Queries · q017 · Chief Financial Officer · week 1, night 2
“Do refund rates differ meaningfully across merchant verticals, and which verticals look like outliers?”
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 2, request 2 of 3
Before trusting the 2025 revenue narrative, I cross-footed the three Night 1 reports. Report 2's top-25 merchants alone sum to ~€3.32M gross volume and ~€79.9k fee revenue, and Report 3 independently confirms a 2025 total of ~€3.97M gross volume with top-10 fee share of 65.1%. But Report 1's monthly trend sums to only ~€1.99M gross volume and ~€53.2k fee revenue for all of 2025 — roughly half the true total, and even less than the top-25 merchants alone. That's a real inconsistency, not noise. The implied average fee rate is similar across reports (~2.5-2.7% vs ~2.35%), which points to a coverage problem rather than a rate/conversion-formula error: Report 1 is likely dropping a chunk of payouts, most plausibly through an inner join to exchange_rates that silently excludes payouts in currencies/dates without a matching FX row, rather than a global bug that would also show up in Reports 2/3 (which are internally consistent with each other). I want this diagnosed before I ask for a restated monthly trend, so I'm spending one slot on a currency-level reconciliation with an explicit LEFT JOIN and a count of unmatched rows.
With the remaining two slots I'm moving to Tobias's #3 (refunds/returns), since #2 (concentration) already has a solid answer from Reports 2-3 and doesn't need more tonight. My hypothesis: B2B wholesale verticals (industrial supplies, office equipment) have lower refund rates than apparel/footwear and beauty (higher return propensity categories), and a handful of merchants will be clear outliers worth flagging to Tobias. I'm using payments.amount vs payments.amount_refunded as the cleanest refund-rate source (already cumulative per payment), split by vertical and then by merchant to find outliers.
The engineer · 1 dry run on the empty schema, then the submission
SELECT vertical, merchant_count, payment_count, gross_captured_eur, amount_refunded_eur, ROUND(CASE WHEN gross_captured_eur = 0 THEN NULL ELSE amount_refunded_eur / gross_captured_eur * 100 END, 2) AS refund_rate_pct, ROUND(avg_refund_rate_pct, 2) AS avg_refund_rate_per_merchant_pct FROM ( SELECT vertical, COUNT(*) AS merchant_count, SUM(payment_count) AS payment_count, SUM(gross_captured_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur, AVG(tenant_refund_rate_pct) AS avg_refund_rate_pct FROM ( SELECT COALESCE(t.industry, 'Unknown') AS vertical, t.id AS tenant_id, COUNT(*) AS payment_count, SUM(p.amount / er.rate) AS gross_captured_eur, SUM(p.amount_refunded / er.rate) AS amount_refunded_eur, CASE WHEN SUM(p.amount / er.rate) = 0 THEN 0 ELSE SUM(p.amount_refunded / er.rate) / SUM(p.amount / er.rate) * 100 END AS tenant_refund_rate_pct FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = DATE(p.captured_at) WHERE p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' AND p.status IN ('captured','refunded','partially_refunded') GROUP BY COALESCE(t.industry, 'Unknown'), t.id ) AS tenant_agg GROUP BY vertical ) AS vertical_agg ORDER BY refund_rate_pct DESC, vertical ASC
“The query compiles and produces the requested columns at one row per vertical.”
“Submitted. Summary of what the query does:
- Grain: one row per tenant industry vertical (from tenants.industry, with nulls bucketed as "Unknown").
- Filter: payments with captured_at in 2025 and status in captured, refunded, or partially_refunded.
- FX: each payment's amount and amount_refunded are converted to EUR using that day's EUR→currency_code rate from exchange_rates.
- Metrics: merchant count, payment count, gross captured EUR, refunded EUR, the volume-weighted refund rate (sum refunded / sum captured), and the simple average of each merchant's own EUR-based refund rate within the vertical — letting the CFO spot verticals where a few large merchants skew the headline rate.
- Ordered by weighted refund rate descending, tie-broken by vertical name.
Noted in the submission: industry is free text rather than a fixed vertical taxonomy, so a few tenants may fall under "Unknown" if industry is null.”
