Queries · q094 · Head of Data · week 1, night 3

Order-to-payment gap re-measured with refund-inclusive payment status

“How much of the gap between order value and 'captured' payments is really just that refunded/partially-refunded payments were excluded by my status filter, versus a genuine hole where money was never collected?”

Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told

1. A business event lands on the Head of Data's desk

   Week 1; shared with Tobias Lindqvist, Chief Financial Officer

   Before the board pack goes out, Data must certify the key metrics. Do revenue, orders, customers, refunds and payouts agree wherever they are recorded, and where they do not, why not and by how much?

   One of the 35 events we wrote for the role play; it shaped the manager's first note. How events arise · All the events

2. Rosa Van den Berg writes to Yuki Tanaka

   The manager, at the start of week 1

   Subject: Certifying the key metrics before the board pack — what I need from you this week

   Hi Yuki,

   Tobias and I have to certify revenue, orders, customers, refunds and payouts before they go into the board pack. "Certify" means we're telling the board these numbers are reconcilable — so this week's job is to find out where the different places we record these figures agree, where they don't, and whether any gap is big enough to change the story we tell. If something doesn't reconcile, I need to know why and by how much, not just that it's "close enough." That feeds directly into the go/no-go Tobias and I give on the board pack, so please prioritize breadth across the five metrics over depth on any one of them — we can always dig deeper next week on whichever one looks shakiest.

   You only have a handful of report slots a night, so here's what matters most to me, roughly in priority order:

   1. If someone asks "what was our revenue last month," do we get the same answer regardless of which part of the business we ask? Where it diverges, how far apart are the answers, and is the gap explained by something sensible (timing, currency, returns) or does it look like a real inconsistency?
   2. Do our order counts and our revenue tell a consistent story — i.e., is there a clean, traceable link from an order to the money that was actually collected for it, or are there orders that seem to vanish or multiply along the way?
   3. Refunds and payouts: when money goes back to a customer, does it show up consistently as a reduction in revenue and in what we actually pay out to merchants? I want to know if refunds could be hiding or double-counted anywhere.
   4. Customers: can we count "how many customers we have" or "how many are active" in one consistent way, or do different parts of the data give meaningfully different headcounts?
   5. If you have a slot left: is there anything about how timestamps or currencies are handled that could quietly distort any of the above when we roll numbers up across time or across merchants?

   Please flag anything you find inconsistent together with your best read of why, and roughly how big the discrepancy is in practice — that's what Tobias and I need to decide whether we can certify as-is, certify with a caveat, or need to hold a number back.

   Thank you — I know this is a lot for one week. Let's talk before you spend your first slot tonight if you want to sanity-check the plan.

   Rosa

   Show the whole note
   A note from the Chief Financial Officer, Tobias Lindqvist

   Subject: What I need for the revenue/payout certification

   Hi Yuki,

   A few things from my side as you scope this week's work:

   1. Does our reported revenue tie out end to end? I want to know whether what we book as platform fees and processing fees matches what actually settles to us through payouts — and if there's a gap, roughly how large it is and whether it's timing (month-end cutoff) or something structural.
   2. Do merchant payouts reconcile to the gross volume and refunds we report? I'm particularly interested in whether net payouts consistently equal gross sales minus refunds minus our fees, or whether certain merchants/currencies show persistent drift.
   3. Are our B2B wholesalers' outstanding receivables consistent with what we'd show in the annual accounts? I want to understand how much is genuinely overdue versus just lagging normal payment terms, since that affects how we present them.

   If anything else jumps out while you're in there — especially around currency conversion timing or refund treatment — flag it even if it's outside these three.

   Thanks, Tobias

3. Yuki Tanaka plans the night

   The analyst · week 1, night 3, request 1 of 3

   Night 2 gave three follow-ups worth running tonight. (1) The order-status breakdown showed "refunded" orders at a near-total 99.8% gap and "fulfilled" orders at a persistent 22% gap — I suspect both are partly a filter artifact: my captured-payment match only counted payments.status='captured', but a payment that was later refunded moves to status 'refunded'/'partially_refunded' and would be invisible to that filter even though money genuinely changed hands. I want to re-run with a status-inclusive filter to see how much of the gap is this artifact vs a real reconciliation hole, especially for 'fulfilled' orders where there's no reason to expect any gap at all. (2) The payment-linked payout/refund check got noisier, not cleaner, than the calendar-month version (swinging -16.6% to +17.7% with no consistent sign) — payout_items has a unique constraint on payment_id (one row per payment, ever), so a refund processed after a payment's payout has already settled has no slot in that payout to be reflected in; it would have to hit some later, differently-linked settlement, breaking the clean per-payout linkage I assumed. I want to directly test how often refunds are processed after their payment's payout already settled, since that would explain the noise and is itself a traceability gap worth flagging to Tobias. (3) Rosa's customer-count question (#4) hasn't been touched yet and we're entering the back half of the week, so I'm using tonight's third slot to get a first cut at how many "customers" we have under different plausible definitions, to see if that's a quick certify or another can of worms for night 4.

