Queries · q211 · Chief Executive Officer · week 2, night 2
“Does the refund spike Finance flagged for Q3 2025 (naming OfficeHub Nordic and Sports & Outdoor sellers) hold up at the merchant level, and is it the same story as Q4?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
Week 2; shared with Tobias Lindqvist, Chief Financial Officer
The board meets at the end of January and wants a clear view of unit economics: per merchant, vertical and country, what Tidewick earns and what it costs us to serve them. Which merchants are worth growing, and which cost more than they bring?
The manager, at the start of week 2
Subject: Great first week — here's where I want to go next
Hannah,
This was exactly the kind of grounding I needed — thank you. A few things surprised me and one big new ask from the board that I need us to lead together with Tobias.
What surprised me / needs explaining:
The new board ask — unit economics by merchant, vertical and country:
The board wants to know, plainly, what we earn from serving each merchant/vertical/country and what it costs us — so we can say which parts of the business are worth leaning into and which are quietly expensive. This is bigger than one report, so here's what I think it needs, and I want you and Tobias to scope it jointly this week rather than me prescribing the method:
My questions for this week:
Let's talk briefly before you lock in this week's requests so we spend the slots on the unit economics scoping rather than re-running what we already mostly know.
Thanks, Maren
Note to Hannah Brooks
Hannah — a few things from the finance side as you scope the board unit-economics piece:
If there's room, a rough read on how refund patterns differ by vertical or country would help round out the "which merchants are worth growing" question.
Happy to talk through before Friday's memo.
— Tobias
The analyst · week 2, night 2, request 1 of 3
Night 1 results: the currency breakdown report confirms my €53,186 figure to the penny (sums exactly), with no currency-specific anomaly — solid evidence that Finance's €93,583 has a methodology bug on their side, which I'll bring to a joint session with Tobias's team rather than keep guessing blindly at their query. On refunds: my Q4 2025 merchant breakdown shows our two biggest Apparel merchants (Cragsley, Marrowby & Vane) actually improved slightly, and the spike is driven by small long-tail merchants (Fell & Fjell, Bytefield, Brick & Bobbin) with big % jumps but tiny order counts — consistent with Kofi's "concentrated, not broad" read. But Kofi specifically named Q3 2025 and OfficeHub Nordic, and my data shows OfficeHub Nordic at 0% refund rate in Q4 — so I need the Q3-specific breakdown to confirm or correct his named examples before this goes to Maren as settled. Separately, the risk snapshot surfaced a much bigger receivables problem than Castell Fasteners: Kontor24 Bürobedarf has €116.7k unpaid (8x Castell's), and the biggest unpaid balances are all B2B wholesalers — directly matching Tobias's receivables question. Tonight: (1) redo the refund check on Kofi's actual Q3 window to nail the root-cause question, (2) a receivables-aging report to answer Tobias's "who's slow to pay" question and deepen the watch-list, (3) start the unit-economics groundwork with a cost-driver volume inventory per merchant (shipping, returns, refunds, tickets, webhook failures) — the measurable building blocks Tobias and I will need to assign real costs to.
