Queries · q021 · Chief Financial Officer · week 1, night 3
“Within the outlier Apparel & Footwear vertical, is the elevated refund rate structural/stable or getting worse over time, and how does it compare to the company average each quarter?”
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: Finding my bearings – first set of questions for the week
Hi Kofi,
Good to be working with you. I'm still building my mental map of the business, so I'd rather ask a few broad questions well than a lot of narrow ones badly. Given the slot budget, here's what I'd prioritize this week, roughly in order:
For this week, let's focus on #1 and #2 — those are the two I most need before I can speak credibly about 2025 and the 2026 budget. If there's room, #3 would be next; #4 and #5 can wait for a follow-up week if slots are tight.
Let me know if any of these need me to narrow scope further before you brief the data engineers.
Thanks, Tobias
The analyst · week 1, night 3, request 3 of 3
The currency reconciliation report is the key finding tonight: payout_count_missing_fx_rate is 0 for every currency, so FX coverage is complete — my "inner join dropping unmatched currencies" hypothesis was wrong. But summing the reconciliation report's own gross_volume_eur across currencies gives €3,972,988 and fee_revenue_eur gives €93,583 — both match Night 1's Report 3 total almost to the cent. That confirms the TRUE 2025 totals, and proves Night 1's Report 1 (monthly trend: €1.99M gross / €53.2k fee) was wrong by a different mechanism than I guessed — not missing FX matches, but something in how that specific query aggregated/converted by month (gross was off by ~2.0x, fee only by ~1.76x, so it's not a uniform "half the rows missing" bug either, more likely a currency-conversion-direction or grouping error localized to that query). Rather than guess further, I'm asking for the monthly report to be rebuilt using the exact validated per-payout conversion method from tonight's reconciliation report, with an explicit annual TOTAL row so it self-checks against the €3,972,988 / €93,583 figures I now trust. This is the top priority tonight since it underpins the board-facing 2025 revenue narrative.
On refunds: Apparel & Footwear stands out hard at ~20.8% refund rate, 3-6x every other vertical, and it's broad-based across apparel merchants (not just one outlier per Report 3), so I don't think it's a data artifact — apparel/fashion e-commerce genuinely runs high return rates. Still, since this affects net revenue and cash payouts (Tobias's framing), I want to know if it's stable or worsening, especially given the Nov/Dec volume spike we saw in gross volume. I'm using my remaining two slots for a company-wide quarterly refund-rate trend and an Apparel-specific quarterly trend, both 2024 vs 2025, to see direction of travel before I write the memo.
The engineer · 1 dry run on the empty schema, then the submission
WITH quarters AS ( SELECT DISTINCT year_num, quarter_num FROM calendar_days WHERE year_num IN (2024, 2025) ), exch AS ( SELECT quote_code, as_of, rate FROM exchange_rates WHERE base_code = 'EUR' ), payment_eur AS ( SELECT p.id AS payment_id, p.tenant_id, t.industry, cd.year_num, cd.quarter_num, p.amount / er.rate AS amount_eur, p.amount_refunded / er.rate AS amount_refunded_eur FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS date) JOIN exch er ON er.quote_code = p.currency_code AND er.as_of = cd.calendar_date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= TIMESTAMP '2024-01-01' AND p.captured_at < TIMESTAMP '2026-01-01' ), apparel_agg AS ( SELECT year_num, quarter_num, COUNT(DISTINCT tenant_id) AS merchant_count, COUNT(*) AS payment_count, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur WHERE industry = 'Apparel & Footwear' GROUP BY year_num, quarter_num ), company_agg AS ( SELECT year_num, quarter_num, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur GROUP BY year_num, quarter_num ) SELECT q.year_num || '-Q' || q.quarter_num AS quarter, COALESCE(a.merchant_count, 0) AS merchant_count, COALESCE(a.payment_count, 0) AS payment_count, ROUND(COALESCE(a.gross_captured_eur,0),2) AS gross_captured_eur, ROUND(COALESCE(a.amount_refunded_eur,0),2) AS amount_refunded_eur, ROUND(CASE WHEN COALESCE(a.gross_captured_eur,0) = 0 THEN NULL ELSE a.amount_refunded_eur / a.gross_captured_eur * 100 END, 2) AS refund_rate_pct, ROUND(CASE WHEN COALESCE(c.gross_captured_eur,0) = 0 THEN NULL ELSE c.amount_refunded_eur / c.gross_captured_eur * 100 END, 2) AS company_avg_refund_rate_pct FROM quarters q LEFT JOIN apparel_agg a ON a.year_num = q.year_num AND a.quarter_num = q.quarter_num LEFT JOIN company_agg c ON c.year_num = q.year_num AND c.quarter_num = q.quarter_num ORDER BY q.year_num, q.quarter_num
“Query compiles and returns the expected 7 columns at quarterly grain. Submitting.”
