Customer Discovery & Growth Experiment Kit

Customer Discovery & Growth Experiment Kit

Purpose: Help small and growing businesses turn guesses into validated learning — fast, cheap, and repeatable. Use this kit to discover real customer needs, test pricing and offers as experiments, and measure whether an idea is worth scaling.

Who this is for

Founders, product managers, marketers, sales leads, and small-business owners who need to find repeatable, profitable ways to grow without wasting cash on unvalidated bets.

What you'll get (expanded)

• Interview scripts and note templates (with prompts for problem discovery, decision drivers, and pricing sensitivity)
• Hypothesis-led discovery checklist — convert assumptions into testable hypotheses
• Three simple pricing experiment designs you can run with minimal tooling
• Early funnel measurement dashboard (one-page) — key metrics and how to capture them
• Suggested sample experiments and an experiment logging template
• Common mistakes, quick tips, and follow-up actions to convert learning into decisions

How to use this kit

1. Pick one core business question (e.g., 'Will customers pay $X for Y?').
2. Write a short hypothesis: format — When [customer type] experiences [problem], they will [desired outcome] and be willing to pay [price] or trade [resource].
3. Run 5–15 qualitative interviews using the script. Capture notes in the template.
4. Design a small experiment (pricing test, presale, landing page) from the experiment designs below.
5. Track early funnel metrics on the one‑page dashboard for the duration of the test (usually 1–4 weeks).
6. Decide: Pivot, persevere, or kill based on pre-agreed success criteria.

Interview script (compact, usable in 20–30 minutes)

Open with permission and context: 'Thanks for taking time. I'm exploring how people solve [problem]. There are no right answers — I'm trying to learn from you. Is it okay if I ask a few questions? This is confidential.'

1. Background: Tell me about what you do and how you currently handle [problem].
2. Recent story: Walk me through the last time you faced this problem. What happened? Who was involved? How did you decide what to do?
3. Pain and cost: How often does this happen? What are the consequences (time, money, stress)?
4. Existing solutions: What have you tried? What worked? What didn’t? What did you wish had been different?
5. Decision drivers: What matters most when choosing a solution (price, speed, trust, features)? Rank or describe.
6. Pricing probe: If a product/service solved this in [desired way], what would you expect to pay? Would you pay monthly, per-use, or upfront? (Offer ranges rather than single numbers.)
7. Commitment check: If we launched this tomorrow, would you try it? Why or why not? What would make you sign up now?
8. Close: Anything else I should know? Can I follow up with a prototype or a short survey?

Note on pricing questions: Ask range-based or comparative questions (e.g., 'would you prefer $X monthly or $Y per-use?') rather than open-ended 'how much would you pay?' to reduce anchoring bias.

Hypothesis-led discovery checklist

1. State the hypothesis clearly (who, problem, outcome, price).
2. List the critical assumptions that must be true for the hypothesis to hold.
3. For each assumption, pick a lightweight test (interview, landing page, smoke test, pre-order).
4. Decide success criteria before running the test (qualitative patterns + quantitative threshold).
5. Assign owners, timeline (1–4 weeks), and required tools.
6. Run tests, collect notes, and log metrics in the experiment template.
7. Review results, document decisions, and update the next hypothesis or experiment.

Simple pricing experiments (three patterns)

1. Anchor & Compare (A/B landing pages)

   Create two landing pages with identical copy and a different price anchor (e.g., Premium $99 vs Premium $149 with a discounted $99 option). Measure click-to-signup and interest signups. Low cost — can use simple page builders.

2. Presale / Minimum Viable Offer

   Offer a pre-order or pilot spot at a fixed price. Require payment or a deposit. If customers pay before full build, you’ve validated willingness to pay. Track conversion rate from visitors to paid presale.

3. Pay-what-you-want / Suggested Price

   Test different suggested prices or tiers when asking people to sign up. Useful when business model flexible or for services. Compare average revenue per paying customer across variants.

Experiment logging template (one-line summary for each experiment)

• Date
• Hypothesis (one line)
• Test type (interview, landing page, presale, pricing A/B)
• Primary metric (e.g., conversion rate, signups, revenue per visitor)
• Sample size / duration
• Result (quantitative + 2–3 bullet learnings)
• Decision (Pivot / Persevere / Kill) and next steps

Early funnel measurement — one‑page dashboard

Track the few metrics that tell you whether an idea could scale. Keep it short.

• Visitors (V) — how many people saw the offer
• Leads / interest signups (L) — people who provided contact or expressed interest
• Trials / demos / engaged prospects (E)
• Paid conversions (P) — customers who paid or committed
• Revenue per visitor (R/V) and conversion rate (P/V)

Use simple ratios: L/V, E/L, P/V. Predefine a threshold for 'worth scaling' (for example, P/V > 1% at target price) before running the test.

Examples of quick experiments

• Run 10 customer interviews this week to validate the top 3 problems and capture willingness-to-pay ranges.
• Create a single landing page describing the offer and track signups for two price points for 2 weeks.
• Open 20 presale spots at a discounted pilot price — require payment and deliver limited scope work.

Common mistakes and how to avoid them

• Measuring the wrong thing: don’t confuse clicks with willingness to pay. Track actual commitments where possible.
• Small sample illusions: treat single anecdotes as hypotheses, not proof. Look for patterns across conversations.
• Designing vague success criteria: set quantitative thresholds and qualitative signals in advance.
• Bias in interviews: avoid selling during the interview; separate learning conversations from sales attempts.

Next steps and templates

Suggested immediate plan:

1. Define 1 hypothesis and its success criteria.
2. Schedule and complete at least 8 interviews using the script.
3. Choose one pricing experiment design and run it for 1–4 weeks.
4. Log results and make a Go / No‑Go decision.

Templates included in this kit:

• Interview note template (fields: customer profile, story, pain, current solutions, pricing signals, verbatim quotes)
• Experiment log (one-line summary per experiment)
• Landing page copy checklist

Quick tips

• Record interviews (with permission) and capture exact quotes — they’re persuasive for decisions and internal alignment.
• Focus on the decision journey: who decides, how long, and what else affects that choice.
• Prefer real commitments (payment, calendar booking) over surveys where possible.

Where this kit fits in the domain

This toolkit lives in the "Grow Your Business in a Changing World" resource collection. It focuses on rapid learning and low-risk experiments that feed higher-confidence growth decisions.

Signals this kit will help with

• You have growth ideas but no validated customer demand.
• You’re worried about spending marketing budget on unproven offers.
• You need a repeatable process to test pricing or product-market fit.

Credits & versioning

Author: The Hunger Engine Library. Kit version: 1.0. Copy and adapt to your team's language and context.