The engineer · 1 dry run on the empty schema, then the submission
WITH q3_refunds AS ( SELECT pr.tenant_id, SUM(pr.amount / er.rate) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = pr.processed_at::date WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-07-01' AND pr.processed_at < DATE '2025-10-01' GROUP BY pr.tenant_id ), q3_payments AS ( SELECT p.tenant_id, SUM(p.amount / er.rate) AS captured_amount_eur FROM payments p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = p.captured_at::date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= DATE '2025-07-01' AND p.captured_at < DATE '2025-10-01' GROUP BY p.tenant_id ), h1_refunds AS ( SELECT pr.tenant_id, SUM(pr.amount / er.rate) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = pr.processed_at::date WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-01-01' AND pr.processed_at < DATE '2025-07-01' GROUP BY pr.tenant_id ), h1_payments AS ( SELECT p.tenant_id, SUM(p.amount / er.rate) AS captured_amount_eur FROM payments p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = p.captured_at::date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2025-07-01' GROUP BY p.tenant_id ), q3_orders AS ( SELECT tenant_id, COUNT(*) AS order_count FROM orders WHERE placed_at >= DATE '2025-07-01' AND placed_at < DATE '2025-10-01' GROUP BY tenant_id ) SELECT t.name AS merchant_name, t.industry AS vertical, ROUND(100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0), 2) AS refund_rate_q3_pct, ROUND(100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0), 2) AS refund_rate_h1_baseline_pct, ROUND( (100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0)) - (100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0)) , 2) AS change_vs_baseline_pp, ROUND(COALESCE(qr.refund_amount_eur,0), 2) AS refund_amount_q3_eur, qo.order_count AS order_count_q3 FROM q3_orders qo JOIN tenants t ON t.id = qo.tenant_id LEFT JOIN q3_refunds qr ON qr.tenant_id = qo.tenant_id LEFT JOIN q3_payments qp ON qp.tenant_id = qo.tenant_id LEFT JOIN h1_refunds hr ON hr.tenant_id = qo.tenant_id LEFT JOIN h1_payments hp ON hp.tenant_id = qo.tenant_id WHERE qo.order_count >= 10 ORDER BY change_vs_baseline_pp DESC NULLS LAST, refund_amount_q3_eur DESC, merchant_name ASC
“Query compiles and matches the requested grain and columns. Submitting.”
“Submitted. Summary of what was built:
- Grain: one row per merchant (tenant), restricted to merchants with ≥10 orders placed in Q3 2025.
- Method: refund rate = 100 × (succeeded payment_refunds, EUR-converted, processed_at in quarter) ÷ (captured/partially_refunded/refunded payments, EUR-converted, captured_at in quarter), attributed by tenant_id directly on those two tables — same approach as the Q4 report, applied to Q3 2025 vs. the H1 2025 baseline.
- FX: each amount divided by the same-day EUR→currency rate from exchange_rates.
- Columns: merchant name, vertical (tenants.industry), Q3 2025 refund rate %, H1 2025 baseline rate %, change in percentage points, Q3 refund amount (EUR), Q3 order count.
- Order: change vs. baseline descending, tie-break by Q3 refund amount descending, then name for determinism.
Noted in the submit comment: "vertical" is sourced from tenants.industry (no separate vertical field exists), and the ≥10-orders filter uses the count of orders placed in Q3 2025.”
As Hannah Brooks saw it, from the small dataset the agents worked on
