Queries · q022 · Chief Financial Officer · week 1, night 4
“Which vertical(s) outside Apparel & Footwear drove the rise in company-wide refund rate through 2025, especially the Q2-Q3 spike?”
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 4, request 1 of 3
Report 1 confirms the fix: the corrected monthly series now ties exactly to the independently-verified 2025 annual totals (€3,972,987.99 gross / €93,583.20 fee / 2.4% blended fee rate), and payout counts roughly doubled vs. the original buggy report — consistent with it having silently dropped close to half of all payouts for a reason we never fully root-caused, but which is now moot since the corrected figures cross-foot cleanly. #1 is now safe to put in front of Tobias, with the one caveat I'll carry into the memo: nothing in this warehouse captures Tidewick's own platform-plan billing of merchants (the plans/subscriptions/invoices tables here model each merchant's billing of their customers, not Tidewick's billing of the merchant) — so I can only report the processing-fee half of the revenue split, not the plan-fee half.
On refunds, I did the arithmetic behind Report 2 and 3 by hand: subtracting Apparel's numbers from the company totals shows the non-Apparel refund rate was stable at ~2-3% through 2024, but spiked to 4.9% in 2025-Q2 and 9.1% in 2025-Q3 before dropping back to 4.2% in Q4 — a 3-4x jump that Apparel's own flat/improving trend doesn't explain. Apparel itself looks structural (19-26% every quarter, actually lowest in the latest quarter), so that part of #3 is answered and not worsening. But this non-Apparel spike is new information worth chasing on this last night before the memo, since it could be a specific merchant issue (e.g. a product recall or quality problem) rather than normal seasonal variation. I'm spending two slots to localize it (by vertical, then by merchant) and a third to cross-check the payments-based refund figures against the independent order_returns table, given how costly the earlier monthly-revenue bug was — I don't want to hand Tobias a refund narrative built on a single, unverified source.
The engineer · 1 dry run on the empty schema, then the submission
SELECT COALESCE(t.industry, 'Unknown') AS vertical, 'Q' || cd.quarter_num AS quarter, COUNT(DISTINCT t.id) AS merchant_count, COUNT(*) AS payment_count, SUM(p.amount / er.rate) AS gross_captured_eur, SUM(p.amount_refunded / er.rate) AS amount_refunded_eur, ROUND(100.0 * SUM(p.amount_refunded / er.rate) / NULLIF(SUM(p.amount / er.rate), 0), 2) AS refund_rate_pct 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 exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = CAST(p.captured_at AS date) WHERE p.status IN ('captured', 'refunded', 'partially_refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' GROUP BY 1, 2 ORDER BY vertical ASC, quarter ASC
“Query compiles and returns the expected columns. Submitting.”
“Submitted. The report groups payments by tenants.industry (nulls as "Unknown") and calendar quarter of captured_at for 2025, converting local amounts to EUR via the daily EUR-base exchange rate matched to the capture date, and computes refund rate as refunded/gross. This should let Kofi see which non-apparel verticals contributed most to the Q2–Q3 refund-rate spike.”
As Kofi Mensah saw it, from the small dataset the agents worked on
