Queries · q007 · Chief Executive Officer · week 1, night 3
“Which merchant verticals generate the bulk of Tidewick's processing volume and fee revenue today? (redo of night-1 report, which disagreed with the merchant-level total by ~1.76x)”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
The manager, at the start of week 1
Subject: A few questions to get me oriented before the board meeting
Hi Hannah,
Thanks for taking these on this week — I know slots are tight, so please prioritize in the order below and don't force everything into this week if it doesn't fit cleanly. I'd rather get three solid answers than five rushed ones.
What I need this for: I'm building my mental model of the business ahead of the board meeting at the end of January, and I want my first real conversation with the board to show I understand where we're strong, where we're exposed, and what I'm watching closely. Your Friday memo will be the backbone of that.
Let me know if any of these need narrowing once you've had a look at what's feasible — happy to discuss before you lock in the report requests.
Thanks, Maren
The analyst · week 1, night 3, request 1 of 3
The reconciliation confirms the correct answer: payouts.status only has two values, 'paid' (fully settled, paid_at always populated) and 'in_transit' (future-dated, paid_at always null). Filtering status='paid' with paid_at in [2025-01-01, 2026-01-01) gives fee revenue of €53,185.85 for the trailing 12 months — matching night 1's merchant-level report almost exactly. That means the merchant-concentration report (Report 1, night 1) was correct, and the vertical-level report (Report 3, night 1, €93.6k) has a bug, most likely a join fan-out against exchange_rates or tenants. I'm having that redone first since it's a wrong number already in front of me. I also want to note for Maren: I don't see any table in the catalog that represents Tidewick's own monthly platform-plan billing of merchants — billing/subscriptions/invoices tables are all tenant-scoped to the merchant's own business (their customers' subscriptions), not Tidewick-to-merchant billing. So priority #3 (platform fees vs. processing mix) likely can't be answered from this warehouse as a two-sided split; I'll reframe it around the processing take-rate trend, which is measurable, and flag the gap. Third slot starts priority #4 (vertical health) with a YoY growth view by vertical.
The engineer · 1 dry run on the empty schema, then the submission
WITH payout_eur AS ( SELECT p.tenant_id, p.gross_amount / er.rate AS gross_eur, p.fee_amount / er.rate AS fee_eur FROM payouts p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = date(p.paid_at) WHERE p.status = 'paid' AND p.paid_at >= DATE '2025-01-01' AND p.paid_at < DATE '2026-01-01' ), tenant_agg AS ( SELECT tenant_id, SUM(gross_eur) AS gross_eur, SUM(fee_eur) AS fee_eur FROM payout_eur GROUP BY tenant_id ) SELECT COALESCE(NULLIF(TRIM(t.industry), ''), 'Unspecified') AS vertical, COUNT(DISTINCT ta.tenant_id) AS merchant_count, ROUND(SUM(ta.gross_eur)::numeric,2) AS gross_payment_volume_eur, ROUND(SUM(ta.fee_eur)::numeric,2) AS fee_revenue_eur, ROUND((SUM(ta.fee_eur) / COUNT(DISTINCT ta.tenant_id))::numeric,2) AS avg_fee_revenue_per_merchant_eur, ROUND((100.0 * SUM(ta.fee_eur) / SUM(SUM(ta.fee_eur)) OVER ())::numeric,2) AS share_of_total_fee_revenue_pct FROM tenant_agg ta JOIN tenants t ON t.id = ta.tenant_id GROUP BY vertical ORDER BY fee_revenue_eur DESC, vertical ASC