As Kofi Mensah saw it, from the small dataset the agents worked on
vertical | merchant_count | payment_count | gross_captured_eur | amount_refunded_eur | refund_rate_pct | avg_refund_rate_per_merchant_pct --------------------------+----------------+---------------+--------------------+---------------------+-----------------+--------------------------------- Apparel & Footwear | 12 | 5829 | 1078095.0931260681 | 223899.98025102273 | 20.77 | 20.17 Consumer Electronics | 3 | 45 | 32704.557149522407 | 4536.256832012029 | 13.87 | 12.09 Sports & Outdoor | 5 | 542 | 208642.66525501793 | 27615.81190173126 | 13.24 | 12.67 Home & Living | 9 | 826 | 130876.13832331166 | 9636.863087465277 | 7.36 | 7.69 Beauty | 8 | 1251 | 125299.2177342255 | 7778.69901928958 | 6.21 | 4.19 Office Supplies (B2B) | 2 | 239 | 707746.699374597 | 30565.586683472724 | 4.32 | 3.64 Books & Stationery | 4 | 115 | 5631.0311708235095 | 186.7947706139839 | 3.32 | 2.27 Pet Supplies | 7 | 1095 | 55275.88436275425 | 877.5520952376191 | 1.59 | 1.53 Food & Coffee | 5 | 1530 | 77195.58403267068 | 881.7447615475771 | 1.14 | 0.48 Industrial Supplies (B2B) | 1 | 154 | 386487.6248121912 | 1547.2102448206222 | 0.4 | 0.4 Jewelry & Accessories | 1 | 14 | 3001.8742620116054 | 0.0 | 0.0 | 0.0 (11 rows)
The memo and the queries that followed; links found by title, so inferred
Later queries that build on this report:
Changed only where the benchmark needs the same result on every system
SELECT vertical, merchant_count, payment_count, gross_captured_eur, amount_refunded_eur, ROUND(CASE WHEN gross_captured_eur = 0 THEN NULL ELSE amount_refunded_eur / gross_captured_eur * 100 END, 2) AS refund_rate_pct, ROUND(avg_refund_rate_pct, 2) AS avg_refund_rate_per_merchant_pct CASE WHEN gross_captured_eur = 0 THEN NULL ELSE amount_refunded_eur / gross_captured_eur * 100 END AS refund_rate_pct, avg_refund_rate_pct AS avg_refund_rate_per_merchant_pctFROM ( SELECT vertical, COUNT(*) AS merchant_count, SUM(payment_count) AS payment_count, SUM(gross_captured_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur, AVG(tenant_refund_rate_pct) AS avg_refund_rate_pct FROM ( SELECT COALESCE(t.industry, 'Unknown') AS vertical, t.id AS tenant_id, COUNT(*) AS payment_count, SUM(p.amount / er.rate) AS gross_captured_eur, SUM(p.amount_refunded / er.rate) AS amount_refunded_eur, CASE WHEN SUM(p.amount / er.rate) = 0 THEN 0 ELSE SUM(p.amount_refunded / er.rate) / SUM(p.amount / er.rate) * 100 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, CASE WHEN SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) = 0 THEN 0 ELSE SUM(CAST(p.amount_refunded / er.rate AS NUMERIC(18,6))) / SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) * 100 END AS tenant_refund_rate_pct FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = DATE(p.captured_at) AND er.as_of = CAST(p.captured_at AS DATE) WHERE p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' AND p.status IN ('captured','refunded','partially_refunded') GROUP BY COALESCE(t.industry, 'Unknown'), t.id ) AS tenant_agg GROUP BY vertical) AS vertical_aggORDER BY refund_rate_pct DESC, vertical ASCORDER BY refund_rate_pct DESC NULLS FIRST, vertical ASC NULLS LAST
SELECT vertical, merchant_count, payment_count, gross_captured_eur, amount_refunded_eur, CASE WHEN gross_captured_eur = 0 THEN NULL ELSE amount_refunded_eur / gross_captured_eur * 100 END AS refund_rate_pct, avg_refund_rate_pct AS avg_refund_rate_per_merchant_pct FROM ( SELECT vertical, COUNT(*) AS merchant_count, SUM(payment_count) AS payment_count, SUM(gross_captured_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur, AVG(tenant_refund_rate_pct) AS avg_refund_rate_pct FROM ( SELECT COALESCE(t.industry, 'Unknown') AS vertical, t.id AS tenant_id, 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, CASE WHEN SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) = 0 THEN 0 ELSE SUM(CAST(p.amount_refunded / er.rate AS NUMERIC(18,6))) / SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) * 100 END AS tenant_refund_rate_pct FROM payments p JOIN tenants t ON t.id = p.tenant_id 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.