Market Research and Customer Insights Questions

Situation: I’m evaluating a new product where a reputable analyst report estimates a $2B market, but our user survey shows low willingness-to-pay (WTP) and low purchase intent.

Approach (how I’d reconcile and validate):

1. Validate inputs and definitions

• Confirm analyst report scope: TAM/SAM/SOM definitions, geography, customer segments, price assumptions, and methodology.
• Inspect our survey: sample size, sampling bias, question framing, incentive design, and segmentation (early adopters vs. general population).

2. Segment and compare apples-to-apples

• Break the analyst market into segments (industry, company size, use case).
• Re-analyze survey responses by those same segments to see if some segments have higher WTP/purchase intent.

3. Run triangulation analyses

• Conjoint or Van Westendorp pricing study to get more accurate price elasticity and acceptable price ranges.
• Behavioral experiments: A/B test landing pages with mock pricing and CTA to measure conversion intent (paid sign-up, signup funnel).
• Customer interviews / qualitative discovery for root causes of low intent (value perception, alternatives, trust, procurement complexity).
• Competitor pricing and publicly available revenue indicators (job postings, customer lists, public filings) to estimate real market traction.

4. Quantitative synthesis

• Build bottom-up SOM using addressable customers * adoption rate scenarios (best/likely/worst) informed by experiments and analyst assumptions.
• Sensitivity analysis on price, conversion, and retention to see when market becomes attractive.

Decision criteria to postpone product development

• If refined bottom-up SOM under realistic adoption/pricing scenarios cannot reach a minimum viable revenue threshold for business objectives within a 3–5 year horizon.
• If pricing experiments and qualitative interviews show fundamental mismatch in perceived value (users unable to articulate willingness to pay even after prototype demos).
• If procurement or regulatory barriers make customer acquisition costs prohibitively high vs. LTV.
• If segments that match the analyst’s assumptions are non-representative or inaccessible to us.

Next steps if ambiguous:

• Run a small paid pilot or concierge sales effort focused on high-value segments; postpone full development until pilots validate repeatable acquisition and pricing.

Approach: Combine Van Westendorp (price perception), a discrete-choice conjoint (feature + price tradeoffs), and a direct WTP/Gabor‑Granger check to triangulate willingness to pay (WTP) and price elasticity for freemium → paid tiers.

Survey structure & key questions:

• Screener: active photo-editing users, usage frequency, platform, pro vs casual, current spend on editing apps.
• Van Westendorp (4 questions):
  • “At what price would you consider the paid version so expensive you would not consider it?”
  • “At what price would you consider it so cheap you’d doubt quality?”
  • “At what price would you consider it a good value?”
  • “At what price would you begin to think it’s getting expensive but still consider?”
• Gabor‑Granger (direct WTP): show single product description (paid tier features) and ask likelihood to buy at sequential price points (e.g., $1/mo, $3, $5, $8, $12).
• Discrete-choice conjoint (CBC): 8–12 choice tasks varying price and key features (advanced filters, batch processing, RAW support, cloud storage, watermark removal). Include an opt-out “none”.
• Open-ended/value drivers: “Which features make you willing to pay more?” and “Max monthly you’d pay for feature X”.
• Behavioral anchors: current subscription services used and price paid.

Sample size rationale:

• Overall target N = 1,200 respondents to allow stable conjoint estimates and segmentation (rule: ~50 respondents per estimated parameter). If constrained, minimum N = 600.
• Ensure at least 200–300 per priority segment (power users/pro photographers, hobbyists, social sharers).

Segmentation plan:

• Usage intensity (heavy vs light)
• Professional status (professional/semipro vs consumer)
• Platform (mobile vs desktop)
• Price-sensitivity clusters from Van Westendorp (value seekers vs quality seekers)
• Current spenders vs non‑spenders

High-level analysis → pricing recommendation:

1. Clean & weight sample to target user base.
2. Van Westendorp: compute PPD (price point of marginal cheapness/expensiveness), identify acceptable price range and optimal price (intersection of cumulative curves).
3. Gabor‑Granger: derive demand at discrete prices → estimate revenue curve and price elasticity.
4. Conjoint: estimate part‑worths and simulate share-of-choice for candidate packages/price points using market simulation (include opt-out). Segment conjoint results to see differentiated willingness to pay.
5. Combine: use conjoint for product-feature packaging, Van Westendorp/Gabor‑Granger to set price bounds and expected conversion. Optimize for revenue = price × choice probability and consider LTV uplift for higher tiers.
6. Sensitivity & scenario: run scenarios for conversion lift, churn, and tier mix; recommend launch price(s) with A/B test plan (e.g., test 2 prices around predicted optimum) and monitoring metrics (conversion, ARPU, churn).