   Read the whole plan
   Order-to-payment gap re-measured with refund-inclusive payment status

   Question

   How much of the gap between order value and 'captured' payments is really just that refunded/partially-refunded payments were excluded by my status filter, versus a genuine hole where money was never collected?

   One row per

   order status

   Columns

   • order_status
   • order_count
   • orders_grand_total_eur
   • orders_with_money_collected_count
   • gross_collected_eur
   • net_collected_eur
   • gap_vs_gross_eur
   • gap_vs_gross_pct

4. Bea writes the SQL

   The engineer · 1 dry run on the empty schema, then the submission

   Dry run 1 · STATUS: COMPILES (dry run on the empty schema, no data read)

   WITH t0 AS (
     SELECT
       o0.id AS order_id,
       o0.status AS order_status,
       o0.grand_total / er0.rate AS grand_total_eur
     FROM orders o0
     JOIN exchange_rates er0
       ON er0.base_code = 'EUR'
      AND er0.quote_code = o0.currency_code
      AND er0.as_of = CAST(o0.placed_at AS date)
     WHERE o0.placed_at >= TIMESTAMP '2023-01-01 00:00:00'
       AND o0.placed_at < TIMESTAMP '2025-07-01 00:00:00'
   ),
   t1 AS (
     SELECT
       p1.order_id AS order_id,
       p1.amount / er1.rate AS gross_eur,
       (p1.amount - p1.amount_refunded) / er1.rate AS net_eur
     FROM payments p1
     JOIN exchange_rates er1
       ON er1.base_code = 'EUR'
      AND er1.quote_code = p1.currency_code
      AND er1.as_of = CAST(p1.captured_at AS date)
     WHERE p1.status IN ('captured', 'refunded', 'partially_refunded')
       AND p1.order_id IN (SELECT t0.order_id FROM t0)
   ),
   t2 AS (
     SELECT
       t1.order_id AS order_id,
       SUM(t1.gross_eur) AS gross_eur,
       SUM(t1.net_eur) AS net_eur
     FROM t1
     GROUP BY t1.order_id
   ),
   t3 AS (
     SELECT
       t0.order_status AS order_status,
       COUNT(t0.order_id) AS order_count,
       SUM(t0.grand_total_eur) AS orders_grand_total_eur
     FROM t0
     GROUP BY t0.order_status
   ),
   t4 AS (
     SELECT
       t0.order_status AS order_status,
       COUNT(t2.order_id) AS orders_with_money_collected_count,
       SUM(COALESCE(t2.gross_eur, 0)) AS gross_collected_eur,
       SUM(COALESCE(t2.net_eur, 0)) AS net_collected_eur
     FROM t0
     LEFT JOIN t2
       ON t2.order_id = t0.order_id
     GROUP BY t0.order_status
   )
   SELECT
     t3.order_status AS order_status,
     t3.order_count AS order_count,
     t3.orders_grand_total_eur AS orders_grand_total_eur,
     t4.orders_with_money_collected_count AS orders_with_money_collected_count,
     t4.gross_collected_eur AS gross_collected_eur,
     t4.net_collected_eur AS net_collected_eur,
     t3.orders_grand_total_eur - t4.gross_collected_eur AS gap_vs_gross_eur,
     ROUND(
       (t3.orders_grand_total_eur - t4.gross_collected_eur) / NULLIF(t3.orders_grand_total_eur, 0) * 100,
       1
     ) AS gap_vs_gross_pct
   FROM t3
   JOIN t4
     ON t4.order_status = t3.order_status
   ORDER BY t3.orders_grand_total_eur DESC, t3.order_status ASC

   “The query compiles and returns the requested columns. Submitting now.”

   Submitted · the SQL of the last dry run, unchanged · Submitted to tonight's batch. Your work on this request is done.

