Data and Business Outcomes Questions

This topic focuses on converting data analysis and insights into actionable business decisions and measurable outcomes. Candidates should demonstrate the ability to translate trends into business implications, choose appropriate key performance indicators, design and interpret experiments, perform cohort or funnel analysis, reason about causality and data quality, and build dashboards or reports that inform stakeholders. Emphasis should be on storytelling with data, framing recommendations in terms of business levers such as revenue, retention, acquisition cost, and operational efficiency, and explaining instrumentation and measurement approaches that make impact measurable.

EasyTechnical

0 practiced

A third-party vendor delivers a monthly CSV feed with customer transactions. You suspect data quality issues. Describe a prioritized checklist you would run to assess data quality, the metrics you would compute to quantify issues, and how you would communicate severity and next steps to engineering and the business owner.

MediumTechnical

0 practiced

You observe two user acquisition channels with different retention curves. Design an analysis plan to compare long-term value across channels while controlling for differences in initial user quality and demographics. Include data requirements, statistical tests or models, and how you would present actionable recommendations.

HardTechnical

0 practiced

Company leadership wants continuous monitoring of experiments but is worried about false positives from 'peeking'. Explain statistical approaches to allow interim looks (alpha spending, O'Brien-Fleming, Pocock boundaries) and contrast with Bayesian approaches. Recommend a company policy for continuous monitoring, describing operational constraints.

EasyTechnical

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You're instrumenting a checkout funnel for a web product. List the minimal set of events and properties you would capture to measure funnel conversion reliably (page view, add_to_cart, begin_checkout, payment_attempt, payment_success, etc.). For each event include required properties (e.g., user_id, session_id, product_id, price, currency) and explain how you'd keep schema backward-compatible over time.

EasyTechnical

0 practiced

Write a SQL query (any ANSI SQL / Postgres) that builds a 7-day retention cohort table from the following schema:

user_events(user_id BIGINT, event_time TIMESTAMP, event_name TEXT)

Define cohorts by users' first 'signup' date (cohort_week), and compute percentage of users in each cohort who perform 'open_app' on day 1, day 2, ..., day 7. Return cohort_week and retention columns day_1 ... day_7.

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