Deliverable: recommended tiering (e.g., $4.99/mo standard, $9.99/mo pro) with confidence intervals and A/B test roadmap.

Market research questions (what I must know)

• Target customers: Which company sizes, industries, and buyer personas have the strongest pain aligned to our product? (SMB vs mid-market vs enterprise)
• Willingness-to-pay: What ARPU uplift is realistic per segment? What pricing/packaging models do they prefer (seat, usage, tiered, enterprise license)?
• Decision process: Who are the economic buyer, technical buyer, and day-to-day users? Typical sales cycle length and procurement/legal hurdles?
• Competitive landscape: Who are incumbents and substitutes, what are their feature gaps, pricing, integrations, and contract terms?
• Integration & security needs: Required APIs, SSO, data residency, compliance (SOC2, GDPR) that affect cost/time-to-market.
• Implementation cost and ROI: Implementation effort, onboarding time, and measurable ROI customers expect.

Validation experiments (hypothesis-driven, low-to-higher fidelity)

1. Customer discovery interviews (2–3 weeks): 30 targeted interviews across segments to validate pain, buyers, and purchase triggers. Success: >60% express willingness to pay and cite specific use cases.
2. Pricing and packaging A/B tests (4–6 weeks): Mock pricing pages + gated interest form; measure conversion to MQL and price sensitivity. KPI: acceptable conversion at target price point.
3. Concierge/Pilot program (6–12 weeks): Recruit 5 pilot customers for paid pilot with SLAs and limited integrations. Deliver onboarding, measure time-to-value, NPS, and retention intent. Success: >70% convert to paid contract.
4. Sales motion experiment (8–12 weeks): Run a small outbound SDR + AE pilot for one vertical, log sales cycle length, win rate, ACV, required touch points. KPI: target ACV and CAC payback within threshold.
5. Technical feasibility spike (2–4 weeks): Build minimal SSO, basic role management, and an API stub to validate engineering effort and performance. Measure build time and maintenance cost.
6. Legal & procurement test: Send mock contracts/terms to 3 prospective customers to surface negotiation points and required concessions.

Organizational changes required
Roadmap & Product:

• Create a B2B product pillar with prioritized items: multi-tenancy/roles, SSO, audit logs, admin console, billing/usage metering, onboarding flows.
• Shift OKRs: from MAU/engagement to ARPU, ACV, time-to-value, and retention (logo & revenue retention).
• Adopt customer-driven roadmap: include pilot feedback loops, success metrics, and enterprise feature gating.

Go-to-market / Sales:

• Build a two-track GTM: self-serve freemium for B2C/light SMB; direct sales motion for mid-market/enterprise.
• Hire/align SDRs, AEs, and a Sales Engineer for technical demos and pilots; train on new ICP and value propositions.
• Set up Salesforce/CRM processes for longer cycles, pipeline stages, contract templates, and enablement materials.

Customer Success & Support:

• Create onboarding and implementation teams (CSMs + implementation specialists) for paid accounts; define SLAs.
• Build playbooks: onboarding checklist, health scoring, expansion motions, and renewal cadence.
• Instrument product analytics and CSM dashboards (product usage, feature adoption, risk signals).

Cross-functional enablers

• Legal: standard enterprise contracts, SLAs, data processing agreements.
• Finance: new billing/invoicing, revenue recognition, churn modeling.
• Engineering: dedicated roadmap capacity, monitoring for multi-tenant ops, security certification plan.
• Marketing: vertical messaging, case studies, and demand-gen for ICPs.

High-level timeline & success criteria (first 6 months)

• Months 0–1: Discovery interviews + pricing tests.
• Months 1–3: Pilot builds, technical spikes, legal template prep.
• Months 3–6: Sales pilot, onboarding of first paid customers, measure ACV, CAC, time-to-value, NPS. Decision point: proceed full GTM if pilot ACV and retention projections support target ARPU and CAC payback.

This approach balances qualitative discovery, rapid technical validation, and real sales pilots to de-risk the pivot while aligning org structure and roadmap to capture B2B value.

