with strategic vision: fellow operators who ship; economists who translate signals into numbers; visionary minds who see wide horizons. They prefer candor and measurable signals over vibes, which helps them behave honestly.

Trust grows through disciplined interactions: weekly comment threads, slow-paced experiments, and deck updates that reveal failures as learning. A focus on alignment yields a huge payoff in speed and quality.

Accessibility matters; starting with multilingual notes in панджаби and English to broaden participation within a year.

Measure circle health with concrete metrics: comment quality, speed of decisions, and increases in shared understanding; every hundred cycles, track progress and celebrate happening, a sign of real alignment.

Finally, keep a living roster visible to all: include ones who motivate, encourage, and protect candor; this practice motivates discipline and keeps everyone honest.

Filter the Noise: concrete criteria to discard low-value feedback

Begin with triage: assign input to three buckets: signal, doubtful, discard. Retain only signal items that map to user needs and to early-stage productdesign foundations.

Criterion 1: direct link to needs. Rate on a 0–3 scale by clarity of problem, anticipated impact, and feasibility within minutes. Avoid praise or vague vibes; seek concrete hypotheses ready for testing.

Criterion 2: measurability and falsifiability. Define a concrete test with a single metric, baseline, and expected bound. If results cannot be quantified in minutes or yield no numeric signal, discard.

Criterion 3: alignment with growth plan within economy constraints. Map input to target segments, value proposition, and potential to grow team.

Discard sources: vanity metrics, untestable claims, or inputs with opaque predictors. Prefer signals tied to real usage, engagement, or clear business impact.

Data sources: display metrics from advertising, google data, and experiments with landing pages. Track rate of conversions, time to activation, and bounce rate.

Process cadence: set a minutes-based review cycle; capture highlights; log each input with a bound on follow-up.

World context: times ahead drive changed expectations; monitor coming shifts in economy; test predictions against reality.

Multilingual filter: include корейский messages to test translation friction; ensure team can interpret signals and act on needs.

Delivery: convert passing input into a lightweight test plan with steps, owner, and a display of results to iterate.

Extract Signals: convert impressions into Jobs-to-Be-Done statements

Recommendation: Build 10 JTBD statements in 48 hours by turning impressions into job-to-be-done statements using a tight template; this yields faster alignment and fewer misreads.

1. Signal sources: weekly interviews, stories, papers, google trends, networks, and Turner group notes from early-stage experiments; label every signal with pain, context, and outcome.
2. JTBD template: When [state], user wants [outcome], so that [benefit]. Use variables such as time, cost, and effort to keep scope precise.
3. Convert signals into job statements: for each signal, craft 1-2 lines focusing on outcome plus success metric (numbers). Example: If user feels [pain] while [context], they want [outcome], reducing [cost or time] by [percentage or hours].
4. Quality gate: ensure each statement is testable within 4-6 weeks; attach a measurement variable as deck card (e.g., expected monthly spent, number of users impacted, sprint time saved).
5. Prioritization: score by impact on user state and competition pressure; balance quick wins against long-term value; consider trends and networks influence.
6. Validation plan: pick top 5 statements; design 2-week experiments; track metrics such as time-to-validate, spend, revisions in months; capture learnings in a tips deck.

Tips to refine signals:

• Track signals weekly; convert into JTBD items; attach a concrete success metric.
• Compare trends across networks and group segments; wherever competition tightens, JTBD statements should reveal unique value.
• Capture pain-discovery in user state; measure impact in months; store learnings in a deck.
• Engage experts and Turner for peer reviews; align with strategy across team; share messages via weekly stories to reinforce learning.
• Discovery loop: use quick experiments to discover new signals and update states.

Prototype Fast: lightweight experiments to test ideas with real users

Begin with one clear assumption, run a 48-hour lightweight probe with real users, and collect observable signals that map to validation goals.

Use lightweight artifacts: landing pages, hand-made demos, quick surveys, and click-through prototypes; on hand tools keep costs lower while speeding learning; these options prove useful. Demos made quickly.

Focus on metrics that matter: activation rate, drop-off points, time-to-value, and user-confirmed signals for basic demand. Looked for patterns across responses to identify where value lands. based on these signals, pivot options quickly.

reduces cash burn through tight loops; hundreds of interactions across multiple days yield clearer direction. Economic argument stands: fast loops deliver meaningful new directions cheaper than lengthy product cycles.

Process evolves throughout learning loops; validation builds trusted signals with users, not only engineers. If conditions changed, adaptability matters for future cycles.

Include multilingual prompts: français, вьетнамский, internal surveys, aimed at college students, interns, and early-career developers. career growth remains a priority for participants.

40-year wisdom from Turners, jointly built theory, and a practical roadmap; annual re-tests sharpen direction.

Building blocks stay lightweight; building confidence becomes objective, not final product. full learning comes from each probe.

Turnaround times for iterations: 24–72 hours, depending on access to users; keep scope tight and avoid feature creep.

Documentation: capture what changed, why, and what to test next; this record fuels internal career growth and college programs.

Score and Select: a simple rubric to prioritize the next big idea

Recommendation: run a 15-point rubric across five factors; assign 0–3 on each; apply weights; total decides which concept to pursue.

Weights listed: 1) market demand; 2) testing ease; 3) monetization path; 4) differentiating feature; 5) data readiness.

Scoring rules: each candidate earns 0–3 per factor. Multiply by weight (1–5). Sum results to yield total. If data is missing, use average of available factors to avoid bias; meant to guide quick pivots.

Process cadence: weekly review, couple of designers meeting, and a post with one-page summary.

Screen for gaps, test key hypotheses, and assess power of signals; remember, youre double-checking for negative indicators; poor signals makes decisions risky; a strong feature takes form quickly.

Follow-up actions: after scoring, collect observation from weekly meeting with rachel and designers; post concise one-page summary; include interest signals, data, intelligence, and future steps; lead decision with whole-team buy-in; therefore, move only high-score concepts.

Show stakeholders what high-scoring options deliver.