Data Analysis and Performance Measurement Questions

Covers the end to end use of quantitative analysis to track, interpret, and act on business performance across accounts and campaigns. Candidates should be fluent in account level metrics such as customer retention rate, net revenue retention, annual recurring revenue, net promoter score, customer health scores, and customer lifetime value, as well as marketing and acquisition metrics such as click through rate, conversion rate, customer acquisition cost, return on advertising spend, and attribution model outcomes. Expect discussion of data sources and instrumentation, cohort and funnel analysis, segmentation, anomaly detection, attribution approaches, and calculating return on investment for initiatives. Candidates should be able to describe how they used analytics tools and queries, dashboards, and experiments or A B tests to identify at risk accounts or underperforming campaigns, prioritize actions, optimize strategies, and measure the impact of initiatives. Strong answers explain concrete metrics chosen, analysis methods, tools used, how results informed decisions, and how success was measured over time.

HardTechnical

0 practiced

Design a robust approach to detect fraud or bot activity in acquisition funnels that inflate CTR/installs but do not convert. Describe signals (e.g., IP velocity, improbable click-to-install times, user-agent anomalies), unsupervised and supervised models you would use, and how you would operationalize mitigation and retroactive correction of metrics.

MediumTechnical

0 practiced

You plan an A/B test to change a checkout flow. Baseline conversion is 5% and you expect a lift to 6%. Calculate the sample size needed per variation for 80% power and alpha=0.05 using normal approximation for proportions. Show formula, z-values, and numeric result, listing assumptions.

EasyTechnical

0 practiced

Write a Python (pandas) function that accepts an events DataFrame with columns (user_id, event_time, event_type) and returns daily unique active users (DAU) and rolling 7-day MAU. Indicate efficiency considerations for large datasets and how you'd adapt for distributed processing.

EasyTechnical

0 practiced

You have funnel events: impressions → clicks → signups → purchases. Given daily counts for each step, describe how to compute per-step conversion rates, overall conversion, and identify the biggest drop-off. Provide a sample interpretation and a recommended first experiment to improve conversions.

EasyTechnical

0 practiced

Explain cohort analysis and write a SQL snippet to compute 30-day retention per signup_week cohort given a 'users' table (user_id, signup_date) and an 'events' table (user_id, event_date). Describe how you'd present the results visually to product managers.

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