Approach (overview): run a three-phase research program — Desk Review, Expert Validation, and Regulatory-Go/No-Go & Sequencing — producing a decision pack for roadmap and launch milestones.

Desk Review — information sources:

• EU-level: ECB, European Banking Authority (EBA) opinions, PSD2, MiCA, AMLD, Payment Services Directive texts and Q&As.
• Member-state regulators: FCA (for UK-adjacent), BaFin (DE), ACPR (FR), DNB (NL), CNB (CZ) — plus national registers for licensable activities.
• Industry & legal: law firm briefs (CMS, Linklaters, Hogan Lovells), Big Four regulatory updates.
• Industry bodies and standards: European Payments Council, FintechEurope, EBA consults.
• Market data: competitor filings, public license applications, privacy (GDPR) guidance.

Expert validation:

• Create an issue log with interpretations prioritized by risk and ambiguity.
• Set up 1:1s with local counsel + compliance leads in target countries and 2–3 regulator touchpoints (ask for informal guidance or sandbox rules).
• Run a workshop with external consultant + internal legal to reconcile differences and document rationale.

Priority compliance questions:

1. Is the product a payment service, e-money, or investment product? (licensing consequence)
2. Does RD/PSP or EMI licence apply per jurisdiction?
3. AML/CFT obligations: KYC thresholds, PEP/Sanctions screening, transaction monitoring needs.
4. Consumer protection & disclosure requirements (fees, FX, dispute resolution).
5. Data residency & GDPR implications for cross-border operations.
6. Sandbox/Passporting feasibility and capital/PRUDENTIAL requirements.

Regulatory timing & GTM sequencing:

• Map license timelines (informal clarity, application, approval, appeals): some EMIs ~6–12 months; payment registrations shorter but vary.
• Use phased market entry: launch in permissive jurisdictions first (where license clear/fast), or launch via PSP partners/white-label while pursuing full licence.
• Buffer roadmap with gating milestones: regulator feedback received → apply → approval expected → full launch; parallelize non-licensed product features, pilot via sandbox to de-risk.
• Plan contingency: partner contracts, temporary agent models, and marketing hold criteria based on regulatory milestones.

Deliverable: decision pack with recommended initial jurisdictions, required licences, estimated timelines/costs, prioritized compliance checklist, and go/no-go gating for roadmap sprint planning.

Detecting survivorship bias

• Compare report scope vs. universe: check whether the analyst only lists "active" winners. Request or reconstruct the full vendor registry (including delisted/ceased vendors) over the period.
• Look for truncated time windows or milestones (e.g., “top vendors in 2024” without historical cohort data).
• Examine entry/exit counts over time — spikes in exits or steady churn suggest survivors-only framing.
• Check underlying metrics: if growth rates are calculated on current survivors only, that's a red flag.

Practical adjustments

• Build historical cohorts: group vendors by entry year and follow each cohort’s size, average revenue, and survival rate over time.
• Use time-series of entrants/exits: compute net cohort attrition and incorporate it into market size or share estimates.
• Incorporate failure/exit rates: adjust average growth metrics downward by expected attrition (e.g., Kaplan–Meier estimate for survival probability).
• Weight averages by cohort size at origin (not current survivors) to avoid over-emphasizing long-lived winners.

Short example adjustment (logic)
Suppose report shows average 5-year revenue CAGR = 30% computed across 10 current top vendors (survivors). You know 40 vendors entered five years ago; 6 survived (the top 10 are subset). Compute cohort-weighted CAGR:

• Step 1: For each of the 40 original entrants, record 5-year revenue (use 0 or last observed revenue for failed firms).
• Step 2: Replace missing/failure firms with terminal revenue = 0 (or use liquidation value), then compute mean CAGR across all 40.
• Step 3: Alternatively, compute expected CAGR = P(survive)*E[CAGR | survive] + P(fail)*E[CAGR | fail].

If 6/40 survived, P(survive)=0.15. If survivors’ mean CAGR=30% and failed firms effectively −100% (to zero), expected cohort CAGR ≈ 0.1530% + 0.85(−100%) ≈ −82.5% — showing the analyst’s 30% is massively biased. A more realistic adjustment uses partial revenue recovery on exit instead of −100%; still yields a much lower cohort-average growth than the survivors-only figure.

Actionable: When presenting product strategy, use cohort-weighted market growth and sensitivity ranges (best/worst case based on survival assumptions) to set realistic TAM/SAM projections and prioritize features for retention vs. acquisition.