I went with 14-day post-contact purchase conversion using only first contacts, which felt right.
Start by clarifying the campaign's objective and the business context (e.g., Stripe's B2B payments focus). Then propose a primary metric that is clearly attributable to the campaign, measured over a fixed time window, and resistant to short-term activity spikes (e.g., using a 7-day average or a cumulative measure). Finally, define two guardrail metrics with precise formulas to ensure the campaign doesn't harm other key areas.
Pro tip: Choose a primary metric that is a ratio (e.g., conversion rate) rather than an absolute count, as ratios are naturally robust to activity spikes. Also, explicitly state the attribution window (e.g., 14-day click-through) to avoid ambiguity.
Ask clarifying questions to understand the campaign's goal (e.g., lead generation, sign-ups, revenue) and the target audience. This ensures the metric aligns with business priorities.
Propose a metric that is attributable (e.g., incremental sign-ups from the campaign), time-bounded (e.g., within 30 days of campaign exposure), and robust to spikes (e.g., use a 7-day rolling average or a ratio like conversion rate). Write the precise formula.
Identify two metrics that could be negatively impacted by the campaign (e.g., customer acquisition cost, churn rate). Write their formulas and explain why they are important to monitor.
Describe how the primary metric mitigates the effect of short-term spikes (e.g., by using a smoothed average or a ratio that normalizes for traffic volume).
Recap the primary and guardrail metrics, and suggest how to validate them (e.g., through A/B testing or holdout groups).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The first-contact filter using ROW_NUMBER was straightforward.
Start by identifying the first contact per user using a window function, then join to purchases and filter out purchases before contact time. Aggregate purchases within 14 days post-contact to compute conversion rate and average revenue per contacted user, grouped by contact week and country.
Pro tip: Clarify the definition of 'contact week' (e.g., ISO week of first contact) and ensure you handle users with no purchases correctly by using LEFT JOINs and COALESCE. Also, consider time zones if contact and purchase timestamps are in different zones.
Use a window function like ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY contact_time) to select the earliest contact for each user.
Left join the first contacts to the purchases table on user_id, ensuring purchases occur after the contact time.
Apply a condition that purchase_time is between contact_time and contact_time + INTERVAL '14 days' to only include relevant purchases.
Group by the week of contact (e.g., DATE_TRUNC('week', contact_time)) and country, then compute conversion rate as COUNT(DISTINCT user_id with purchase) / COUNT(DISTINCT user_id) and average revenue as SUM(revenue) / COUNT(DISTINCT user_id).
Use COALESCE to avoid division by zero, and ensure the final output includes contact_week, country, conversion_rate, and avg_revenue_per_user.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part I was least prepared for.
Use a window function like ROW_NUMBER() to assign a rank to non-contacted users within each signup_week and country group, ordered by user_id, then join each contacted user to the non-contacted user with rank 1. After matching, compute the 14-day purchase rate for the matched group by checking if a purchase occurred within 14 days of signup, and aggregate the rate.
Pro tip: Clarify the definition of '14-day purchase rate'—whether it's measured from signup date or contact date—and ensure the matching is done without replacement to avoid bias. Also, mention that you'd check for balance in covariates after matching to validate the baseline.
Filter the user table to separate users who were contacted (e.g., received a marketing email) from those who were not, ensuring you have the necessary signup_week and country fields.
Use ROW_NUMBER() OVER (PARTITION BY signup_week, country ORDER BY user_id) to assign a unique rank to each non-contacted user within each signup_week and country combination.
Join the contacted users to the ranked non-contacted users on signup_week and country, selecting only the non-contacted user with rank = 1 for each contacted user.
For the matched pairs, determine if each user made a purchase within 14 days of signup (or contact), then calculate the purchase rate as the proportion of users who purchased.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the metric definition and time windows, then structure the SQL to compute the drop by device and product_id. Use a CTE to calculate the prior 7-day average and the 14-day conversion for each slice, then compute the delta and rank slices by their approximate contribution to the overall drop.
Pro tip: Always validate the metric definition and time zones with stakeholders before writing SQL; a 20% drop might be due to data pipeline issues or seasonality rather than a real product change.
Confirm what 'contacted-user conversion' means (e.g., contacted users who converted within 14 days) and the exact date of the drop. Establish the prior 7-day period for comparison.
Write a subquery to calculate the number of contacted users and conversions for each slice on the specific date and the prior 7-day average. Ensure you handle any missing data appropriately.
Calculate the 14-day conversion rate for each slice on the drop date and the prior period. Compute the delta as the difference between the two rates.
For each slice, approximate its contribution to the overall drop by multiplying the delta by the slice's share of contacted users in the prior period. Rank slices by this contribution.
Select the top 3 slices by approximate contribution and present the count contacted, 14-day conversion, and delta for each.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer question but I overthought it.
Start by acknowledging the bias: users who unsubscribe immediately after contact are likely reacting negatively to the outreach, and their exclusion from metrics can inflate performance. Propose a check that compares metrics with and without these users, and consider treating unsubscribes as a negative signal rather than missing data. Emphasize that the right approach depends on the metric's purpose—whether it's measuring engagement or overall campaign impact.
Pro tip: Frame unsubscribes as a form of negative feedback that should be incorporated into the metric, not just excluded. Suggest a sensitivity analysis to quantify the bias and recommend a guardrail metric to monitor unsubscribe rates alongside primary metrics.
Explain that users who unsubscribe immediately are likely dissatisfied, and excluding them from metrics like click-through or conversion rates can overstate success. Quantify the potential bias by comparing metrics with and without these users.
Decide whether to include unsubscribes as a negative outcome (e.g., assign a value of 0 for engagement) or to analyze them separately. Consider the metric's goal: if measuring overall campaign health, unsubscribes should count against it.
Calculate the primary metric both including and excluding immediate unsubscribers. If the difference is large, the metric is biased; report both versions or adjust the metric definition.
Track unsubscribe rate as a guardrail to ensure that improvements in the primary metric don't come at the cost of user experience. Set thresholds for acceptable unsubscribe rates.
Present findings to stakeholders, recommending metric adjustments or further analysis. Continuously monitor for similar biases in other metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.