Consultative Discovery and Requirements Gathering Questions

Good probing question types for a 30‑minute customer interview (each avoids leading phrasing and invites open answers):

1. Contextual / background — "Walk me through a typical day when you use [product/solution]. What are the steps you take?"
2. Task‑based / process — "Can you describe the last time you tried to accomplish [specific task]? What did you do from start to finish?"
3. Pain‑point / frustration — "What was the most frustrating part of that experience? Can you tell me more about what made it difficult?"
4. Frequency / behavior — "How often do you perform this task, and what usually prompts you to do it?"
5. Outcome / goal — "What result are you hoping to achieve when you do this? How will you know you’ve succeeded?"
6. Emotional / impact — "How do you feel before, during, and after doing this? Why do those feelings matter to you?"
7. Workaround / coping strategies — "If the current tools don’t work, what do you do instead? Can you show or describe that workaround?"
8. Constraints / trade‑offs — "What limits or trade‑offs influence how you approach this task (time, budget, policies)? How do you decide what to compromise on?"

These cover behavior, needs, and unmet opportunities without steering answers.

Situation: You're on a 15-minute call with a demanding stakeholder pushing to expand the project scope mid-sprint.

Five clarifying questions (phrased to be concise and factual):

1. "Can you describe the additional capability you want and the user problem it solves?"
2. "Who is the primary user or persona for this change, and how does it change their workflow?"
3. "What business outcome or KPI are you hoping this expansion will improve, and by when?"
4. "Is this an absolute requirement for launch, or a nice-to-have that could follow in a later release?"
5. "Do we have any constraints I should be aware of (budget, compliance, dependencies, or timeline)?"

How I’d frame trade-offs to keep the conversation constructive:

• Acknowledge the value: "I see how this would improve X for users—good insight."
• Present concrete trade-offs: "To include this now we’d either push the release by 2–3 weeks, reduce scope of feature Y, or add developer headcount which increases cost. Which is preferable given your priority?"
• Offer alternatives: "We could deliver an MVP version now that covers the core user need and follow with an enhanced version in the next sprint—this balances speed and quality."
• Close with next step: "If you agree, I’ll map the impact to roadmap, resource needs, and a recommended option and share it within 24 hours."

Clarifying questions (grouped to quickly surface constraints and hypotheses)

• Merchant & product context
  • What is your merchant category (MCC), average ticket size, refund/return policy, and sales channels (card-not-present, in-person, mobile app, marketplaces)?
  • Are chargebacks concentrated on specific SKUs, campaigns, geographies, or customer cohorts?
• Dispute reasons & timelines
  • What are the most common dispute codes (fraud, non-receipt, product not as described, processing error)? Any temporal patterns?
  • Typical time between transaction and dispute filing?
• Current controls & workflows
  • What fraud/preauth/3DS/AVS/CVV, fulfillment tracking, refund/self-service, and customer support flows exist today?
  • What post-transaction evidence do you collect (tracking numbers, signed receipts, chat logs)?
• Data availability & access
  • Can you share transaction-level data, dispute outcomes, chargeback representment history, and user identifiers? How far back?
  • Any limitations due to PII, GDPR, or data retention?
• Partner & operational constraints
  • Who is the payment processor/acquirer/gateway? Do they support dispute alerts, retrieval request automation, or custom rules?
  • SLAs for response, acceptable false-positive rates, and tolerance for customer friction?

Initial experiment to validate the highest-impact assumption

• Key assumption: Most chargebacks are driven by preventable operational issues (non-delivery, unclear descriptors) rather than undetected fraud.
• Experiment (4 weeks, A/B test on 10k transactions):
  • Sample: Randomly split high-risk transactions (by simple rule: international + high ticket + first-time buyer) into control vs. intervention.
  • Intervention: Enhanced frictionless prevention — improved descriptor clarity on receipts, automated shipment-confirmation emails with tracking, and proactive customer outreach 48–72h post-purchase for orders not shipped.
  • Metrics: Chargeback rate per 1k txns (primary), dispute reason mix, false-positive cancellations, revenue impact, customer CSAT.
  • Success criteria: >=30% reduction in chargebacks for intervention cohort with <1% negative impact on revenue or CSAT.
• Data & next steps: If positive, iterate by adding targeted fraud rules (3DS enforcement, behavioral signals) and run representment automation with processor. If negative, pivot to testing stronger fraud-detection models.

