DB Olympics

Queries · q361 · Head of Pricing and Promotions · week 2, night 1

What's driving Welcome10's elevated refund pattern: category concentration and return timing

“Is the Welcome10 instance's outsized refund total concentrated in particular product categories, and does the time between order and return look like a fit/expectation-mismatch problem (fast returns) or something else?”

Manager · asked the questionMarco BianchiHead of Pricing and Promotions. Previously in revenue management at a travel company.
Analyst · planned the reportOliver GrantPeer-minded: compares each merchant with peers of similar size and vertical.
Engineer · wrote the SQLBeaWrites SQL the way the company's BI tool generates it: verbose fully qualified aliases (t0, t1, ...), a subquery per measure joined back on the grain keys, COALESCE everywhere.

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 Pricing and Promotions's desk

    Week 1; shared with Tobias Lindqvist, Chief Financial Officer

    Finance questions the cost of discounts. Which promotions and coupons brought additional sales, which only gave away margin, and is anything being misused?

    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. Marco Bianchi writes to Oliver Grant

    The manager, at the start of week 2

    Subject: Good work — here's where I want to go next

    Oliver,

    This is exactly the kind of memo I needed — it separates real findings from artifacts, which is rarer than it should be. A few reactions and where I want to push next week.

    The lapsed-subscriber giveaway (~€57k) is now the headline for Tobias, not the promo/coupon story — good catch pulling it out. Before I take that number upstairs, I want it airtight. And the Welcome10 broad refund pattern is the one piece of "margin erosion" I think is genuinely actionable rather than a sample-size illusion, so I want to go a layer deeper there before we talk about capping anything.

    On incrementality — I accept we may never get a clean causal answer here, but I'd like one more honest attempt before I write it off as "probably just bigger carts clear the threshold anyway."

    Questions for this week:

    1. Is the active-but-orphan 18% a timing bug or a real gap? I need to know whether the full €57k-ish exposure is confirmed leakage or whether part of it shrinks once the overlap check is fixed properly.
    2. What's actually driving Welcome10's refund pattern? Is it concentrated in particular products, categories, or merchants, or does it look like people discount-shopping and then returning — i.e., is this a fit/expectation problem or a "discount attracts returners" problem?
    3. Can we get a cleaner incrementality read by comparing similar-sized orders with and without the discount, rather than comparing discounted vs. all orders? I'd rather have a more honest "we still can't tell" than a flattering but confounded number.
    4. For the lapsed-subscriber leakage, how far back does it go and is it accelerating or steady? I want to know if this is a slow-burning historical gap or something that's gotten worse recently, since that changes how urgently we need to fix the underlying process versus just write off the past.

    No need to re-litigate the B2B volume-tier outlier — just keep it excluded as you planned. Let's talk Wednesday before the last batch so I can steer based on what the orphan-subscription check turns up.

    Thanks, Marco

  3. Oliver Grant plans the night

    The analyst · week 2, night 1, request 3 of 3

    Marco wants the €57k lapsed-subscriber number airtight before it goes to Tobias, wants Welcome10's refund pattern explained one layer deeper, wants an honest size-matched incrementality attempt, and wants the leakage trend over time — four questions, three slots tonight, so I'm sequencing by what's blocking the Wednesday sync.

    Priority tonight is the subscription-leakage story since Marco explicitly wants it checked before taking it upstairs, so two of three slots go there:

    1. A methodology check on the 18%/€11.7k "active" bucket from last week — I suspect this isn't a real gap but an artifact of how I categorized customers by their single most-recent subscription record: if their current subscription started after the free order, there may well have been an older (now-superseded) subscription that actually covered the order date, which would mean it belongs with the "canceled/lapsed" leakage story rather than being unexplained. I want this resolved before stating a number to Tobias.
    2. A monthly trend (full 2023-2025 history) on the confirmed "canceled-subscriber" leakage bucket, to tell Marco whether this is a slow-burning historical gap or getting worse — directly answers his accelerating-or-steady question.