   “Submitted. The report groups orders by orders.status over the 2023-01-01 to 2025-06-30 window, converts order totals to EUR using the placed_at date and payment amounts using the captured_at date, and compares orders_grand_total_eur against both gross and net collected amounts from payments with status in captured/refunded/partially_refunded — isolating how much of the original gap was really refund-inclusive collections versus a true collection shortfall.”

5. The report comes back overnight

   As Yuki Tanaka saw it, from the small dataset the agents worked on

   order_status | order_count | orders_grand_total_eur | orders_with_money_collected_count | gross_collected_eur | net_collected_eur |   gap_vs_gross_eur | gap_vs_gross_pct
   -------------+-------------+------------------------+-----------------------------------+---------------------+-------------------+--------------------+-----------------
   fulfilled    |       18540 |      4863934.962385263 |                             16619 |   4822446.930504212 | 4433540.985117448 |  41488.03188105114 |              0.9
   cancelled    |         788 |     158380.30196644366 |                               259 |  46949.160132157034 |               0.0 | 111431.14183428662 |             70.4
   refunded     |         923 |     125051.87291907084 |                               923 |  118419.97198707449 | 2606.396122521615 |  6631.900931996352 |              5.3
   (3 rows)

6. What came of it

   The memo and the queries that followed; links found by title, so inferred

   Later queries that build on this report:

   • q299Order-to-payment match rate by order age

   It builds on:

   • q091Order-to-payment gap by order status, aged cohort

7. It becomes benchmark query q094

   Changed only where the benchmark needs the same result on every system

   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6) (3×)
   • made explicita selected t.col gets AS col, its standard name made explicit
   • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (2×)
   The benchmark text, against the submitted SQL

   WITH t0 AS (  SELECT    o0.id AS order_id,    o0.status AS order_status,    o0.grand_total / er0.rate AS grand_total_eur    CAST(o0.grand_total / er0.rate AS NUMERIC(18,6)) AS grand_total_eur  FROM orders o0  JOIN exchange_rates er0    ON er0.base_code = 'EUR'   AND er0.quote_code = o0.currency_code   AND er0.as_of = CAST(o0.placed_at AS date)  WHERE o0.placed_at >= TIMESTAMP '2023-01-01 00:00:00'    AND o0.placed_at < TIMESTAMP '2025-07-01 00:00:00'),t1 AS (  SELECT    p1.order_id AS order_id,    p1.amount / er1.rate AS gross_eur,    (p1.amount - p1.amount_refunded) / er1.rate AS net_eur    CAST(p1.amount / er1.rate AS NUMERIC(18,6)) AS gross_eur,    CAST((p1.amount - p1.amount_refunded) / er1.rate AS NUMERIC(18,6)) AS net_eur  FROM payments p1  JOIN exchange_rates er1    ON er1.base_code = 'EUR'   AND er1.quote_code = p1.currency_code   AND er1.as_of = CAST(p1.captured_at AS date)  WHERE p1.status IN ('captured', 'refunded', 'partially_refunded')    AND p1.order_id IN (SELECT t0.order_id FROM t0)    AND p1.order_id IN (SELECT t0.order_id AS order_id FROM t0)),t2 AS (  SELECT    t1.order_id AS order_id,    SUM(t1.gross_eur) AS gross_eur,    SUM(t1.net_eur) AS net_eur  FROM t1  GROUP BY t1.order_id),t3 AS (  SELECT    t0.order_status AS order_status,    COUNT(t0.order_id) AS order_count,    SUM(t0.grand_total_eur) AS orders_grand_total_eur  FROM t0  GROUP BY t0.order_status),t4 AS (  SELECT    t0.order_status AS order_status,    COUNT(t2.order_id) AS orders_with_money_collected_count,    SUM(COALESCE(t2.gross_eur, 0)) AS gross_collected_eur,    SUM(COALESCE(t2.net_eur, 0)) AS net_collected_eur  FROM t0  LEFT JOIN t2    ON t2.order_id = t0.order_id  GROUP BY t0.order_status)SELECT  t3.order_status AS order_status,  t3.order_count AS order_count,  t3.orders_grand_total_eur AS orders_grand_total_eur,  t4.orders_with_money_collected_count AS orders_with_money_collected_count,  t4.gross_collected_eur AS gross_collected_eur,  t4.net_collected_eur AS net_collected_eur,  t3.orders_grand_total_eur - t4.gross_collected_eur AS gap_vs_gross_eur,  ROUND(    (t3.orders_grand_total_eur - t4.gross_collected_eur) / NULLIF(t3.orders_grand_total_eur, 0) * 100,    1  ) AS gap_vs_gross_pct  (t3.orders_grand_total_eur - t4.gross_collected_eur) / NULLIF(t3.orders_grand_total_eur, 0) * 100 AS gap_vs_gross_pctFROM t3JOIN t4  ON t4.order_status = t3.order_statusORDER BY t3.orders_grand_total_eur DESC, t3.order_status ASCORDER BY t3.orders_grand_total_eur DESC NULLS FIRST, t3.order_status ASC NULLS LAST