merchant_name | vertical | refund_rate_q3_pct | refund_rate_h1_baseline_pct | change_vs_baseline_pp | refund_amount_q3_eur | order_count_q3 ------------------------------+---------------------------+--------------------+-----------------------------+-----------------------+----------------------+--------------- OfficeHub Nordic | Office Supplies (B2B) | 20.98 | 2.99 | 17.99 | 23267.74 | 45 Saffron & Sage | Beauty | 9.62 | 0.0 | 9.62 | 183.14 | 25 Nordic Bloom Cosmetics | Beauty | 12.2 | 4.59 | 7.61 | 591.12 | 38 Petalcourt Beauty | Beauty | 11.63 | 4.03 | 7.6 | 1754.09 | 164 Kayak Corner | Sports & Outdoor | 14.12 | 8.78 | 5.34 | 828.54 | 24 Feather & Fin | Pet Supplies | 3.95 | 0.56 | 3.39 | 104.85 | 51 Honeycomb Larder | Food & Coffee | 4.2 | 0.93 | 3.27 | 231.58 | 149 Kibblewick | Pet Supplies | 2.0 | 0.69 | 1.31 | 153.25 | 166 Juniper Apothecary | Beauty | 7.53 | 6.49 | 1.04 | 242.06 | 31 Fennel & Flint | Home & Living | 7.85 | 7.24 | 0.61 | 910.97 | 75 Tail Wag Supply | Pet Supplies | 2.34 | 2.39 | -0.05 | 32.83 | 23 Castell Fasteners | Industrial Supplies (B2B) | 0.0 | 0.34 | -0.34 | 0.0 | 37 Deskline Supplies | Office Supplies (B2B) | 0.0 | 0.46 | -0.46 | 0.0 | 18 Oakhurst Tailoring | Apparel & Footwear | 13.06 | 14.01 | -0.95 | 518.28 | 37 The Balm Room | Beauty | 4.5 | 5.53 | -1.03 | 154.76 | 23 Grind Collective | Food & Coffee | 0.0 | 1.1 | -1.1 | 0.0 | 279 Fell & Fjell | Sports & Outdoor | 2.61 | 4.1 | -1.48 | 382.12 | 22 Coastline Interiors | Home & Living | 7.04 | 8.77 | -1.73 | 184.7 | 25 Cinder Peak Sports Co. | Sports & Outdoor | 9.28 | 12.54 | -3.26 | 2712.96 | 75 Cragsley Outfitters | Apparel & Footwear | 22.82 | 26.1 | -3.27 | 28724.77 | 762 Purrfect Pantry | Pet Supplies | 0.0 | 3.44 | -3.44 | 0.0 | 13 Thistlecombe | Apparel & Footwear | 21.64 | 25.16 | -3.52 | 561.43 | 25 Marrowby & Vane | Apparel & Footwear | 20.24 | 24.4 | -4.16 | 10088.68 | 369 Willow & Wren | Home & Living | 6.22 | 13.31 | -7.09 | 233.03 | 15 Lamplight Living | Home & Living | 0.66 | 11.98 | -11.32 | 12.77 | 15 Velvet Ash | Apparel & Footwear | 19.65 | 38.05 | -18.4 | 773.06 | 29 Ampfeld Elektronik | Consumer Electronics | NULL | NULL | NULL | 0.0 | 80 Atelier Nouveau | Apparel & Footwear | NULL | NULL | NULL | 0.0 | 18 Ballycorran Industrial Supply | Industrial Supplies (B2B) | NULL | NULL | NULL | 0.0 | 79 Basecamp Nord | Sports & Outdoor | NULL | NULL | NULL | 0.0 | 12 Brennquell Kaffee | Food & Coffee | NULL | NULL | NULL | 0.0 | 980 Brewline Coffee | Food & Coffee | NULL | NULL | NULL | 0.0 | 10 Chocolaterie Mireille | Food & Coffee | NULL | NULL | NULL | 0.0 | 32 Gratlinie Outdoor | Sports & Outdoor | NULL | NULL | NULL | 0.0 | 51 Hanse Industriebedarf | Industrial Supplies (B2B) | NULL | NULL | NULL | 0.0 | 15 Hautnah | Beauty | NULL | NULL | NULL | 0.0 | 13 Hound Hamper | Pet Supplies | NULL | NULL | NULL | 0.0 | 11 Kettle & Key | Home & Living | NULL | NULL | NULL | 0.0 | 12 Kontor24 Bürobedarf | Office Supplies (B2B) | NULL | NULL | NULL | 0.0 | 57 Linden & Loom | Home & Living | NULL | NULL | NULL | 0.0 | 45 Lumen Collective | Apparel & Footwear | NULL | NULL | NULL | 0.0 | 11 Maison Clair | Apparel & Footwear | NULL | NULL | NULL | 0.0 | 10 Midland Safety Wear | Industrial Supplies (B2B) | NULL | NULL | NULL | 0.0 | 45 Mirelle | Beauty | NULL | NULL | NULL | 0.0 | 15 Noordzee Surfwear | Apparel & Footwear | NULL | NULL | NULL | 0.0 | 16 Ostwind Mode | Apparel & Footwear | NULL | NULL | NULL | 0.0 | 22 Pfotenglück | Pet Supplies | NULL | NULL | NULL | 0.0 | 26 Polder Packaging | Industrial Supplies (B2B) | NULL | NULL | NULL | 0.0 | 18 Ridgeback Gear | Sports & Outdoor | NULL | NULL | NULL | 0.0 | 28 Rue Lafayette Studio | Apparel & Footwear | NULL | NULL | NULL | 0.0 | 44 (58 rows, first 50 printed)
The memo and the queries that followed; links found by title, so inferred
It builds on:
Changed only where the benchmark needs the same result on every system