    Third slot goes to digging one layer into Welcome10's refund pattern (which category of products it clusters in, and whether return timing looks like fit/expectation mismatch vs. quick-flip behavior) — the one piece Marco called out as worth a deeper look before any capping conversation. I'm holding the harder, size-matched incrementality re-cut for night 2, since it needs more careful design and isn't needed before Wednesday's sync, whereas the subscription-leakage number is.

    Also noting for context (not acting on tonight): Growth Marketing's company-wide finding that discounted orders return 5-7pts more with no basket-size boost in apparel/electronics lines up with our Welcome10 finding — useful external corroboration, not a new data pull.

    What's driving Welcome10's elevated refund pattern: category concentration and return timing
    Question
    Is the Welcome10 instance's outsized refund total concentrated in particular product categories, and does the time between order and return look like a fit/expectation-mismatch problem (fast returns) or something else?
    One row per
    product category, restricted to the single Welcome10 discount instance with 260 orders (coupon_code='WELCOME10') identified in last week's report
    Columns
    • category name (products.primary_category_id -> categories.name)
    • order_item count sold under this discount instance
    • distinct orders
    • items returned (return_items count)
    • return rate (% of items sold that were returned)
    • total refund amount for this category (EUR)
    • this category's share of the instance's total refund amount (%)
    • avg days between order placed_at and the return's requested_at (for returned items)

  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 d.id AS discount_id, d.tenant_id
  FROM discounts d
  WHERE d.name = 'Welcome 10% off'
),
t1 AS (
  SELECT
    t0.discount_id AS discount_id,
    COUNT(DISTINCT o.id) AS order_count_2025
  FROM t0
  JOIN order_discounts od ON od.discount_id = t0.discount_id
  JOIN orders o ON o.id = od.order_id
  WHERE o.status <> 'cancelled'
    AND o.placed_at >= DATE '2025-01-01'
    AND o.placed_at < DATE '2026-01-01'
  GROUP BY t0.discount_id
),
t2 AS (
  SELECT t1.discount_id AS discount_id
  FROM t1
  WHERE t1.order_count_2025 = 260
),
t3 AS (
  SELECT DISTINCT o.id AS order_id, o.placed_at AS order_placed_at
  FROM order_discounts od
  JOIN t2 ON t2.discount_id = od.discount_id
  JOIN orders o ON o.id = od.order_id
  WHERE o.status <> 'cancelled'
    AND o.placed_at >= DATE '2025-01-01'
    AND o.placed_at < DATE '2026-01-01'
),
t4 AS (
  SELECT
    oi.id AS order_item_id,
    oi.order_id AS order_id,
    t3.order_placed_at AS order_placed_at,
    COALESCE(cat.id, -1) AS category_id,
    COALESCE(cat.name, 'Uncategorized') AS category_name
  FROM order_items oi
  JOIN t3 ON t3.order_id = oi.order_id
  JOIN product_variants pv ON pv.id = oi.product_variant_id
  JOIN products p ON p.id = pv.product_id
  LEFT JOIN categories cat ON cat.id = p.primary_category_id
),
t5 AS (
  SELECT DISTINCT t4.category_id AS category_id, t4.category_name AS category_name
  FROM t4
),
t6 AS (
  SELECT
    ri.id AS return_item_id,
    ri.order_item_id AS order_item_id,
    ri.refund_amount AS refund_amount,
    ri.currency_code AS currency_code,
    ret.requested_at AS requested_at,
    ret.refunded_at AS refunded_at
  FROM return_items ri
  JOIN order_returns ret ON ret.id = ri.return_id
  JOIN t4 ON t4.order_item_id = ri.order_item_id
),
t7 AS (
  SELECT
    t6.return_item_id AS return_item_id,
    t6.order_item_id AS order_item_id,
    t6.refund_amount AS refund_amount,
    t6.requested_at AS requested_at,
    er.rate AS eur_rate
  FROM t6
  LEFT JOIN exchange_rates er
    ON er.base_code = 'EUR'
   AND er.quote_code = t6.currency_code
   AND er.as_of = CAST(COALESCE(t6.requested_at, t6.refunded_at) AS date)
),
t8 AS (
  SELECT
    t4.category_id AS category_id,
    t7.return_item_id AS return_item_id,
    t7.refund_amount / NULLIF(t7.eur_rate, 0) AS refund_amount_eur,
    CAST(t7.requested_at AS date) - CAST(t4.order_placed_at AS date) AS days_to_return
  FROM t7
  JOIN t4 ON t4.order_item_id = t7.order_item_id
),
t9 AS (
  SELECT
    t4.category_id AS category_id,
    COUNT(*) AS items_sold,
    COUNT(DISTINCT t4.order_id) AS distinct_orders
  FROM t4
  GROUP BY t4.category_id
),
t10 AS (
  SELECT
    t8.category_id AS category_id,
    COUNT(*) AS items_returned,
    SUM(t8.refund_amount_eur) AS total_refund_eur,
    AVG(t8.days_to_return) AS avg_days_to_return
  FROM t8
  GROUP BY t8.category_id
),
t11 AS (
  SELECT SUM(t8.refund_amount_eur) AS grand_total_refund_eur
  FROM t8
)
SELECT
  t5.category_name AS category_name,
  COALESCE(t9.items_sold, 0) AS order_items_sold,
  COALESCE(t9.distinct_orders, 0) AS distinct_orders,
  COALESCE(t10.items_returned, 0) AS items_returned,
  ROUND(
    100.0 * COALESCE(t10.items_returned, 0) / NULLIF(COALESCE(t9.items_sold, 0), 0),
    1
  ) AS return_rate_pct,
  ROUND(COALESCE(t10.total_refund_eur, 0), 2) AS total_refund_eur,
  ROUND(
    100.0 * COALESCE(t10.total_refund_eur, 0) / NULLIF(t11.grand_total_refund_eur, 0),
    1
  ) AS share_of_instance_refund_pct,
  ROUND(t10.avg_days_to_return, 1) AS avg_days_order_to_return
FROM t5
LEFT JOIN t9 ON t9.category_id = t5.category_id
LEFT JOIN t10 ON t10.category_id = t5.category_id
CROSS JOIN t11
ORDER BY total_refund_eur DESC, category_name ASC