“Submitted. The report builds an 8-row quarterly series (2024-Q1 to 2025-Q2025-Q4) with Apparel & Footwear's merchant count, payment count, gross captured, refunds, and refund rate, alongside the volume-weighted company-wide refund rate for the same quarter — letting Kofi see directly whether the apparel gap is widening, narrowing, or flat.”
As Kofi Mensah saw it, from the small dataset the agents worked on
quarter | merchant_count | payment_count | gross_captured_eur | amount_refunded_eur | refund_rate_pct | company_avg_refund_rate_pct --------+----------------+---------------+--------------------+---------------------+-----------------+---------------------------- 2024-Q1 | 7 | 894 | 197075.62 | 41567.9 | 21.09 | 10.58 2024-Q2 | 8 | 922 | 181654.82 | 41182.07 | 22.67 | 10.48 2024-Q3 | 9 | 988 | 168971.48 | 35312.27 | 20.9 | 8.4 2024-Q4 | 9 | 1975 | 386950.72 | 99873.88 | 25.81 | 14.13 2025-Q1 | 11 | 1150 | 216621.44 | 44831.18 | 20.7 | 7.77 2025-Q2 | 11 | 1169 | 198881.22 | 44578.08 | 22.41 | 10.66 2025-Q3 | 11 | 1212 | 192047.6 | 42818.11 | 22.3 | 13.45 2025-Q4 | 11 | 2298 | 470544.84 | 91672.62 | 19.48 | 11.92 (8 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 quarters AS ( SELECT DISTINCT year_num, quarter_num FROM calendar_days WHERE year_num IN (2024, 2025)),exch AS ( SELECT quote_code, as_of, rate FROM exchange_rates WHERE base_code = 'EUR'),payment_eur AS ( SELECT p.id AS payment_id, p.tenant_id, t.industry, cd.year_num, cd.quarter_num, p.amount / er.rate AS amount_eur, p.amount_refunded / er.rate AS amount_refunded_eur p.tenant_id AS tenant_id, t.industry AS industry, cd.year_num AS year_num, cd.quarter_num AS quarter_num, CAST(p.amount / er.rate AS NUMERIC(18,6)) AS amount_eur, CAST(p.amount_refunded / er.rate AS NUMERIC(18,6)) AS amount_refunded_eur FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS date) JOIN exch er ON er.quote_code = p.currency_code AND er.as_of = cd.calendar_date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= TIMESTAMP '2024-01-01' AND p.captured_at < TIMESTAMP '2026-01-01'),apparel_agg AS ( SELECT year_num, quarter_num, COUNT(DISTINCT tenant_id) AS merchant_count, COUNT(*) AS payment_count, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur WHERE industry = 'Apparel & Footwear' GROUP BY year_num, quarter_num),company_agg AS ( SELECT year_num, quarter_num, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur GROUP BY year_num, quarter_num)SELECT q.year_num || '-Q' || q.quarter_num AS quarter, COALESCE(a.merchant_count, 0) AS merchant_count, COALESCE(a.payment_count, 0) AS payment_count, ROUND(COALESCE(a.gross_captured_eur,0),2) AS gross_captured_eur, ROUND(COALESCE(a.amount_refunded_eur,0),2) AS amount_refunded_eur, ROUND(CASE WHEN COALESCE(a.gross_captured_eur,0) = 0 THEN NULL ELSE a.amount_refunded_eur / a.gross_captured_eur * 100 END, 2) AS refund_rate_pct, ROUND(CASE WHEN COALESCE(c.gross_captured_eur,0) = 0 THEN NULL ELSE c.amount_refunded_eur / c.gross_captured_eur * 100 END, 2) AS company_avg_refund_rate_pct COALESCE(a.gross_captured_eur,0) AS gross_captured_eur, COALESCE(a.amount_refunded_eur,0) AS amount_refunded_eur, CASE WHEN COALESCE(a.gross_captured_eur,0) = 0 THEN NULL ELSE a.amount_refunded_eur / a.gross_captured_eur * 100 END AS refund_rate_pct, CASE WHEN COALESCE(c.gross_captured_eur,0) = 0 THEN NULL ELSE c.amount_refunded_eur / c.gross_captured_eur * 100 END AS company_avg_refund_rate_pctFROM quarters qLEFT JOIN apparel_agg a ON a.year_num = q.year_num AND a.quarter_num = q.quarter_numLEFT JOIN company_agg c ON c.year_num = q.year_num AND c.quarter_num = q.quarter_numORDER BY q.year_num, q.quarter_numORDER BY q.year_num NULLS LAST, q.quarter_num NULLS LAST