WITH payout_eur AS ( SELECT p.tenant_id, p.gross_amount / er.rate AS gross_eur, p.fee_amount / er.rate AS fee_eur FROM payouts p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = date(p.paid_at) WHERE p.status = 'paid' AND p.paid_at >= DATE '2025-01-01' AND p.paid_at < DATE '2026-01-01'),tenant_agg AS ( SELECT tenant_id, SUM(gross_eur) AS gross_eur, SUM(fee_eur) AS fee_eur FROM payout_eur GROUP BY tenant_id)SELECT COALESCE(NULLIF(TRIM(t.industry), ''), 'Unspecified') AS vertical, COUNT(DISTINCT ta.tenant_id) AS merchant_count, ROUND(SUM(ta.gross_eur)::numeric,2) AS gross_payment_volume_eur, ROUND(SUM(ta.fee_eur)::numeric,2) AS fee_revenue_eur, ROUND((SUM(ta.fee_eur) / COUNT(DISTINCT ta.tenant_id))::numeric,2) AS avg_fee_revenue_per_merchant_eur, ROUND((100.0 * SUM(ta.fee_eur) / SUM(SUM(ta.fee_eur)) OVER ())::numeric,2) AS share_of_total_fee_revenue_pct ROUND(SUM(ta.gross_eur)::numeric, 2) AS gross_payment_volume_eur, ROUND(SUM(ta.fee_eur)::numeric, 2) AS fee_revenue_eur, ROUND((SUM(ta.fee_eur) / COUNT(DISTINCT ta.tenant_id))::numeric, 2) AS avg_fee_revenue_per_merchant_eur, ROUND((100.0 * SUM(ta.fee_eur) / SUM(SUM(ta.fee_eur)) OVER ())::numeric, 2) AS share_of_total_fee_revenue_pctFROM tenant_agg taJOIN tenants t ON t.id = ta.tenant_idGROUP BY verticalORDER BY fee_revenue_eur DESC, vertical ASC
“Submitted. Summary of what the query does:
- Converts each qualifying payout (status='paid', paid_at in 2025) to EUR individually by joining exchange_rates on base_code='EUR', quote_code=payouts.currency_code, as_of=date(paid_at) — before any aggregation, avoiding the prior fan-out bug.
- Aggregates gross/fee EUR per tenant_id first (CTE tenant_agg), then joins to tenants and groups by industry (nulls/blanks bucketed as 'Unspecified').
- Reports: vertical, merchant count, gross volume (EUR), fee revenue (EUR), average fee per merchant, and share of total fee revenue — ordered by fee revenue descending, tie-broken by vertical name.
One caveat flagged in the submit note: since dry-run never reads real data, I couldn't personally confirm the grand total lands at ~EUR 53,186 before handing it off — worth a quick cross-check once the report prints tonight.”
As Hannah Brooks saw it, from the small dataset the agents worked on
vertical | merchant_count | gross_payment_volume_eur | fee_revenue_eur | avg_fee_revenue_per_merchant_eur | share_of_total_fee_revenue_pct --------------------------+----------------+--------------------------+-----------------+----------------------------------+------------------------------- Apparel & Footwear | 12 | 1070404.44 | 29028.10 | 2419.01 | 54.58 Sports & Outdoor | 5 | 209893.74 | 5796.45 | 1159.29 | 10.90 Office Supplies (B2B) | 2 | 176549.46 | 4592.96 | 2296.48 | 8.64 Industrial Supplies (B2B) | 1 | 107753.57 | 3499.84 | 3499.84 | 6.58 Home & Living | 9 | 128877.56 | 3085.55 | 342.84 | 5.80 Food & Coffee | 5 | 77170.34 | 2597.96 | 519.59 | 4.89 Beauty | 8 | 125191.99 | 2346.81 | 293.35 | 4.41 Pet Supplies | 7 | 55346.78 | 1468.94 | 209.85 | 2.76 Consumer Electronics | 3 | 31713.49 | 529.66 | 176.55 | 1.00 Books & Stationery | 4 | 5565.70 | 145.03 | 36.26 | 0.27 Jewelry & Accessories | 1 | 2982.02 | 94.56 | 94.56 | 0.18 (11 rows)
The memo and the queries that followed; links found by title, so inferred