First I’d clarify the three KPIs (example: “increase DAU”, “improve NPS”, “increase conversion rate”) and then ask focused questions to align the metric to strategy, user value and measurability:

Clarifying questions

• Business strategy: Which business objective does launch prioritize (acquisition, retention, monetization, engagement, brand)?
• Time horizon & targets: What is the time window and target magnitude for the KPI?
• User value: What user behavior or outcome are we trying to change for customers?
• Causality: Which features or experiments will move each KPI directly?
• Data & scope: Do we have reliable events/instrumentation to measure it? Is it at user, session, or cohort level?
• Acceptable trade-offs: What negative outcomes are tolerable (e.g., lower NPS for higher revenue)?
• Success criteria: What statistical confidence and segments matter?

How I’d eliminate two

• Eliminate DAU (engagement) if strategy is monetization/launch-to-convert: DAU is broad, noisy, influenced by many factors, and may not correlate with revenue uplift from this launch.
• Eliminate NPS (satisfaction) for launch: NPS is valuable long-term but slow to move and coarse for short-term launch decisions; low sensitivity for feature-level impact.

Primary metric choice and justification

• Choose: Conversion Rate (e.g., trial->paid or visitor->signup) as the primary launch metric.
• Why: Directly ties to monetization objective, represents clear user action (user value: achieved intent), and is measurable with existing instrumentation. It supports short-term A/B testing, gives actionable levers (UX flows, pricing, copy), and has clear success thresholds.
• Measurement plan: Define numerator/denominator, event definitions, segmentation (new vs returning users), guardrails (monitor funnel drop-offs, NPS and retention as secondary metrics), and required sample size and confidence level.

This ensures the primary metric maps to company goals, reflects user value, and is actionable/measurable; secondary metrics (NPS, DAU) remain monitored to detect unintended consequences.

Clarifying questions

Features / scope

• Which user flows must work fully offline (read-only vs read+write)? e.g., viewing content, creating/editing items, attachments, search?
• Are there size limits for offline data (e.g., weeks of data, recent N items, specific projects)?
• Do background tasks (notifications, background sync) need to run while offline?
• Which platforms must support offline (iOS, Android, web PWA, desktop)? Any OS/version constraints?
• Are attachments/media required offline or only metadata?

Conflict resolution & consistency

• Who “wins” on conflicting edits: last-writer-wins, server-authoritative, or merge-by-field?
• Should we surface conflicts to users or auto-resolve silently?
• Are some fields immutable or require manual reconciliation (e.g., billing, approvals)?
• Are we okay with eventual consistency or must some actions be transactional?

Data sync timing & UX

• When should sync occur: on reconnect, periodically, or only on user action?
• Should users be able to mark items “force-sync” or “offline-only”?
• How to indicate sync status and failures in the UI?
• What network conditions should trigger sync backoff or retry strategies?

Acceptable data-loss & failure modes

• What is the maximum acceptable window of data loss (e.g., none, <1 minute, session-level)?
• Which operations are non-recoverable and must be prevented offline (payments, legal sign-offs)?
• For partially uploaded artifacts (large files), acceptable fallback: resumable uploads or queue and notify failure?

MVP offline scope (recommended)

• Platforms: iOS, Android, PWA read/write for core entities
• Data: local cache of most-recent 30 days and current workspace/projects
• Actions allowed offline: create/edit/delete core items (text/metadata), attach small files (<5MB)
• Sync: automatic on reconnect with exponential backoff; show per-item sync status
• Conflict policy: optimistic local edits + server merge where possible; surface conflicts for key fields with clear UI prompt
• Exclusions: payments, approvals, large media, admin-level config

Why: this scope delivers tangible value quickly (field work, travel) while limiting complexity (large files, transactional operations) so engineers can implement reliable local storage, conflict UI, and retry/sync infrastructure for an initial release.