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
Propose a parametric approach to estimate customer lifetime value (LTV) across cohorts that accounts for churn decay and discounting. Compare simple exponential churn decay to Pareto/NBD or BG/NBD models, explain how you would estimate parameters, how to validate out-of-sample predictions, how to incorporate acquisition costs and promotional spend into LTV, and how to present uncertainty to finance stakeholders.
EasyTechnical
0 practiced
As a BI analyst, explain the common account-level metrics used to report business health: churn rate, retention rate, annual recurring revenue (ARR), net revenue retention (NRR), and customer lifetime value (CLV). For each metric provide the calculation formula, a short numeric example, typical use cases, and common pitfalls such as handling upgrades/downgrades, partial-period customers, and currency changes that you would document when sharing with executives.
MediumSystem Design
0 practiced
Design the architecture for a daily executive report pipeline that must be available within 2 hours after day end, support interactive Power BI dashboards for 100 concurrent viewers, and combine CRM, billing, and event-stream data. Describe ingestion, ETL/transform cadence, data modeling (star schema, fact tables), caching strategies, data freshness SLAs, and observability/alerting you would implement.
EasyTechnical
0 practiced
Explain the difference between click-through rate (CTR) and conversion rate in digital marketing analytics. Provide formulas for both, a short numeric example, explain how each metric should be used at different stages of the funnel, and list measurement pitfalls such as bot clicks, impression deduplication, and attribution window effects.
HardTechnical
0 practiced
A sudden spike in reported purchases aligns with a marketing campaign, but reconciliation with the payment gateway suggests duplicate events inflated analytics. Outline how you would quantify the true incremental lift: steps to deduplicate analytics events, reconcile orders with payment gateway data, estimate corrected lift and confidence intervals, implement fixes, and communicate uncertainty and corrective actions to stakeholders.
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