WITH q3_refunds AS ( SELECT pr.tenant_id, SUM(pr.amount / er.rate) AS refund_amount_eur SELECT pr.tenant_id AS tenant_id, SUM(CAST(pr.amount / er.rate AS NUMERIC(18,6))) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = pr.processed_at::date AND er.as_of = CAST(pr.processed_at AS date) WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-07-01' AND pr.processed_at < DATE '2025-10-01' GROUP BY pr.tenant_id),q3_payments AS ( SELECT p.tenant_id, SUM(p.amount / er.rate) AS captured_amount_eur SELECT p.tenant_id AS tenant_id, SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) AS captured_amount_eur FROM payments p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = p.captured_at::date AND er.as_of = CAST(p.captured_at AS date) WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= DATE '2025-07-01' AND p.captured_at < DATE '2025-10-01' GROUP BY p.tenant_id),h1_refunds AS ( SELECT pr.tenant_id, SUM(pr.amount / er.rate) AS refund_amount_eur SELECT pr.tenant_id AS tenant_id, SUM(CAST(pr.amount / er.rate AS NUMERIC(18,6))) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = pr.processed_at::date AND er.as_of = CAST(pr.processed_at AS date) WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-01-01' AND pr.processed_at < DATE '2025-07-01' GROUP BY pr.tenant_id),h1_payments AS ( SELECT p.tenant_id, SUM(p.amount / er.rate) AS captured_amount_eur SELECT p.tenant_id AS tenant_id, SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) AS captured_amount_eur FROM payments p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = p.captured_at::date AND er.as_of = CAST(p.captured_at AS date) WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2025-07-01' GROUP BY p.tenant_id),q3_orders AS ( SELECT tenant_id, COUNT(*) AS order_count FROM orders WHERE placed_at >= DATE '2025-07-01' AND placed_at < DATE '2025-10-01' GROUP BY tenant_id)SELECT t.name AS merchant_name, t.industry AS vertical, ROUND(100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0), 2) AS refund_rate_q3_pct, ROUND(100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0), 2) AS refund_rate_h1_baseline_pct, ROUND( (100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0)) - (100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0)) , 2) AS change_vs_baseline_pp, ROUND(COALESCE(qr.refund_amount_eur,0), 2) AS refund_amount_q3_eur, 100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0) AS refund_rate_q3_pct, 100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0) AS refund_rate_h1_baseline_pct, (100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0)) - (100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0)) AS change_vs_baseline_pp, COALESCE(qr.refund_amount_eur,0) AS refund_amount_q3_eur, qo.order_count AS order_count_q3FROM q3_orders qoJOIN tenants t ON t.id = qo.tenant_idLEFT JOIN q3_refunds qr ON qr.tenant_id = qo.tenant_idLEFT JOIN q3_payments qp ON qp.tenant_id = qo.tenant_idLEFT JOIN h1_refunds hr ON hr.tenant_id = qo.tenant_idLEFT JOIN h1_payments hp ON hp.tenant_id = qo.tenant_idWHERE qo.order_count >= 10ORDER BY change_vs_baseline_pp DESC NULLS LAST, refund_amount_q3_eur DESC, merchant_name ASCORDER BY change_vs_baseline_pp DESC NULLS LAST, refund_amount_q3_eur DESC NULLS FIRST, merchant_name ASC NULLS LAST, t.id ASC NULLS LAST
WITH q3_refunds AS ( SELECT pr.tenant_id AS tenant_id, SUM(CAST(pr.amount / er.rate AS NUMERIC(18,6))) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = CAST(pr.processed_at AS date) WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-07-01' AND pr.processed_at < DATE '2025-10-01' GROUP BY pr.tenant_id ), q3_payments AS ( SELECT p.tenant_id AS tenant_id, SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) AS captured_amount_eur FROM payments p 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','partially_refunded','refunded') AND p.captured_at >= DATE '2025-07-01' AND p.captured_at < DATE '2025-10-01' GROUP BY p.tenant_id ), h1_refunds AS ( SELECT pr.tenant_id AS tenant_id, SUM(CAST(pr.amount / er.rate AS NUMERIC(18,6))) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = CAST(pr.processed_at AS date) WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-01-01' AND pr.processed_at < DATE '2025-07-01' GROUP BY pr.tenant_id ), h1_payments AS ( SELECT p.tenant_id AS tenant_id, SUM(CAST(p.amount / er.rate AS NUMERIC(18,6))) AS captured_amount_eur FROM payments p 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','partially_refunded','refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2025-07-01' GROUP BY p.tenant_id ), q3_orders AS ( SELECT tenant_id, COUNT(*) AS order_count FROM orders WHERE placed_at >= DATE '2025-07-01' AND placed_at < DATE '2025-10-01' GROUP BY tenant_id ) SELECT t.name AS merchant_name, t.industry AS vertical, 100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0) AS refund_rate_q3_pct, 100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0) AS refund_rate_h1_baseline_pct, (100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0)) - (100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0)) AS change_vs_baseline_pp, COALESCE(qr.refund_amount_eur,0) AS refund_amount_q3_eur, qo.order_count AS order_count_q3 FROM q3_orders qo JOIN tenants t ON t.id = qo.tenant_id LEFT JOIN q3_refunds qr ON qr.tenant_id = qo.tenant_id LEFT JOIN q3_payments qp ON qp.tenant_id = qo.tenant_id LEFT JOIN h1_refunds hr ON hr.tenant_id = qo.tenant_id LEFT JOIN h1_payments hp ON hp.tenant_id = qo.tenant_id WHERE qo.order_count >= 10 ORDER BY change_vs_baseline_pp DESC NULLS LAST, refund_amount_q3_eur DESC NULLS FIRST, merchant_name ASC NULLS LAST, t.id 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: Merchant refund-rate spike check, Q3 2025 vs H1 2025 baseline Question: Does the refund spike Finance flagged for Q3 2025 (naming OfficeHub Nordic and Sports & Outdoor sellers) hold up at the merchant level, and is it the same story as Q4? Grain: one row per merchant (tenant) Columns: - merchant name - vertical - refund rate Q3 2025 (%) - refund rate H1 2025 baseline (%) - change vs own baseline (percentage points) - refund amount Q3 2025 (EUR) - order count Q3 2025 Details: Same method as last night's Q4 refund-spike report: refund rate = 100 * (succeeded payment_refunds amount, EUR, processed_at in quarter) / (captured payments amount, EUR, captured_at in quarter, status in 'captured','partially_refunded','refunded'), attributed to merchant via tenant_id on payments/payment_refunds directly. Q3 2025 = 2025-07-01 to 2025-10-01; H1 2025 baseline = 2025-01-01 to 2025-07-01. Include only merchants with at least 10 orders in Q3 2025. Order by change vs own baseline descending, tie-break by refund amount Q3 2025 descending. I specifically want to see where OfficeHub Nordic and any Sports & Outdoor merchants land in this ranking, and whether the same merchants appear here as in the Q4 version of this report. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH q3_refunds AS ( SELECT pr.tenant_id, SUM(pr.amount / er.rate) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = pr.processed_at::date WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-07-01' AND pr.processed_at < DATE '2025-10-01' GROUP BY pr.tenant_id ), q3_payments AS ( SELECT p.tenant_id, SUM(p.amount / er.rate) AS captured_amount_eur FROM payments p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = p.captured_at::date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= DATE '2025-07-01' AND p.captured_at < DATE '2025-10-01' GROUP BY p.tenant_id ), h1_refunds AS ( SELECT pr.tenant_id, SUM(pr.amount / er.rate) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = pr.processed_at::date WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-01-01' AND pr.processed_at < DATE '2025-07-01' GROUP BY pr.tenant_id ), h1_payments AS ( SELECT p.tenant_id, SUM(p.amount / er.rate) AS captured_amount_eur FROM payments p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = p.captured_at::date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2025-07-01' GROUP