This is the core of the whole case and I spent way too long setting up the framework before getting to actual numbers.
Start by framing the 20% drop as a multiplicative decomposition: Revenue = Traffic × Conversion Rate × Average Order Value (AOV). Then, for each component, break down further into price, mix, discounts, and post-purchase deductions (returns/cancellations) using a waterfall to attribute percentage-point contributions. Use a structured, step-by-step approach to isolate each effect and quantify its impact.
Pro tip: Always validate the decomposition by ensuring the sum of percentage-point contributions equals the total drop (20%). Also, consider seasonality and external factors (e.g., crypto market volatility for Coinbase) before attributing changes to internal metrics.
Decompose the 20% revenue drop into traffic, conversion rate, and AOV using the multiplicative formula. Calculate the percentage change for each and their approximate contributions to the total drop.
Further decompose AOV changes into price effects (changes in list prices), mix effects (shift in product/customer mix), and discount effects (changes in promotional depth or frequency).
Adjust revenue for returns and cancellations by analyzing their rates and impact on net revenue. Break down returns/cancellations into price, mix, and discount effects if possible.
Build a waterfall chart showing the percentage-point contribution of each factor (traffic, conversion, price, mix, discounts, returns, cancellations) to the total 20% drop, ensuring they sum to 20%.
Check for seasonality, external events, and data quality issues. Validate the decomposition by cross-checking with other metrics or segments, and consider if the drop is uniform across segments or driven by a specific one.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by segmenting the revenue drop by key dimensions (product, geography, user cohort, time) to localize the issue. Then prioritize hypotheses that can be tested quickly with existing data, focusing on high-impact areas like conversion funnels, trading volumes, and external events. Use rapid queries and dashboards to confirm or falsify each hypothesis within 24 hours.
Pro tip: Focus on falsification: design checks that can quickly rule out hypotheses, as eliminating wrong paths is as valuable as confirming the right one. Also, consider data freshness and pipeline delays to avoid false conclusions.
Break down the revenue drop by dimensions such as product (e.g., spot trading, staking), geography, user type (retail vs institutional), and time (hourly/daily) to identify where the drop is concentrated.
Examine key funnel metrics: active users, conversion rates, trading volumes, and average revenue per user. Compare against historical trends and seasonality to spot anomalies.
Overlay external data like crypto market prices, competitor actions, or regulatory news to see if the drop aligns with market-wide movements or specific events.
Ensure the drop is not due to data pipeline issues, logging errors, or tracking changes by cross-checking with multiple data sources and recent deployments.
Rank hypotheses by likelihood and impact, then run quick queries or A/B tests (if applicable) to confirm or falsify each, focusing on the most probable causes first.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on how to set up the control group cleanly given both treatments happened close together.
Start by framing the problem as a causal inference challenge with multiple simultaneous interventions and time-varying confounders. Propose a quasi-experimental design (e.g., difference-in-differences or synthetic control) that isolates the free-shipping threshold effect by using a control group unaffected by the threshold but exposed to the competitor sale and seasonality. Then outline pre-trend checks and robustness tests to validate the parallel trends assumption and rule out alternative explanations.
Pro tip: Emphasize that you would pre-register the analysis plan and use placebo tests on pre-period data to demonstrate rigor. Also, mention that you would triangulate with individual-level data (e.g., users in regions where the threshold didn't apply) to strengthen causal claims.
Clearly state the target effect (e.g., ATT of free-shipping threshold on conversion) and list the three confounders: competitor sale, seasonality, and any other concurrent changes. Discuss how each could bias the estimate.
Propose a difference-in-differences (DiD) design with a control group of users or regions not exposed to the threshold change but subject to the same competitor sale and seasonality. Alternatively, consider synthetic control if no clean control exists.
Plot pre-intervention trends for treatment and control groups and test for parallel trends using event-study regressions. If trends diverge, consider matching or weighting to improve comparability.
Run placebo tests (e.g., fake treatment dates), check for spillovers, and test alternative model specifications (e.g., different time windows, covariates). Use negative control outcomes to detect unobserved confounding.
Estimate the effect with confidence intervals, discuss limitations, and triangulate with other methods (e.g., instrumental variables if available). Provide actionable insights for decision-making.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the revenue decline as a funnel problem, then prioritize experiments based on expected impact and ease of implementation. For each experiment, define clear success metrics tied to revenue and user behavior, and emphasize iterative testing to validate fixes.
Pro tip: Tie every experiment to a specific revenue lever (e.g., conversion, retention, pricing) and quantify the expected lift to show business acumen. Also, mention guardrail metrics to ensure fixes don't harm user experience or long-term value.
Break down revenue by user segments, product lines, and funnel stages to identify where the drop is concentrated. Use data to form hypotheses about root causes.
Rank potential experiments by expected impact on revenue, confidence in hypothesis, and ease of implementation (ICE framework). Focus on high-leverage areas like onboarding, pricing, or retention.
For each prioritized experiment, define the hypothesis, target audience, success metrics, and guardrail metrics. Ensure proper randomization and sample size for statistical power.
For each experiment, specify primary metrics (e.g., conversion rate, ARPU) and secondary metrics (e.g., engagement, retention). Include counter-metrics to monitor unintended consequences.
Analyze results, learn from failures, and double down on wins. Use a continuous experimentation loop to refine fixes and scale successful changes.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.