This is the part I felt least confident about.
Start by mapping the full user journey from loan comparison to on-site application and click-out partner application, then define a consistent event taxonomy and identity resolution strategy. Address cookie unreliability by combining server-side events, first-party identifiers, and probabilistic stitching, and explain how you'd validate the instrumentation end-to-end.
Pro tip: Emphasize that you'd design the measurement plan around the business decision it informs (e.g., optimizing conversion or partner routing), not just tracking everything. Also, mention that you'd instrument a 'source of truth' event for each critical step and use it to reconcile discrepancies across domains.
Outline the key steps: loan comparison page view, rate/term selection, click to on-site apply, on-site application steps, click-out to partner, and partner application completion. Define the primary conversion metrics and funnel stages you need to measure.
Propose a consistent naming schema (e.g., object_action, snake_case) for events like loan_comparison_viewed, application_started, application_submitted, and partner_redirect. Include required properties such as loan_type, amount, partner_id, and timestamp.
Specify the identifiers to capture: user_id (if logged in), anonymous_id (first-party cookie), session_id, and a cross-domain tracking ID passed via URL parameters. Explain how to join events across domains using these keys and a server-side user mapping table.
Describe passing a unique tracking token (e.g., via query string or postMessage) when redirecting to partner sites, and using server-side APIs to receive events from partners. For cookie unreliability, propose server-side event collection, fingerprinting, and probabilistic matching based on device, IP, and timing.
Outline a QA plan: unit tests for event firing, end-to-end tests with synthetic users, and dashboards to monitor event volumes and funnel drop-offs. Include reconciliation between client-side and server-side data to detect gaps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the comparison page as a marketplace funnel and each partner as a supplier within it, then define metrics at both levels with explicit numerators and denominators. Distinguish success metrics (what you want to maximize) from guardrails (what you must not break), and tie each metric to a specific business decision. Use precise definitions to avoid ambiguity and ensure alignment across teams.
Pro tip: Always specify the unit of analysis (session, user, application) and the time window for each metric, and call out where denominators can shift due to eligibility or attribution rules. This prevents metric drift and makes your definitions audit-ready.
Outline the end-to-end user journey on the comparison page and for each partner: impression → click → on-site start → completion → qualified application → funded loan. This ensures every metric maps to a stage and clarifies where numerators and denominators come from.
For each stage, specify the metric formula. For example: CTR = clicks / impressions; on-site completion rate = completed applications / started applications; qualified rate = qualified applications / completed applications; funded loan rate = funded loans / qualified applications; revenue per session = total revenue / sessions. State the unit (session, user, application) and any filters (e.g., eligible partners only).
Identify guardrails such as user experience (e.g., page load time, error rate), partner fairness (e.g., distribution of impressions), and risk (e.g., default rate, fraud rate). Define them with the same rigor: numerator, denominator, and threshold for alerting.
Explain how to aggregate partner-level metrics to the comparison page level (e.g., weighted average by traffic) and how to compare partners (e.g., cohort-based, controlling for traffic source). Clarify whether metrics are computed per partner or across all partners, and how to handle partners with low volume.
Connect each metric to a business objective (e.g., revenue, conversion, risk) and describe how they would be used in A/B tests or partner evaluations. Mention the importance of statistical significance and minimum sample sizes when comparing partners or page variants.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by framing the problem as a two-sided marketplace where visible listing differences act as selection mechanisms, influencing which borrowers apply and which partners fund. Then, hypothesize how specific differences (APR ranges, fees, eligibility messaging) might attract different risk profiles and loan sizes, and connect those shifts to downstream funding rates through adverse selection or improved matching.
Pro tip: Quantify the trade-off: a wider APR range may attract more subprime borrowers, increasing funding costs, but could also improve conversion if priced correctly. Always tie insights back to measurable metrics like approval rate, average loan size, and funding cost per loan.
Identify how each listing attribute (APR range, fees, eligibility messaging) signals value to different borrower segments (e.g., prime vs. subprime, small vs. large loans). Consider how these signals affect self-selection.
Predict how changes in these attributes might alter the composition of applicants—e.g., a lower APR floor could attract more prime borrowers, while vague eligibility messaging might increase applications from unqualified borrowers.
Explain how shifts in borrower risk profiles and loan characteristics affect the cost of funds for partners, considering factors like default risk, prepayment risk, and operational costs.
Derive three insights that are not immediately apparent, such as second-order effects (e.g., fee structure influencing loan duration) or interactions between attributes (e.g., APR range and eligibility messaging together creating a niche appeal).
Propose how to test these insights using A/B tests or observational data, focusing on metrics like conversion rate, average APR, default rate, and partner funding costs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with session as the unit, randomized at page load with stratification on credit tier and loan purpose.
Start by clarifying the business goal and defining the experimental unit as the user, with randomization at the user level to avoid contamination. Then outline the design: stratification by key covariates, sample size calculation with power analysis, and a pre-registered decision rule. Finally, address selection bias and attribution mismatches by proposing methods like intent-to-treat analysis and sensitivity checks.
Pro tip: Emphasize that you would pre-register the analysis plan and decision rule to prevent p-hacking, and discuss how you'd handle partial compliance using instrumental variables or CACE analysis. This shows rigor and practical experience.
Confirm the primary metric (e.g., application completion rate) and guardrail metrics (e.g., approval rate, fraud). Define the experimental unit as the user and the treatment as the entry point (on-site vs. click-out).
Randomize users to treatment or control, stratify by relevant covariates (e.g., partner, device, credit score band), and calculate sample size using power analysis with assumptions on baseline rate, minimum detectable effect, alpha, and power.
Specify the primary analysis (e.g., intent-to-treat), the decision threshold (e.g., statistically significant improvement in primary metric with no degradation in guardrails), and the stopping rule.
Use intent-to-treat to handle non-compliance, and consider CACE analysis to estimate treatment effect among compliers. For attribution mismatches, define a clear attribution window and use methods like last-touch or multi-touch attribution, and run sensitivity analyses.
Monitor for sample ratio mismatch, novelty effects, and ensure data quality. Analyze results using the pre-registered plan, and conduct subgroup analyses as pre-specified.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tab hoarding is such a specific thing to call out and I was not ready for it.
Start by clarifying the business context and defining cannibalization and contamination precisely, then propose a multi-layered measurement framework that combines experimental designs (e.g., geo holdouts, switchback tests) with causal inference methods to isolate incremental effects. Address the specific biases (revisits, tab hoarding, last-click) by incorporating user-level deduplication, multi-touch attribution, and session stitching, and validate with sensitivity analyses.
Pro tip: Emphasize that perfect measurement is impossible; instead, focus on bounding the bias and triangulating across methods to inform decisions. Mention that you'd align with stakeholders on the acceptable trade-off between precision and actionability.
Clarify what 'partner cannibalization' and 'cross-listing traffic contamination' mean in Upstart's context, including which partners, products, and user journeys are involved. Align with stakeholders on the decision the measurement will inform.
Propose randomized controlled experiments (e.g., user-level randomization, geo-based holdouts, or switchback tests) to measure incremental impact. Ensure proper power analysis and account for spillover effects.
Use user IDs, device graphs, and session stitching to deduplicate revisits and tab hoarding. Define a 'true visit' window and apply heuristics to filter out non-intentional traffic.
Move beyond last-click by using data-driven attribution (e.g., Markov chains, Shapley values) or causal models (e.g., instrumental variables, difference-in-differences) to estimate true incremental contributions.
Run sensitivity analyses to test assumptions, compare experimental and observational estimates, and set up ongoing monitoring dashboards to detect contamination or cannibalization over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.