Company Product and Design Knowledge Questions

Demonstrate a well researched understanding of the company, its major products, target users, market position, and core business model, combined with familiarity with the company design philosophy and visible product design patterns. Prepare to speak about flagship products and features, typical user demographics and needs, the engineering or product challenges the company faces, and how those constraints shape product and design decisions. For design roles, be ready to articulate what you admire about the company design aesthetic, specific patterns or interactions you observe, accessibility and usability trade offs, and how your own design sensibilities or past work align with and could contribute to that aesthetic. For non design roles, emphasize product priorities, technical or operational challenges, and how your skills would help advance those products. Cite concrete examples such as a recent feature, a product workflow, a known engineering challenge, or public design documentation to show you have done focused research.

MediumTechnical

67 practiced

Design a KPI dashboard focused on accessibility for product designers and PMs. Propose six metrics (both quantitative and qualitative) you would include to help prioritize accessibility work. For each KPI explain how you would measure it, the data sources required, and an example alert threshold or rule that would flag a problem.

Sample Answer

Situation: Product teams need a compact, actionable accessibility KPI dashboard to prioritize fixes, allocate design/dev effort, and show progress to PMs and designers.

Proposed six KPIs (with measurement, data sources, and alert rules):

1) Accessibility Coverage (% of UI audited)- Measure: (Number of critical screens/components audited / total in scope) * 100- Data sources: Audit tracker (spreadsheet/issue tracker), UI inventory from design system- Alert: < 60% audited or no new screens audited in 30 days

2) Severity-weighted Issue Count- Measure: Sum(issue_count_by_component * severity_weight) (weights e.g., Critical=5, Major=3, Minor=1)- Data sources: Automated scanner outputs (Axe/Lighthouse), manual WCAG audit tickets- Alert: Weighted score increases > 20% week-over-week or > X (e.g., 100)

3) Time-to-Remediate (median days)- Measure: Median days from issue reported to resolved per severity- Data sources: Issue tracker (Jira/GitHub) timestamps, tag by a11y- Alert: Median > SLA (Critical > 7 days, Major > 30 days)

4) User Impact Estimate (monthly affected users)- Measure: Sum of monthly active users for pages/components with unresolved critical issues- Data sources: Product analytics (GA/Segment), mapping of page/component to analytics events, issue status- Alert: > 10,000 MAUs affected or > 5% of product MAU

5) Keyboard & Screen-reader Success Rate (qualitative -> quantifiable)- Measure: % of test tasks completed successfully by assistive-tech test users or automated end-to-end paths (synthetic tests)- Data sources: Usability test sessions, automated Playwright/Puppeteer scripts with accessibility checks- Alert: Success rate < 85% or drop > 10% vs baseline

6) Accessibility Regression Rate- Measure: # of new accessibility regressions introduced per release / total releases- Data sources: CI/CD test reports, automated regression tests, PR labels- Alert: >1 critical regression per release or regression trend upward 3 consecutive releases

Design notes for the dashboard:- Display leading indicators (coverage, automated scan trend) and lagging (remediation time, user impact).- Provide filters by product, platform, component, and severity.- Include drilldowns: raw issues, linked tickets, owner, and suggested remediation priority score.- Automate alerts via email/Slack when thresholds hit; surface suggested next actions to PMs/designers.

MediumTechnical

67 practiced

Case study: You observe a competitor launch a novel interaction demonstrated in a public demo. Outline an analysis plan to estimate whether adding a similar interaction would be valuable for your company. Include the types of data you would need, experiments or pilots you would run, leading metrics to forecast ROI, and how you would present a recommendation to product leadership.

Sample Answer

Framework: treat this as a product-investment decision — gather evidence, estimate impact, validate with experiments, then recommend with ROI and risk.

1) Clarify scope & success criteria- Define the interaction (what problem it solves, target user segments, primary KPI e.g., conversion or retention).- Constraints: engineering effort, time, legal/privacy, UX guidelines.

2) Data to collect- Product telemetry: current funnels, activation rates, DAU/WAU, session length, feature usage by cohort.- Business metrics: conversion to paid, average revenue per user (ARPU), churn, LTV.- Qualitative: competitor demo transcript, user interviews, support tickets, NPS comments.- Operational: implementation cost estimates, expected maintenance.