vertical | quarter | merchant_count | payment_count | gross_captured_eur | amount_refunded_eur | refund_rate_pct --------------------------+---------+----------------+---------------+--------------------+---------------------+---------------- Apparel & Footwear | Q1 | 11 | 1150 | 216621.44091669368 | 44831.176879493345 | 20.7 Apparel & Footwear | Q2 | 11 | 1169 | 198881.2193894893 | 44578.07783795187 | 22.41 Apparel & Footwear | Q3 | 11 | 1212 | 192047.59636464564 | 42818.10689885081 | 22.3 Apparel & Footwear | Q4 | 11 | 2298 | 470544.8364552379 | 91672.61863472672 | 19.48 Beauty | Q1 | 6 | 280 | 25818.54401057241 | 1085.7447446305723 | 4.21 Beauty | Q2 | 7 | 228 | 25725.83935097318 | 619.50811391392 | 2.41 Beauty | Q3 | 8 | 284 | 29641.158365632327 | 3150.3888966371146 | 10.63 Beauty | Q4 | 7 | 459 | 44113.676007047616 | 2923.057264107972 | 6.63 Books & Stationery | Q1 | 3 | 27 | 1355.1836199234158 | 58.63298735958467 | 4.33 Books & Stationery | Q2 | 3 | 26 | 1434.8048158637575 | 75.81729784451257 | 5.28 Books & Stationery | Q3 | 4 | 17 | 817.6496332968031 | 11.501861056716475 | 1.41 Books & Stationery | Q4 | 4 | 45 | 2023.393101739535 | 40.8426243531702 | 2.02 Consumer Electronics | Q1 | 2 | 7 | 8090.2199209759565 | 57.04991401762959 | 0.71 Consumer Electronics | Q2 | 2 | 8 | 1029.9743631223505 | 0.0 | 0.0 Consumer Electronics | Q3 | 2 | 9 | 11249.432963260206 | 0.0 | 0.0 Consumer Electronics | Q4 | 3 | 21 | 12334.929902163894 | 4479.206917994399 | 36.31 Food & Coffee | Q1 | 3 | 330 | 18354.785502809722 | 62.383472348541886 | 0.34 Food & Coffee | Q2 | 3 | 355 | 18249.53217315367 | 269.9056568395979 | 1.48 Food & Coffee | Q3 | 3 | 363 | 15662.413332444743 | 180.75184916705518 | 1.15 Food & Coffee | Q4 | 5 | 482 | 24928.853024262517 | 368.7037831923822 | 1.48 Home & Living | Q1 | 9 | 167 | 27894.272465097492 | 2433.823480440758 | 8.73 Home & Living | Q2 | 9 | 154 | 23751.388145572895 | 2898.7327936654633 | 12.2 Home & Living | Q3 | 9 | 158 | 24198.566575605862 | 1015.1305518444232 | 4.2 Home & Living | Q4 | 9 | 347 | 55031.91113703543 | 3289.176261514632 | 5.98 Industrial Supplies (B2B) | Q1 | 1 | 30 | 67009.71065267481 | 0.0 | 0.0 Industrial Supplies (B2B) | Q2 | 1 | 42 | 130168.99482296039 | 667.9769975297136 | 0.51 Industrial Supplies (B2B) | Q3 | 1 | 35 | 86880.28106375752 | 0.0 | 0.0 Industrial Supplies (B2B) | Q4 | 1 | 47 | 102428.63827279814 | 879.2332472909086 | 0.86 Jewelry & Accessories | Q1 | 1 | 3 | 708.3609146047186 | 0.0 | 0.0 Jewelry & Accessories | Q2 | 1 | 4 | 789.8097417728766 | 0.0 | 0.0 Jewelry & Accessories | Q3 | 1 | 1 | 474.09138186513644 | 0.0 | 0.0 Jewelry & Accessories | Q4 | 1 | 6 | 1029.6122237688733 | 0.0 | 0.0 Office Supplies (B2B) | Q1 | 2 | 61 | 269281.99522658327 | 538.7285520011415 | 0.2 Office Supplies (B2B) | Q2 | 2 | 43 | 139804.73630781923 | 6759.114464105353 | 4.83 Office Supplies (B2B) | Q3 | 2 | 60 | 156766.32653283176 | 23267.743667366227 | 14.84 Office Supplies (B2B) | Q4 | 2 | 75 | 141893.6413073626 | 0.0 | 0.0 Pet Supplies | Q1 | 5 | 241 | 10973.548507433949 | 137.34976084931193 | 1.25 Pet Supplies | Q2 | 6 | 252 | 12697.327961349287 | 68.52328917673564 | 0.54 Pet Supplies | Q3 | 7 | 264 | 13981.660023570754 | 248.39446202754175 | 1.78 Pet Supplies | Q4 | 7 | 338 | 17623.34787040026 | 423.2845831840298 | 2.4 Sports & Outdoor | Q1 | 4 | 133 | 48481.881703017454 | 4748.589361778662 | 9.79 Sports & Outdoor | Q2 | 4 | 136 | 53277.39476609937 | 8671.6034168609 | 16.28 Sports & Outdoor | Q3 | 4 | 122 | 50531.48401879147 | 7597.09439253762 | 15.03 Sports & Outdoor | Q4 | 5 | 151 | 56351.904767109656 | 6598.52473055408 | 11.71 (44 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
SELECT COALESCE(t.industry, 'Unknown') AS vertical, 'Q' || cd.quarter_num AS quarter, COUNT(DISTINCT t.id) AS merchant_count, COUNT(*) AS payment_count, SUM(p.amount / er.rate) AS gross_captured_eur, SUM(p.amount_refunded / er.rate) AS amount_refunded_eur, ROUND(100.0 * SUM(p.amount_refunded / er.rate) / NULLIF(SUM(p.amount / er.rate), 0), 2) AS refund_rate_pct SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) AS gross_captured_eur, SUM(CAST(p.amount_refunded / er.rate AS NUMERIC(18,6))) AS amount_refunded_eur, 100.0 * SUM(CAST(p.amount_refunded / er.rate AS NUMERIC(18,6))) / NULLIF(SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))), 0) AS refund_rate_pctFROM payments pJOIN tenants t ON t.id = p.tenant_idJOIN calendar_days cd ON cd.calendar_date = CAST(p.captured_at AS date)JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = CAST(p.captured_at AS date)WHERE p.status IN ('captured', 'refunded', 'partially_refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01'GROUP BY 1, 2ORDER BY vertical ASC, quarter ASCORDER BY vertical ASC NULLS LAST, quarter ASC NULLS LAST