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' AND p.status IN ('captured','refunded','partially_refunded') GROUP BY COALESCE(t.industry, 'Unknown'), t.id ) AS tenant_agg GROUP BY vertical ) AS vertical_agg ORDER BY refund_rate_pct 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 Kofi Mensah (analyst for the Chief Financial Officer): Title: Refund Rate by Merchant Vertical, 2025 Question: Do refund rates differ meaningfully across merchant verticals, and which verticals look like outliers? Grain: one tenant industry vertical Columns: - vertical - merchant count - payment count - gross captured amount (EUR) - amount refunded (EUR) - refund rate (%) = refunded / captured - avg refund rate per merchant (%) (simple average across merchants in the vertical, for comparison to the volume-weighted rate) Details: Source: payments joined to tenants (tenants.industry) for the vertical grouping. Filter to payments with captured_at in calendar year 2025 and status in a captured/refunded/partially_refunded state (i.e., payments that were actually captured, regardless of later refund). Convert payments.amount and payments.amount_refunded to EUR via exchange_rates (base_code='EUR', quote_code=payments.currency_code, as_of=date(captured_at)). Compute the volume-weighted refund rate per vertical (sum(amount_refunded)/sum(amount)) and also a simple average of each merchant's own refund rate within the vertical, to see whether a few large merchants are skewing the vertical number. Order by volume-weighted refund rate descending, tie-break vertical name ascending. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT vertical, merchant_count, payment_count, gross_captured_eur, amount_refunded_eur, ROUND(CASE WHEN gross_captured_eur = 0 THEN NULL ELSE amount_refunded_eur / gross_captured_eur * 100 END, 2) AS refund_rate_pct, ROUND(avg_refund_rate_pct, 2) AS avg_refund_rate_per_merchant_pct FROM ( SELECT vertical, COUNT(*) AS merchant_count, SUM(payment_count) AS payment_count, SUM(gross_captured_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur, AVG(tenant_refund_rate_pct) AS avg_refund_rate_pct FROM ( SELECT COALESCE(t.industry, 'Unknown') AS vertical, t.id AS tenant_id, COUNT(*) AS payment_count, SUM(p.amount / er.rate) AS gross_captured_eur, SUM(p.amount_refunded / er.rate) AS amount_refunded_eur, CASE WHEN SUM(p.amount / er.rate) = 0 THEN 0 ELSE SUM(p.amount_refunded / er.rate) / SUM(p.amount / er.rate) * 100 END AS tenant_refund_rate_pct FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = DATE(p.captured_at) WHERE p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' AND p.status IN ('captured','refunded','partially_refunded') GROUP BY COALESCE(t.industry, 'Unknown'), t.id ) AS tenant_agg GROUP BY vertical ) AS vertical_agg ORDER BY refund_rate_pct DESC, vertical ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: vertical, merchant_count, payment_count, gross_captured_eur, amount_refunded_eur, refund_rate_pct, avg_refund_rate_per_merchant_pct
submitSELECT vertical, merchant_count, payment_count, gross_captured_eur, amount_refunded_eur, ROUND(CASE WHEN gross_captured_eur = 0 THEN NULL ELSE amount_refunded_eur / gross_captured_eur * 100 END, 2) AS refund_rate_pct, ROUND(avg_refund_rate_pct, 2) AS avg_refund_rate_per_merchant_pct FROM ( SELECT vertical, COUNT(*) AS merchant_count, SUM(payment_count) AS payment_count, SUM(gross_captured_eur) AS gross_captured_eur, SUM(amount_refunded_eur) AS amount_refunded_eur, AVG(tenant_refund_rate_pct) AS avg_refund_rate_pct FROM ( SELECT COALESCE(t.industry, 'Unknown') AS vertical, t.id AS tenant_id, COUNT(*) AS payment_count, SUM(p.amount / er.rate) AS gross_captured_eur, SUM(p.amount_refunded / er.rate) AS amount_refunded_eur, CASE WHEN SUM(p.amount / er.rate) = 0 THEN 0 ELSE SUM(p.amount_refunded / er.rate) / SUM(p.amount / er.rate) * 100 END AS tenant_refund_rate_pct FROM payments p JOIN tenants t ON t.id = p.tenant_id JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = DATE(p.captured_at) WHERE p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2026-01-01' AND p.status IN ('captured','refunded','partially_refunded') GROUP BY COALESCE(t.industry, 'Unknown'), t.id ) AS tenant_agg GROUP BY vertical ) AS vertical_agg ORDER BY refund_rate_pct DESC, vertical ASC
Submitted to tonight's batch. Your work on this request is done.