3) Hypothesis & leading metrics- Hypothesis: "Adding interaction X increases activation by Y% and reduces churn by Z% for segment S."- Leading metrics to forecast ROI: activation lift (first-week conversion), engagement depth (time on task, events/session), short-term conversion uplift, retention delta (30-day), funnel drop-off reduction.- Map metric changes to revenue: ΔARPU and retention → ΔLTV; use cohort LTV model.

4) Experiments/pilots- Instrument prototype as feature-flagged A/B test (control vs variant). If risky, run an opt-in pilot with power users.- Variants: simple mimic of competitor, smaller/lightweight version, and control.- Sample size & duration: compute power for expected minimum detectable effect (e.g., 3–5% lift), run across representative segments for 2–4 weeks.- Track secondary metrics for harm (error rates, task abandonment, support volume).- If backend impact is large, run a business-simulated pilot in a sandbox environment and usability sessions before A/B.

5) Analysis approach- Pre-register metrics and success thresholds; use sequential testing guardrails.- Use uplift modeling, cohort analysis, and segmented treatment effects (by acquisition channel, device).- Back-of-envelope ROI: incremental revenue = users_exposed * conversion_lift * ARPU; incremental cost = development + infra + ops. Compute payback period and NPV over expected feature lifetime.

6) Presentation to leadership- One-page executive summary: recommendation (Go/No-Go/Iterate), top-line projected ROI, key assumptions and sensitivity ranges.- Visuals: funnel before/after, cohort LTV scenarios, A/B results with confidence intervals, cost breakdown, timeline and risks.- Ask for a clear decision: pilot approval, budget, or kill. Provide contingency plan (roll-back criteria, monitoring dashboard).- Deliverables: BI dashboard with live experiment metrics, SQL queries and data lineage, and a reproducible ROI model so leadership can adjust assumptions.

This plan balances quantitative forecasting, rapid validated learning, and clear decision criteria suited for BI-driven product decisions.

HardTechnical

69 practiced

Hard: The company is expanding globally and needs dashboards to surface locale-specific UX issues (language, reading direction, local patterns). Propose how you would modify analytics and dashboards to surface locale-specific usability problems and measure their business impact on engagement and conversion across locales. Include prioritization criteria and an ROI framework.

Sample Answer

Requirements & scope:- Goal: surface locale-specific UX issues (language, RTL/LTR, date/number formats, cultural patterns) and measure impact on engagement & conversion by locale.- Constraints: existing event schema, BI tools (Looker/Tableau), privacy, sampling latency.

Approach (framework):1. Data instrumentation- Add locale metadata to all key events: user_locale, display_locale, inferred_geo, device_locale, text_rendering_errors, font_fallback_used, is_rtl, keyboard_layout.- Track UX signals: scroll depth, time-on-screen, click-throughs, form abandonment steps, input errors (validation/localization), help/feedback submissions, screen-render failures, A/B variant id.

2. Dashboard design (layers)- Overview: global map + top 20 locales by users, conversion, ARR impact.- Locale drill: per-locale funnel (impression → click → add-to-cart → purchase) + UX error overlays (render errors, high input error rate), heatmaps, top failing pages/components.- Comparative: normalized per-1000 users metrics comparing locale vs. global median (z-score).- Temporal & cohort: before/after releases, A/B for locale fixes.- Segmentation: device OS, browser, language vs. geo mismatch.

3. Analysis & prioritization- Prioritize by expected impact = (users_in_locale * conversion_gap * avg_order_value) and ease = estimated engineering effort; rank by ROI = Impact / Effort.- Signals raising priority: >X% higher form abandonment than baseline, >Y render error rate, negative NPS/comments trend.

4. ROI framework & measurement- Baseline: capture current conversion_rate_locale and revenue_per_user_locale.- After fix: run controlled rollouts (canary or A/B) per locale; measure delta_conversion, delta_engagement at 95% CI.- Calculate incremental revenue = users_exposed * delta_conversion * avg_order_value over 90 days; subtract implementation + localization cost to compute payback and NPV.

Example KPI set:- Locale Conversion Rate, Conversion Gap vs. Global, Form Error Rate, Render Failure Rate, Time to Complete Checkout, Help Ticket Rate per 1k users, Revenue per User.