BY p.tenant_id ), q3_orders AS ( SELECT tenant_id, COUNT(*) AS order_count FROM orders WHERE placed_at >= DATE '2025-07-01' AND placed_at < DATE '2025-10-01' GROUP BY tenant_id ) SELECT t.name AS merchant_name, t.industry AS vertical, ROUND(100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0), 2) AS refund_rate_q3_pct, ROUND(100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0), 2) AS refund_rate_h1_baseline_pct, ROUND( (100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0)) - (100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0)) , 2) AS change_vs_baseline_pp, ROUND(COALESCE(qr.refund_amount_eur,0), 2) AS refund_amount_q3_eur, qo.order_count AS order_count_q3 FROM q3_orders qo JOIN tenants t ON t.id = qo.tenant_id LEFT JOIN q3_refunds qr ON qr.tenant_id = qo.tenant_id LEFT JOIN q3_payments qp ON qp.tenant_id = qo.tenant_id LEFT JOIN h1_refunds hr ON hr.tenant_id = qo.tenant_id LEFT JOIN h1_payments hp ON hp.tenant_id = qo.tenant_id WHERE qo.order_count >= 10 ORDER BY change_vs_baseline_pp DESC NULLS LAST, refund_amount_q3_eur DESC, merchant_name ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: merchant_name, vertical, refund_rate_q3_pct, refund_rate_h1_baseline_pct, change_vs_baseline_pp, refund_amount_q3_eur, order_count_q3
submitWITH q3_refunds AS ( SELECT pr.tenant_id, SUM(pr.amount / er.rate) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = pr.processed_at::date WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-07-01' AND pr.processed_at < DATE '2025-10-01' GROUP BY pr.tenant_id ), q3_payments AS ( SELECT p.tenant_id, SUM(p.amount / er.rate) AS captured_amount_eur FROM payments p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = p.captured_at::date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= DATE '2025-07-01' AND p.captured_at < DATE '2025-10-01' GROUP BY p.tenant_id ), h1_refunds AS ( SELECT pr.tenant_id, SUM(pr.amount / er.rate) AS refund_amount_eur FROM payment_refunds pr JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = pr.currency_code AND er.as_of = pr.processed_at::date WHERE pr.status = 'succeeded' AND pr.processed_at >= DATE '2025-01-01' AND pr.processed_at < DATE '2025-07-01' GROUP BY pr.tenant_id ), h1_payments AS ( SELECT p.tenant_id, SUM(p.amount / er.rate) AS captured_amount_eur FROM payments p JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = p.currency_code AND er.as_of = p.captured_at::date WHERE p.status IN ('captured','partially_refunded','refunded') AND p.captured_at >= DATE '2025-01-01' AND p.captured_at < DATE '2025-07-01' GROUP BY p.tenant_id ), q3_orders AS ( SELECT tenant_id, COUNT(*) AS order_count FROM orders WHERE placed_at >= DATE '2025-07-01' AND placed_at < DATE '2025-10-01' GROUP BY tenant_id ) SELECT t.name AS merchant_name, t.industry AS vertical, ROUND(100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0), 2) AS refund_rate_q3_pct, ROUND(100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0), 2) AS refund_rate_h1_baseline_pct, ROUND( (100.0 * COALESCE(qr.refund_amount_eur,0) / NULLIF(qp.captured_amount_eur,0)) - (100.0 * COALESCE(hr.refund_amount_eur,0) / NULLIF(hp.captured_amount_eur,0)) , 2) AS change_vs_baseline_pp, ROUND(COALESCE(qr.refund_amount_eur,0), 2) AS refund_amount_q3_eur, qo.order_count AS order_count_q3 FROM q3_orders qo JOIN tenants t ON t.id = qo.tenant_id LEFT JOIN q3_refunds qr ON qr.tenant_id = qo.tenant_id LEFT JOIN q3_payments qp ON qp.tenant_id = qo.tenant_id LEFT JOIN h1_refunds hr ON hr.tenant_id = qo.tenant_id LEFT JOIN h1_payments hp ON hp.tenant_id = qo.tenant_id WHERE qo.order_count >= 10 ORDER BY change_vs_baseline_pp DESC NULLS LAST, refund_amount_q3_eur DESC, merchant_name ASC
Submitted to tonight's batch. Your work on this request is done.