   As each system received it; highlighted, the parts spelled for that system

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · DataFusion · Firebolt · ClickHouse · chDB · clickhouse_brew

   WITH t0 AS (
     SELECT
       o0.id AS order_id,
       o0.status AS order_status,
       CAST(o0.grand_total / er0.rate AS NUMERIC(18,6)) AS grand_total_eur
     FROM orders o0
     JOIN exchange_rates er0
       ON er0.base_code = 'EUR'
      AND er0.quote_code = o0.currency_code
      AND er0.as_of = CAST(o0.placed_at AS date)
     WHERE o0.placed_at >= TIMESTAMP '2023-01-01 00:00:00'
       AND o0.placed_at < TIMESTAMP '2025-07-01 00:00:00'
   ),
   t1 AS (
     SELECT
       p1.order_id AS order_id,
       CAST(p1.amount / er1.rate AS NUMERIC(18,6)) AS gross_eur,
       CAST((p1.amount - p1.amount_refunded) / er1.rate AS NUMERIC(18,6)) AS net_eur
     FROM payments p1
     JOIN exchange_rates er1
       ON er1.base_code = 'EUR'
      AND er1.quote_code = p1.currency_code
      AND er1.as_of = CAST(p1.captured_at AS date)
     WHERE p1.status IN ('captured', 'refunded', 'partially_refunded')
       AND p1.order_id IN (SELECT t0.order_id AS order_id FROM t0)
   ),
   t2 AS (
     SELECT
       t1.order_id AS order_id,
       SUM(t1.gross_eur) AS gross_eur,
       SUM(t1.net_eur) AS net_eur
     FROM t1
     GROUP BY t1.order_id
   ),
   t3 AS (
     SELECT
       t0.order_status AS order_status,
       COUNT(t0.order_id) AS order_count,
       SUM(t0.grand_total_eur) AS orders_grand_total_eur
     FROM t0
     GROUP BY t0.order_status
   ),
   t4 AS (
     SELECT
       t0.order_status AS order_status,
       COUNT(t2.order_id) AS orders_with_money_collected_count,
       SUM(COALESCE(t2.gross_eur, 0)) AS gross_collected_eur,
       SUM(COALESCE(t2.net_eur, 0)) AS net_collected_eur
     FROM t0
     LEFT JOIN t2
       ON t2.order_id = t0.order_id
     GROUP BY t0.order_status
   )
   SELECT
     t3.order_status AS order_status,
     t3.order_count AS order_count,
     t3.orders_grand_total_eur AS orders_grand_total_eur,
     t4.orders_with_money_collected_count AS orders_with_money_collected_count,
     t4.gross_collected_eur AS gross_collected_eur,
     t4.net_collected_eur AS net_collected_eur,
     t3.orders_grand_total_eur - t4.gross_collected_eur AS gap_vs_gross_eur,
     (t3.orders_grand_total_eur - t4.gross_collected_eur) / NULLIF(t3.orders_grand_total_eur, 0) * 100 AS gap_vs_gross_pct
   FROM t3
   JOIN t4
     ON t4.order_status = t3.order_status
   ORDER BY t3.orders_grand_total_eur DESC NULLS FIRST, t3.order_status ASC NULLS LAST

   • Firebolt{ "errors": [ { "description": "Line 5, Column 25: Operations between decimals with different precision and scale is not supported at the moment. Explicitly cast one of the decimals to other decimal precision and scale." } ], "query": { "query_id": "e397158c-a8ae-4fb0-a22a-26891a66eb53", "query_labe
   • ClickHouseCode: 69. DB::Exception: Decimal result's scale is less than argument's one: while executing function divide on arguments __table3.grand_total Decimal(14, 4) Decimal64(size = 0), __table4.rate Decimal(12, 6) Decimal64(size = 0). (ARGUMENT_OUT_OF_BOUND)

   Scale factor 1000, c8a.24xlarge, after the last load. q094 on the start page, with every configuration.