SELECT COALESCE(t.industry, 'Unknown') AS vertical, 'Q' || cd.quarter_num AS quarter, COUNT(DISTINCT t.id) AS merchant_count, COUNT(*) AS payment_count, SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) AS gross_captured_eur, SUM(CAST(p.amount_refunded / er.rate AS NUMERIC(18,6))) AS amount_refunded_eur, 100.0 * SUM(CAST(p.amount_refunded / er.rate AS NUMERIC(18,6))) / NULLIF(SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))), 0) AS refund_rate_pct 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 exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = CAST(p.captured_at AS date) WHERE p.status IN ('captured', 'refunded', 'partially_refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' GROUP BY 1, 2 ORDER BY vertical ASC NULLS LAST, quarter 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 Kofi Mensah (analyst for the Chief Financial Officer): Title: Refund Rate by Vertical and Quarter, 2025 Question: Which vertical(s) outside Apparel & Footwear drove the rise in company-wide refund rate through 2025, especially the Q2-Q3 spike? Grain: one tenant industry vertical and one calendar quarter, 2025 only Columns: - vertical - quarter - merchant_count - payment_count - gross_captured_eur - amount_refunded_eur - refund_rate_pct Details: Source: payments joined to tenants (tenants.industry), filtered to status reflecting captured/refunded/partially_refunded with captured_at in calendar year 2025. Convert amount and amount_refunded to EUR via exchange_rates (base_code='EUR', quote_code=payments.currency_code, as_of=date(captured_at)), dividing local amount by the matched rate, consistent with the validated method used in prior reports this week. Group by tenants.industry (null industry as 'Unknown') and calendar quarter of captured_at. Order by vertical name ascending, then quarter ascending (expect up to ~44 rows for ~11 verticals x 4 quarters). Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT COALESCE(t.industry, 'Unknown') AS vertical, 'Q' || cd.quarter_num AS quarter, COUNT(DISTINCT t.id) AS merchant_count, COUNT(*) AS payment_count, SUM(p.amount / er.rate) AS gross_captured_eur, SUM(p.amount_refunded / er.rate) AS amount_refunded_eur, ROUND(100.0 * SUM(p.amount_refunded / er.rate) / NULLIF(SUM(p.amount / er.rate), 0), 2) AS refund_rate_pct 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 exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = CAST(p.captured_at AS date) WHERE p.status IN ('captured', 'refunded', 'partially_refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' GROUP BY 1, 2 ORDER BY vertical ASC, quarter ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: vertical, quarter, merchant_count, payment_count, gross_captured_eur, amount_refunded_eur, refund_rate_pct
submitSELECT COALESCE(t.industry, 'Unknown') AS vertical, 'Q' || cd.quarter_num AS quarter, COUNT(DISTINCT t.id) AS merchant_count, COUNT(*) AS payment_count, SUM(p.amount / er.rate) AS gross_captured_eur, SUM(p.amount_refunded / er.rate) AS amount_refunded_eur, ROUND(100.0 * SUM(p.amount_refunded / er.rate) / NULLIF(SUM(p.amount / er.rate), 0), 2) AS refund_rate_pct 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 exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = CAST(p.captured_at AS date) WHERE p.status IN ('captured', 'refunded', 'partially_refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' GROUP BY 1, 2 ORDER BY vertical ASC, quarter ASC
Submitted to tonight's batch. Your work on this request is done.