Implementation notes:- Build derived tables (materialized) to compute per-locale baselines, z-scores, ROI estimates.- Automate alerts for anomalies (Slack/email) when locale metrics exceed thresholds.- Embed diagnostic links: session replay sampling, logs for font/system locale.

Trade-offs & risks:- Instrumentation cost vs. sampling — start high-priority locales (top 10 by revenue/users) then scale.- Privacy: aggregate and anonymize PII.

Result expectation:- Rapid identification of high-ROI localization defects, data-driven prioritization, measurable uplift in conversion and revenue per locale with transparent payback timelines.

MediumTechnical

53 practiced

Write a SQL approach to compute N-day retention table for signup cohorts using the schemas users(user_id, signup_timestamp) and events(event_id, user_id, event_type, timestamp). The output should show for each signup_week the percentage retained at day_0, day_1, day_7, and day_30. State any assumptions and how you handle users who have no events after signup.

Sample Answer

Approach: create weekly signup cohorts, compute for each user whether they had at least one post-signup event within N days (including day_0 = signup day). Aggregate to percent retained at day_0, day_1, day_7, day_30. Assumptions:- signup_timestamp and event.timestamp are UTC timestamps.- day_N means events with timestamp >= signup_timestamp AND < signup_timestamp + (N+1) days, but we treat day_0 as any event on same calendar day (or event >= signup_timestamp for strict).- Users with no events after signup count as retained only for day_0 if they have an event at signup time; otherwise counted as 0 for all windows.

SQL (Postgres flavor):

sql

WITH users_cohort AS (
  SELECT
    user_id,
    signup_timestamp,
    date_trunc('week', signup_timestamp)::date AS signup_week
  FROM users
),
events_after AS (
  SELECT
    u.user_id,
    u.signup_week,
    u.signup_timestamp,
    e.timestamp AS event_ts,
    (e.timestamp >= u.signup_timestamp) AS post_signup
  FROM users_cohort u
  LEFT JOIN events e ON e.user_id = u.user_id
),
flags AS (
  SELECT
    user_id,
    signup_week,
    MAX(CASE WHEN event_ts >= signup_timestamp AND event_ts < signup_timestamp + interval '1 day' THEN 1 ELSE 0 END) AS day_0,
    MAX(CASE WHEN event_ts >= signup_timestamp AND event_ts < signup_timestamp + interval '2 day' THEN 1 ELSE 0 END) AS day_1,
    MAX(CASE WHEN event_ts >= signup_timestamp AND event_ts < signup_timestamp + interval '8 day' THEN 1 ELSE 0 END) AS day_7,
    MAX(CASE WHEN event_ts >= signup_timestamp AND event_ts < signup_timestamp + interval '31 day' THEN 1 ELSE 0 END) AS day_30
  FROM events_after
  GROUP BY user_id, signup_week
),
cohort_size AS (
  SELECT signup_week, COUNT(*) AS cohort_users
  FROM users_cohort
  GROUP BY signup_week
)
SELECT
  f.signup_week,
  cohort_size.cohort_users,
  ROUND(100.0 * SUM(f.day_0) / cohort_size.cohort_users, 2) AS pct_day_0,
  ROUND(100.0 * SUM(f.day_1) / cohort_size.cohort_users, 2) AS pct_day_1,
  ROUND(100.0 * SUM(f.day_7) / cohort_size.cohort_users, 2) AS pct_day_7,
  ROUND(100.0 * SUM(f.day_30) / cohort_size.cohort_users, 2) AS pct_day_30
FROM flags f
JOIN cohort_size USING (signup_week)
GROUP BY f.signup_week, cohort_size.cohort_users
ORDER BY f.signup_week;

Notes:- If no events exist for a user, flags will be 0 (not retained).- To treat signups with implicit first-use (signup implies activity), mark day_0 = 1 when needed by joining a signup_event flag.- For large datasets, pre-aggregate events per user into min(event_ts) or use windowed existence flags and ensure indexes on events(user_id, timestamp).

MediumBehavioral

100 practiced

Behavioral: Tell me about a time you partnered closely with product designers to translate qualitative UX findings into quantitative metrics or dashboards that influenced a product decision. Use the STAR format and include concrete numbers where possible, describing the situation, your role, the analysis or dashboard you built, and the outcome.

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