8. Everything, as recorded

   The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted

   Bea's whole session, 7 turns

   • The request, as Bea received it

     Report request from Yuki Tanaka (analyst for the Head of Data):
     
     Title: Order-to-payment gap re-measured with refund-inclusive payment status
     Question: How much of the gap between order value and 'captured' payments is really just that refunded/partially-refunded payments were excluded by my status filter, versus a genuine hole where money was never collected?
     Grain: order status
     Columns:
     - order_status
     - order_count
     - orders_grand_total_eur
     - orders_with_money_collected_count
     - gross_collected_eur
     - net_collected_eur
     - gap_vs_gross_eur
     - gap_vs_gross_pct
     Details: Same population as the prior order-status report: orders with placed_at between 2023-01-01 and 2025-06-30 inclusive. Group by orders.status. order_count and orders_grand_total_eur as before (grand_total converted to EUR via exchange_rates using orders.currency_code and placed_at date). orders_with_money_collected_count = count of those orders with at least one linked payments row (payments.order_id = orders.id) whose status is in ('captured','refunded','partially_refunded') - i.e. any payment that was successfully captured at some point, whether or not it was later refunded. gross_collected_eur = sum of payments.amount for those linked payments (the original captured amount, before any refund), converted to EUR via payments.currency_code and captured_at date. net_collected_eur = sum of (payments.amount - payments.amount_refunded) for those same linked payments, converted the same way - this is what the merchant actually kept after refunds. gap_vs_gross_eur = orders_grand_total_eur - gross_collected_eur. gap_vs_gross_pct = gap_vs_gross_eur / orders_grand_total_eur * 100, rounded to 1 decimal. Tables: orders, payments, exchange_rates. Order by orders_grand_total_eur descending.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Bea calls dry_run

     WITH t0 AS (
       SELECT
         o0.id AS order_id,
         o0.status AS order_status,
         o0.grand_total / er0.rate AS grand_total_eur
       FROM orders o0
       JOIN exchange_rates er0
         ON er0.base_code = 'EUR'
        AND er0.quote_code = o0.currency_code
        AND er0.as_of = CAST(o0.placed_at AS date)
       WHERE o0.placed_at >= TIMESTAMP '2023-01-01 00:00:00'
         AND o0.placed_at < TIMESTAMP '2025-07-01 00:00:00'
     ),
     t1 AS (
       SELECT
         p1.order_id AS order_id,
         p1.amount / er1.rate AS gross_eur,
         (p1.amount - p1.amount_refunded) / er1.rate AS net_eur
       FROM payments p1
       JOIN exchange_rates er1
         ON er1.base_code = 'EUR'
        AND er1.quote_code = p1.currency_code
        AND er1.as_of = CAST(p1.captured_at AS date)
       WHERE p1.status IN ('captured', 'refunded', 'partially_refunded')
         AND p1.order_id IN (SELECT t0.order_id FROM t0)
     ),
     t2 AS (
       SELECT
         t1.order_id AS order_id,
         SUM(t1.gross_eur) AS gross_eur,
         SUM(t1.net_eur) AS net_eur
       FROM t1
       GROUP BY t1.order_id
     ),
     t3 AS (
       SELECT
         t0.order_status AS order_status,
         COUNT(t0.order_id) AS order_count,
         SUM(t0.grand_total_eur) AS orders_grand_total_eur
       FROM t0
       GROUP BY t0.order_status
     ),
     t4 AS (
       SELECT
         t0.order_status AS order_status,
         COUNT(t2.order_id) AS orders_with_money_collected_count,
         SUM(COALESCE(t2.gross_eur, 0)) AS gross_collected_eur,
         SUM(COALESCE(t2.net_eur, 0)) AS net_collected_eur
       FROM t0
       LEFT JOIN t2
         ON t2.order_id = t0.order_id
       GROUP BY t0.order_status
     )
     SELECT
       t3.order_status AS order_status,
       t3.order_count AS order_count,
       t3.orders_grand_total_eur AS orders_grand_total_eur,
       t4.orders_with_money_collected_count AS orders_with_money_collected_count,
       t4.gross_collected_eur AS gross_collected_eur,
       t4.net_collected_eur AS net_collected_eur,
       t3.orders_grand_total_eur - t4.gross_collected_eur AS gap_vs_gross_eur,
       ROUND(
         (t3.orders_grand_total_eur - t4.gross_collected_eur) / NULLIF(t3.orders_grand_total_eur, 0) * 100,
         1
       ) AS gap_vs_gross_pct
     FROM t3
     JOIN t4
       ON t4.order_status = t3.order_status
     ORDER BY t3.orders_grand_total_eur DESC, t3.order_status ASC

