Data Driven Recommendations and Impact Questions

Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.

HardTechnical

0 practiced

Explain the synthetic control method and how you would use it to measure the impact of a marketing campaign rolled out to a subset of cities. Specify required pre-treatment data, donor pool selection, implementation steps in SQL or Python, and diagnostics to validate the synthetic control.

EasyTechnical

0 practiced

Design an executive dashboard to monitor the impact of a pricing change across revenue, conversion, and customer satisfaction. List the top 6 KPIs, three visualizations you would include, filters and segmentation (e.g., by cohort, geography), refresh cadence, and suggested alert thresholds for immediate escalation.

EasyTechnical

0 practiced

You have an events table in your data warehouse with schema:

events(event_id STRING PK, user_id STRING, device_id STRING, event_name STRING, event_ts TIMESTAMP, properties JSON)

Describe which events and properties you would instrument to measure 7-day retention and DAU precisely. Then write a SQL query (ANSI SQL) that computes daily DAU and 7-day rolling retention rate per cohort (by install_date). Explain any deduplication logic and user identity assumptions.

HardTechnical

0 practiced

Design a single-page executive visualization to compare 10 candidate initiatives on expected impact, confidence, cost, and time-to-value to aid prioritization. Specify the visual encodings (e.g., x/y axes, sizing, color), the underlying data model, example filters, and how you would surface uncertainty and assumptions for each initiative.

HardTechnical

0 practiced

You observe that early experiments show strong positive effects that fade in later experiments. How would you detect and correct for heterogeneity, publication bias, sequential peeking, and regression to the mean across a program of experiments? Propose statistical and operational controls to improve long-term decision quality.

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