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.

HardTechnical

43 practiced

You have a time series with missing days and outliers. Describe a statistical approach to detect anomalies and build a seasonally adjusted baseline to quantify the impact of a marketing campaign. Mention methods, assumptions, and how you'd present uncertainty to stakeholders.

EasyTechnical

79 practiced

Explain the core statistical concepts used in A/B testing: null hypothesis, p-value, confidence interval, type I and II errors. Provide a short example of how a p-value could be misinterpreted by a non-technical stakeholder.

EasyTechnical

50 practiced

Write a SQL query (standard SQL / BigQuery or PostgreSQL) to compute Monthly Active Users (MAU) from an events table. Schema: events(event_id, user_id, event_name, occurred_at TIMESTAMP). Return rows: month (YYYY-MM), mau_count. Include timezone handling and sample output for 2024-01 and 2024-02.

MediumTechnical

37 practiced

Describe different approaches to attribute revenue to marketing channels (last-click, first-click, linear, time-decay, data-driven/multi-touch). For a mid-size company with limited instrumentation, recommend a practical attribution approach and explain pros/cons and implementation steps.

MediumTechnical

47 practiced

When presenting a time series KPI with seasonality, how do you choose between a rolling average, seasonally adjusted series, or period-over-period comparison? Give examples where each is preferable and explain how you'd implement seasonality adjustment for weekly seasonality.

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