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: order_status, order_count, orders_grand_total_eur, orders_with_money_collected_count, gross_collected_eur, net_collected_eur, gap_vs_gross_eur, gap_vs_gross_pct

   • BeaThe query compiles and returns the requested columns. Submitting now.
   • Bea calls submit

     WITH t0 AS (
       SELECT
         o0.id AS order_id,
         o0.status AS order_status,
         o0.grand_total / er0.rate AS grand_total_eur
       FROM orders o0
       JOIN exchange_rates er0
         ON er0.base_code = 'EUR'
        AND er0.quote_code = o0.currency_code
        AND er0.as_of = CAST(o0.placed_at AS date)
       WHERE o0.placed_at >= TIMESTAMP '2023-01-01 00:00:00'
         AND o0.placed_at < TIMESTAMP '2025-07-01 00:00:00'
     ),
     t1 AS (
       SELECT
         p1.order_id AS order_id,
         p1.amount / er1.rate AS gross_eur,
         (p1.amount - p1.amount_refunded) / er1.rate AS net_eur
       FROM payments p1
       JOIN exchange_rates er1
         ON er1.base_code = 'EUR'
        AND er1.quote_code = p1.currency_code
        AND er1.as_of = CAST(p1.captured_at AS date)
       WHERE p1.status IN ('captured', 'refunded', 'partially_refunded')
         AND p1.order_id IN (SELECT t0.order_id FROM t0)
     ),
     t2 AS (
       SELECT
         t1.order_id AS order_id,
         SUM(t1.gross_eur) AS gross_eur,
         SUM(t1.net_eur) AS net_eur
       FROM t1
       GROUP BY t1.order_id
     ),
     t3 AS (
       SELECT
         t0.order_status AS order_status,
         COUNT(t0.order_id) AS order_count,
         SUM(t0.grand_total_eur) AS orders_grand_total_eur
       FROM t0
       GROUP BY t0.order_status
     ),
     t4 AS (
       SELECT
         t0.order_status AS order_status,
         COUNT(t2.order_id) AS orders_with_money_collected_count,
         SUM(COALESCE(t2.gross_eur, 0)) AS gross_collected_eur,
         SUM(COALESCE(t2.net_eur, 0)) AS net_collected_eur
       FROM t0
       LEFT JOIN t2
         ON t2.order_id = t0.order_id
       GROUP BY t0.order_status
     )
     SELECT
       t3.order_status AS order_status,
       t3.order_count AS order_count,
       t3.orders_grand_total_eur AS orders_grand_total_eur,
       t4.orders_with_money_collected_count AS orders_with_money_collected_count,
       t4.gross_collected_eur AS gross_collected_eur,
       t4.net_collected_eur AS net_collected_eur,
       t3.orders_grand_total_eur - t4.gross_collected_eur AS gap_vs_gross_eur,
       ROUND(
         (t3.orders_grand_total_eur - t4.gross_collected_eur) / NULLIF(t3.orders_grand_total_eur, 0) * 100,
         1
       ) AS gap_vs_gross_pct
     FROM t3
     JOIN t4
       ON t4.order_status = t3.order_status
     ORDER BY t3.orders_grand_total_eur DESC, t3.order_status ASC

   • The warehouse answers

     Submitted to tonight's batch. Your work on this request is done.

   • BeaSubmitted. The report groups orders by `orders.status` over the 2023-01-01 to 2025-06-30 window, converts order totals to EUR using the `placed_at` date and payment amounts using the `captured_at` date, and compares `orders_grand_total_eur` against both gross and net collected amounts from payments with status in `captured`/`refunded`/`partially_refunded` — isolating how much of the original gap was really refund-inclusive collections versus a true collection shortfall.

   Not shown here: 240 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Bea's transcript (271 KB) · Yuki Tanaka's transcript (954 KB) · Rosa Van den Berg's transcript (262 KB) · the night's plan (6 KB) · the night's reports (3 KB) · the week's memo (3 KB)