Company Business Model and Product Market Understanding Questions

Demonstrate understanding of how the company creates and captures value through its business model and product offering. This includes knowledge of the product portfolio, value proposition, target customer segments, use cases, pricing model, and how products map to market needs. Candidates should be able to explain how the company makes money, the primary revenue streams, product positioning, and how product decisions affect customer value and strategic direction.

MediumTechnical

61 practiced

Given tables:

events(event_id UUID, user_id UUID, event_time timestamptz, event_type text, campaign_id TEXT),purchases(purchase_id UUID, user_id UUID, purchase_time timestamptz, amount_cents INTEGER)

Write SQL (PostgreSQL) to attribute each purchase to the last non-null campaign_id for that user within the 30 days prior to the purchase (last-touch). Explain assumptions for purchases without a campaign_id and how to scale this approach.

HardTechnical

69 practiced

Design an experiment to test a new paid per-seat add-on feature. Ensure accurate measurement of incremental revenue, usage, and retention while avoiding selection bias. Describe randomization unit, instrumentation points, sample size/power calculations, primary and guardrail metrics, and a post-experiment rollout decision process.

EasyTechnical

71 practiced

Write a SQL query (PostgreSQL) to compute Monthly Recurring Revenue (MRR) per month from the following subscriptions table: subscriptions(subscription_id UUID, customer_id UUID, start_date DATE, end_date DATE NULLABLE, plan_monthly_price_cents INTEGER, status TEXT). Assume status is 'active' or 'canceled' and ignore proration. Return: month (YYYY-MM), mrr_cents. Also list assumptions about cancellation handling and timezones.

HardTechnical

56 practiced

Design an analytical approach to estimate Customer Lifetime Value (LTV) for subscription customers when data is right-censored (many are still active) and churn varies by cohort. Which statistical models would you consider (e.g., Kaplan-Meier, Pareto/NBD, Cox), what inputs are required, how do you incorporate recurring revenue and discounts, and how would you compute and present confidence intervals for projections?

MediumTechnical

72 practiced

Explain the trade-offs between pre-aggregating metrics in an OLAP store (materialized tables) versus computing them on-the-fly from raw event data for dashboard queries. Discuss latency, cost, storage vs compute tradeoffs, data freshness, flexibility when new dimensions are required, and operational complexity including backfills.

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