Later queries that build on this report:
It builds on:
Changed only where the benchmark needs the same result on every system
WITH payout_eur AS ( SELECT p.tenant_id, p.gross_amount / er.rate AS gross_eur, p.fee_amount / er.rate AS fee_eur SELECT p.tenant_id AS tenant_id, CAST(p.gross_amount / er.rate AS NUMERIC(18,6)) AS gross_eur, CAST(p.fee_amount / er.rate AS NUMERIC(18,6)) AS fee_eur FROM payouts p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = date(p.paid_at) ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = CAST(p.paid_at AS DATE) WHERE p.status = 'paid' AND p.paid_at >= DATE '2025-01-01' AND p.paid_at < DATE '2026-01-01'),tenant_agg AS ( SELECT tenant_id, SUM(gross_eur) AS gross_eur, SUM(fee_eur) AS fee_eur FROM payout_eur GROUP BY tenant_id)SELECT COALESCE(NULLIF(TRIM(t.industry), ''), 'Unspecified') AS vertical, COUNT(DISTINCT ta.tenant_id) AS merchant_count, ROUND(SUM(ta.gross_eur)::numeric, 2) AS gross_payment_volume_eur, ROUND(SUM(ta.fee_eur)::numeric, 2) AS fee_revenue_eur, ROUND((SUM(ta.fee_eur) / COUNT(DISTINCT ta.tenant_id))::numeric, 2) AS avg_fee_revenue_per_merchant_eur, ROUND((100.0 * SUM(ta.fee_eur) / SUM(SUM(ta.fee_eur)) OVER ())::numeric, 2) AS share_of_total_fee_revenue_pct SUM(ta.gross_eur) AS gross_payment_volume_eur, SUM(ta.fee_eur) AS fee_revenue_eur, (SUM(ta.fee_eur) / COUNT(DISTINCT ta.tenant_id)) AS avg_fee_revenue_per_merchant_eur, (100.0 * SUM(ta.fee_eur) / SUM(SUM(ta.fee_eur)) OVER ()) AS share_of_total_fee_revenue_pctFROM tenant_agg taJOIN tenants t ON t.id = ta.tenant_idGROUP BY verticalORDER BY fee_revenue_eur DESC, vertical ASCORDER BY fee_revenue_eur DESC NULLS FIRST, vertical ASC NULLS LAST
WITH payout_eur AS ( SELECT p.tenant_id AS tenant_id, CAST(p.gross_amount / er.rate AS NUMERIC(18,6)) AS gross_eur, CAST(p.fee_amount / er.rate AS NUMERIC(18,6)) AS fee_eur FROM payouts p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = CAST(p.paid_at AS DATE) WHERE p.status = 'paid' AND p.paid_at >= DATE '2025-01-01' AND p.paid_at < DATE '2026-01-01' ), tenant_agg AS ( SELECT tenant_id, SUM(gross_eur) AS gross_eur, SUM(fee_eur) AS fee_eur FROM payout_eur GROUP BY tenant_id ) SELECT COALESCE(NULLIF(TRIM(t.industry), ''), 'Unspecified') AS vertical, COUNT(DISTINCT ta.tenant_id) AS merchant_count, SUM(ta.gross_eur) AS gross_payment_volume_eur, SUM(ta.fee_eur) AS fee_revenue_eur, (SUM(ta.fee_eur) / COUNT(DISTINCT ta.tenant_id)) AS avg_fee_revenue_per_merchant_eur, (100.0 * SUM(ta.fee_eur) / SUM(SUM(ta.fee_eur)) OVER ()) AS share_of_total_fee_revenue_pct FROM tenant_agg ta JOIN tenants t ON t.id = ta.tenant_id GROUP BY vertical ORDER BY fee_revenue_eur DESC NULLS FIRST, vertical ASC NULLS LAST
The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted
Report request from Hannah Brooks (analyst for the Chief Executive Officer): Title: Revenue by vertical, trailing 12 months (corrected) Question: Which merchant verticals generate the bulk of Tidewick's processing volume and fee revenue today? (redo of night-1 report, which disagreed with the merchant-level total by ~1.76x) Grain: one row per merchant vertical (tenants.industry) Columns: - vertical (industry) - merchant count (distinct tenants with a qualifying payout) - gross payment volume (EUR) - fee revenue (EUR) - average fee revenue per merchant (EUR) - share of total fee revenue (%) Details: Source: