The denominator scoping is where people slip up.
Break the problem into two aggregation steps: first count the number of touches per company per month, then average those counts across only the companies that appear in each month. Emphasize that the denominator is the count of distinct active companies in that month, not the total company universe, which is the key nuance of this question.
Pro tip: Explicitly call out the denominator distinction upfront — interviewers at analytics-focused companies like Upstart use this type of question to test whether you read requirements carefully and avoid the common mistake of dividing by the total number of companies in the dataset.
Confirm the table structure (e.g., columns like company_id, touch_date, touch_id) and restate the key constraint: the denominator is only companies with at least one touch in that specific month, not all companies ever recorded.
Truncate or format the touch_date to a year-month grain (e.g., DATE_TRUNC('month', touch_date) in SQL) so all touches can be grouped by calendar month.
Group by (month, company_id) and COUNT the number of touches to produce a row for each company-month combination with its touch count — this naturally filters to only active companies.
Wrap the previous result in an outer query that groups by month and takes AVG(touch_count); since every company in this intermediate table had at least one touch, the denominator is automatically correct.
Verify the output by spot-checking a single month manually, and consider edge cases such as months with only one active company or ties in touch counts to ensure the logic holds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Approach this as a last-touch attribution problem by joining a conversions table to a marketing touches table using a non-equi join (touch timestamp <= conversion timestamp), then use a window function like ROW_NUMBER() or LAST_VALUE() partitioned by company to isolate the most recent touch before conversion. Finally, compute the same-month flag using date truncation or EXTRACT functions on both timestamps, and filter out companies with no prior touches using an INNER JOIN or EXISTS clause.
Pro tip: Mention that in production you'd also want to handle edge cases like ties (two touches at the exact same timestamp) by adding a secondary sort key such as touch_id, and consider indexing strategies on the timestamp columns to avoid a full cross-join scan on large datasets — this signals you think beyond just correctness to performance and reliability.
Start by clarifying the schema — a conversions table (company, conversion_timestamp) and a marketing_touches table (company, touch_timestamp, channel, campaign). Perform a JOIN between them on company where touch_timestamp <= conversion_timestamp to get all eligible touches per conversion.
Apply ROW_NUMBER() OVER (PARTITION BY company ORDER BY touch_timestamp DESC) to rank all qualifying touches per company, so rank = 1 identifies the most recent touch at or before conversion. Alternatively, use a correlated subquery with MAX(touch_timestamp) for the same result.
Wrap the ranked query in a CTE or subquery and filter WHERE row_num = 1 to retain only the last touch record per company, ensuring one output row per converted company.
Add a boolean/integer flag column using DATE_TRUNC('month', conversion_timestamp) = DATE_TRUNC('month', touch_timestamp) — or equivalent EXTRACT(YEAR/MONTH) comparisons — to indicate whether both events fell in the same calendar month.
Because the JOIN is on touch_timestamp <= conversion_timestamp, companies with zero qualifying touches will naturally be excluded by an INNER JOIN; explicitly call this out to show awareness that LEFT JOIN would incorrectly include them with NULL touch data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty much a wrapper around the previous query.
Start by clarifying the schema (e.g., a touches/events table with company_id, touch_timestamp, conversion_timestamp) and then write a SQL query that extracts the calendar month from both the last-touch date and the conversion date, comparing them for equality. For the follow-up, add a DATEDIFF condition (≤ 45 days) alongside the same-month filter, and use COUNT(DISTINCT company_id) in both cases to avoid double-counting.
Pro tip: Explicitly call out edge cases such as companies with multiple conversions or multiple last touches — clarify whether 'last touch' means the most recent touch before conversion per company, and whether you need to handle NULL conversion dates — this signals production-level SQL thinking that interviewers at data-driven fintechs like Upstart reward.
Ask about the table structure (columns like company_id, touch_date, conversion_date) and confirm what 'last touch' means — the maximum touch timestamp per company, or the touch immediately preceding conversion. Also confirm whether a company can have multiple conversions.
Use a subquery or CTE with MAX(touch_date) grouped by company_id (and optionally by conversion_id) to isolate the single last-touch record for each company before writing the main filter logic.
Compare the calendar month and year of the last touch and conversion dates using DATE_TRUNC('month', last_touch_date) = DATE_TRUNC('month', conversion_date) (or YEAR/MONTH extraction), then wrap with COUNT(DISTINCT company_id).
Extend the WHERE clause with DATEDIFF(day, last_touch_date, conversion_date) BETWEEN 0 AND 45 (or ABS version if direction is ambiguous), keeping the same-month condition, and re-run COUNT(DISTINCT company_id) for the stricter result.
Briefly discuss how you would validate the output — for example, checking that the 45-day version always returns a count ≤ the same-month-only version — and mention what the metric could mean for Upstart's attribution or funnel analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.