WITH quarters AS ( SELECT DISTINCT year_num, quarter_num FROM calendar_days WHERE year_num IN (2024, 2025) ), exch AS ( SELECT quote_code, as_of, rate FROM exchange_rates WHERE base_code = 'EUR' ), payment_eur AS ( SELECT p.id AS payment_id, p.tenant_id AS tenant_id, t.industry AS industry, cd.year_num AS year_num, cd.quarter_num AS quarter_num, CAST(p.amount / er.rate AS NUMERIC(18,6)) AS amount_eur, CAST(p.amount_refunded / er.rate AS NUMERIC(18,6)) AS amount_refunded_eur FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS date) JOIN exch er ON er.quote_code = p.currency_code AND er.as_of = cd.calendar_date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= TIMESTAMP '2024-01-01' AND p.captured_at < TIMESTAMP '2026-01-01' ), apparel_agg AS ( SELECT year_num, quarter_num, COUNT(DISTINCT tenant_id) AS merchant_count, COUNT(*) AS payment_count, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur WHERE industry = 'Apparel & Footwear' GROUP BY year_num, quarter_num ), company_agg AS ( SELECT year_num, quarter_num, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur GROUP BY year_num, quarter_num ) SELECT q.year_num || '-Q' || q.quarter_num AS quarter, COALESCE(a.merchant_count, 0) AS merchant_count, COALESCE(a.payment_count, 0) AS payment_count, COALESCE(a.gross_captured_eur,0) AS gross_captured_eur, COALESCE(a.amount_refunded_eur,0) AS amount_refunded_eur, CASE WHEN COALESCE(a.gross_captured_eur,0) = 0 THEN NULL ELSE a.amount_refunded_eur / a.gross_captured_eur * 100 END AS refund_rate_pct, CASE WHEN COALESCE(c.gross_captured_eur,0) = 0 THEN NULL ELSE c.amount_refunded_eur / c.gross_captured_eur * 100 END AS company_avg_refund_rate_pct FROM quarters q LEFT JOIN apparel_agg a ON a.year_num = q.year_num AND a.quarter_num = q.quarter_num LEFT JOIN company_agg c ON c.year_num = q.year_num AND c.quarter_num = q.quarter_num ORDER BY q.year_num NULLS LAST, q.quarter_num 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 Kofi Mensah (analyst for the Chief Financial Officer): Title: Apparel & Footwear Refund Rate Trend by Quarter, 2024-2025 Question: Within the outlier Apparel & Footwear vertical, is the elevated refund rate structural/stable or getting worse over time, and how does it compare to the company average each quarter? Grain: one calendar quarter (2024-Q1 through 2025-Q4), restricted to the Apparel & Footwear vertical Columns: - quarter - merchant_count (Apparel & Footwear tenants with qualifying payments that quarter) - payment_count - gross_captured_eur - amount_refunded_eur - refund_rate_pct - company_avg_refund_rate_pct (all verticals, same quarter, for comparison) Details: Source: payments joined to tenants where tenants.industry = 'Apparel & Footwear', filtered to status reflecting captured/refunded/partially_refunded with captured_at between 2024-01-01 and 2025-12-31, converted to EUR the same way as the company-wide refund trend report (exchange_rates matched on currency_code and date(captured_at)). Group by calendar quarter. The company_avg_refund_rate_pct column should be the all-tenant, all-vertical refund rate for that same quarter (volume-weighted: total amount_refunded_eur / total gross_captured_eur across every tenant), to show the gap each quarter. Order chronologically 2024-Q1 to 2025-Q4 (8 rows). Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH quarters AS ( SELECT DISTINCT year_num, quarter_num FROM calendar_days WHERE year_num IN (2024, 2025) ), exch AS ( SELECT quote_code, as_of, rate FROM exchange_rates WHERE base_code = 'EUR' ), payment_eur AS ( SELECT p.id AS payment_id, p.tenant_id, t.industry, cd.year_num, cd.quarter_num, p.amount / er.rate AS amount_eur, p.amount_refunded / er.rate AS amount_refunded_eur FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS date) JOIN exch er ON er.quote_code = p.currency_code AND er.as_of = cd.calendar_date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= TIMESTAMP '2024-01-01' AND p.captured_at < TIMESTAMP '2026-01-01' ), apparel_agg AS ( SELECT year_num, quarter_num, COUNT(DISTINCT tenant_id) AS merchant_count, COUNT(*) AS payment_count, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur WHERE industry = 'Apparel & Footwear' GROUP BY year_num, quarter_num ), company_agg AS ( SELECT year_num, quarter_num, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur GROUP BY year_num, quarter_num ) SELECT q.year_num || '-Q' || q.quarter_num AS quarter, COALESCE(a.merchant_count, 0) AS merchant_count, COALESCE(a.payment_count, 0) AS payment_count, ROUND(COALESCE(a.gross_captured_eur,0),2) AS gross_captured_eur, ROUND(COALESCE(a.amount_refunded_eur,0),2) AS amount_refunded_eur, ROUND(CASE WHEN COALESCE(a.gross_captured_eur,0) = 0 THEN NULL ELSE a.amount_refunded_eur / a.gross_captured_eur * 100 END, 2) AS refund_rate_pct, ROUND(CASE WHEN COALESCE(c.gross_captured_eur,0) = 0 THEN NULL ELSE c.amount_refunded_eur / c.gross_captured_eur * 100 END, 2) AS company_avg_refund_rate_pct FROM quarters q LEFT JOIN apparel_agg a ON a.year_num = q.year_num AND a.quarter_num = q.quarter_num LEFT JOIN company_agg c ON c.year_num = q.year_num AND c.quarter_num = q.quarter_num ORDER BY q.year_num, q.quarter_num
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: quarter, merchant_count, payment_count, gross_captured_eur, amount_refunded_eur, refund_rate_pct, company_avg_refund_rate_pct
submitWITH quarters AS ( SELECT DISTINCT year_num, quarter_num FROM calendar_days WHERE year_num IN (2024, 2025) ), exch AS ( SELECT quote_code, as_of, rate FROM exchange_rates WHERE base_code = 'EUR' ), payment_eur AS ( SELECT p.id AS payment_id, p.tenant_id, t.industry, cd.year_num, cd.quarter_num, p.amount / er.rate AS amount_eur, p.amount_refunded / er.rate AS amount_refunded_eur FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS date) JOIN exch er ON er.quote_code = p.currency_code AND er.as_of = cd.calendar_date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= TIMESTAMP '2024-01-01' AND p.captured_at < TIMESTAMP '2026-01-01' ), apparel_agg AS ( SELECT year_num, quarter_num, COUNT(DISTINCT tenant_id) AS merchant_count, COUNT(*) AS payment_count, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur WHERE industry = 'Apparel & Footwear' GROUP BY year_num, quarter_num ), company_agg AS ( SELECT year_num, quarter_num, SUM(amount_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur FROM payment_eur GROUP BY year_num, quarter_num ) SELECT q.year_num || '-Q' || q.quarter_num AS quarter, COALESCE(a.merchant_count, 0) AS merchant_count, COALESCE(a.payment_count, 0) AS payment_count, ROUND(COALESCE(a.gross_captured_eur,0),2) AS gross_captured_eur, ROUND(COALESCE(a.amount_refunded_eur,0),2) AS amount_refunded_eur, ROUND(CASE WHEN COALESCE(a.gross_captured_eur,0) = 0 THEN NULL ELSE a.amount_refunded_eur / a.gross_captured_eur * 100 END, 2) AS refund_rate_pct, ROUND(CASE WHEN COALESCE(c.gross_captured_eur,0) = 0 THEN NULL ELSE c.amount_refunded_eur / c.gross_captured_eur * 100 END, 2) AS company_avg_refund_rate_pct FROM quarters q LEFT JOIN apparel_agg a ON a.year_num = q.year_num AND a.quarter_num = q.quarter_num LEFT JOIN company_agg c ON c.year_num = q.year_num AND c.quarter_num = q.quarter_num ORDER BY q.year_num, q.quarter_num
Submitted to tonight's batch. Your work on this request is done.