payouts only, filtered to status='paid' and paid_at >= 2025-01-01 and paid_at < 2026-01-01 (matches the window validated in last night's reconciliation report, which found total fee revenue of EUR 53,185.85 for this exact filter). To avoid the fan-out bug suspected in the prior version: first aggregate payouts per tenant_id (sum gross_amount and fee_amount, converting each payout row to EUR individually via exchange_rates where base_code='EUR', quote_code=payouts.currency_code, as_of = date(payouts.paid_at) — this should be a 1-row match per payout given the (base_code,quote_code,as_of) unique constraint), and only THEN join that per-tenant aggregate to tenants to pick up industry and group by industry. Please confirm in the report notes that the grand total fee revenue across all vertical rows sums to approximately EUR 53,186 (if it doesn't, something is still wrong and should be flagged rather than printed). Group any tenant with null/blank industry into a single 'Unspecified' row. Order by fee revenue EUR descending, tie-break by vertical name ascending. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH payout_eur AS ( SELECT p.tenant_id, p.gross_amount / er.rate AS gross_eur, p.fee_amount / er.rate AS fee_eur FROM payouts p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = date(p.paid_at) WHERE p.status = 'paid' AND p.paid_at >= DATE '2025-01-01' AND p.paid_at < DATE '2026-01-01' ), tenant_agg AS ( SELECT tenant_id, SUM(gross_eur) AS gross_eur, SUM(fee_eur) AS fee_eur FROM payout_eur GROUP BY tenant_id ) SELECT COALESCE(NULLIF(TRIM(t.industry), ''), 'Unspecified') AS vertical, COUNT(DISTINCT ta.tenant_id) AS merchant_count, ROUND(SUM(ta.gross_eur)::numeric,2) AS gross_payment_volume_eur, ROUND(SUM(ta.fee_eur)::numeric,2) AS fee_revenue_eur, ROUND((SUM(ta.fee_eur) / COUNT(DISTINCT ta.tenant_id))::numeric,2) AS avg_fee_revenue_per_merchant_eur, ROUND((100.0 * SUM(ta.fee_eur) / SUM(SUM(ta.fee_eur)) OVER ())::numeric,2) AS share_of_total_fee_revenue_pct FROM tenant_agg ta JOIN tenants t ON t.id = ta.tenant_id GROUP BY vertical ORDER BY fee_revenue_eur DESC, vertical ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: vertical, merchant_count, gross_payment_volume_eur, fee_revenue_eur, avg_fee_revenue_per_merchant_eur, share_of_total_fee_revenue_pct
submitWITH payout_eur AS ( SELECT p.tenant_id, p.gross_amount / er.rate AS gross_eur, p.fee_amount / er.rate AS fee_eur FROM payouts p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = date(p.paid_at) WHERE p.status = 'paid' AND p.paid_at >= DATE '2025-01-01' AND p.paid_at < DATE '2026-01-01' ), tenant_agg AS ( SELECT tenant_id, SUM(gross_eur) AS gross_eur, SUM(fee_eur) AS fee_eur FROM payout_eur GROUP BY tenant_id ) SELECT COALESCE(NULLIF(TRIM(t.industry), ''), 'Unspecified') AS vertical, COUNT(DISTINCT ta.tenant_id) AS merchant_count, ROUND(SUM(ta.gross_eur)::numeric, 2) AS gross_payment_volume_eur, ROUND(SUM(ta.fee_eur)::numeric, 2) AS fee_revenue_eur, ROUND((SUM(ta.fee_eur) / COUNT(DISTINCT ta.tenant_id))::numeric, 2) AS avg_fee_revenue_per_merchant_eur, ROUND((100.0 * SUM(ta.fee_eur) / SUM(SUM(ta.fee_eur)) OVER ())::numeric, 2) AS share_of_total_fee_revenue_pct FROM tenant_agg ta JOIN tenants t ON t.id = ta.tenant_id GROUP BY vertical ORDER BY fee_revenue_eur DESC, vertical ASC
Submitted to tonight's batch. Your work on this request is done.