This part tripped me up more than I expected.
Start by defining clear proxy signals for off-platform purchase intent, such as saves, link clicks, and dwell time on product pages. Then, discuss methods to identify and mitigate biases in these proxies, and propose counterfactual analyses like holdout experiments or synthetic control to validate the discovery-to-purchase hypothesis.
Pro tip: Emphasize that proxies are noisy and biased, so triangulate multiple signals and use causal inference techniques to strengthen your argument. Show awareness of Instagram's business goals by linking proxies to potential metrics like incremental shopping tab engagement.
List in-app behaviors that indicate purchase intent, such as saves, shares, link clicks, product page views, and time spent on shopping-related content. Prioritize signals with high correlation to off-platform purchases.
Evaluate selection bias (e.g., only engaged users click links) and measurement bias (e.g., clicks may not lead to purchases). Propose techniques like propensity score weighting, stratification, or using negative controls to adjust for bias.
Suggest experiments like holdout groups (users who don't see the Shopping tab) or switchback tests to create counterfactuals. Alternatively, use observational methods like difference-in-differences or synthetic control if randomization is infeasible.
Define success metrics (e.g., lift in proxy signals, survey-based recall) and analyze whether the Shopping tab increases off-platform purchase intent. Use causal models to estimate incremental impact.
Recommend continuous monitoring and refinement of proxies based on new data, and suggest combining multiple proxies into a composite score for robustness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The math itself is straightforward once you set it up: transaction side is 50M times 0.1 times 0.2 times 45 times 0.05, ads side is 50M times 0.1 times 6 times 0.002.
Start by clearly separating the two revenue streams: transaction fees from purchases and ad impression fees. Compute each using the given assumptions, then perform a sensitivity analysis on the most impactful assumptions to show how the estimate changes. Finally, prioritize the assumptions that drive the most variance and suggest specific analyses to test them.
Pro tip: Always state your assumptions explicitly and round numbers for mental math, but note that in a real scenario you'd validate these with data. Also, consider that ad revenue might depend on the same user activity, so avoid double-counting.
Compute the number of weekly intents (50M DAU * 10% = 5M), then purchases (5M * 0.2 = 1M), then gross merchandise value (1M * $45 = $45M), and finally transaction revenue (45M * 5% = $2.25M).
Assume an ad impression fee per user per week (e.g., $0.10) and multiply by DAU to get weekly ad revenue (50M * $0.10 = $5M). If not given, state a reasonable assumption and note it as a key driver.
Compare the two revenue streams (transaction: $2.25M, ads: $5M). Identify which assumptions most affect the total: likely ad impression fee, take rate, and purchases per intentor (or AOV).
For each dominant assumption, vary it over a plausible range (e.g., ad fee from $0.05 to $0.20, take rate from 3% to 7%) and compute the resulting revenue to show the range of outcomes.
Conclude with which revenue stream is larger and more sensitive, and suggest data collection or experiments to reduce uncertainty in the top assumptions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was my favorite part and also where I probably talked too long.
Start by defining a metric tree that decomposes the purchase funnel into stages (impression → click → merchant visit → add-to-cart → checkout → purchase) and segment by user, merchant, and surface. Then systematically investigate each stage using data validation, funnel analysis, and experimentation to isolate the root cause, while evaluating cannibalization and incrementality. Finally, set clear rollback criteria based on the severity and nature of the issue.
Pro tip: Always validate data instrumentation first—many 'funnel drops' are actually logging errors. Also, use holdout groups or geo experiments to measure incrementality, as observational data can be misleading due to selection bias.
Decompose the purchase funnel into stages and define key metrics (CTR, conversion rate, AOV). Immediately check data quality: ensure events are logged correctly, no missing data, and metrics are computed consistently across platforms.
Analyze conversion rates at each stage, segmented by dimensions like device, user demographics, merchant category, and entry point. Identify where the largest drop occurs compared to forecast or baseline.
Determine if Shopping tab purchases are incremental or cannibalizing other surfaces (e.g., main feed, search). Use holdout experiments, geo tests, or difference-in-differences to measure the net impact.
Investigate user experience factors (load time, navigation, checkout flow) and supply-side factors (merchant quality, product availability, pricing). Use session recordings, surveys, and merchant data to diagnose.
Establish thresholds for rollback: e.g., if purchase conversion is >X% below forecast and root cause is not fixable quickly, or if cannibalization outweighs incrementality, or if data issues cannot be resolved.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.