    “The query compiles and returns the expected 8 columns at the category grain. Submitting.”

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

    “Submitted. Summary of what the report does: - Re-identifies the discount instance: finds the discounts row named "Welcome 10% off" whose 2025 non-cancelled orders (via order_discounts) total exactly 260, matching last week's finding. - Grain: one row per product category (via products.primary_category_id -> categories), covering all order items sold under that discount instance. - Measures: items sold, distinct orders, items returned, return rate %, total refund in EUR, share of the instance's total refund %, and average days between order placement and return request — to help distinguish fast "fit/expectation mismatch" returns from slower, other-cause returns. - Conversion: refund amounts (from return_items.refund_amount) converted to EUR using the exchange rate as of the return's requested_at (falling back to refunded_at). One caveat flagged to the analyst: all return_items are included regardless of the return's status (no filter on order_returns.status), since the request didn't specify restricting to e.g. "refunded" only — this may include a few returns still in progress.”

  5. The report comes back overnight

    As Oliver Grant saw it, from the small dataset the agents worked on

    category_name  | order_items_sold | distinct_orders | items_returned | return_rate_pct | total_refund_eur | share_of_instance_refund_pct | avg_days_order_to_return
---------------+------------------+-----------------+----------------+-----------------+------------------+------------------------------+-------------------------
Outerwear      |               42 |              32 |             11 |            26.2 |          2534.23 |                         19.4 |                     14.1
Boots          |               44 |              35 |             13 |            29.5 |           2452.5 |                         18.8 |                     17.2
Sneakers       |               48 |              32 |             12 |            25.0 |          1745.81 |                         13.4 |                      9.3
Outerwear      |               30 |              27 |              8 |            26.7 |           874.26 |                          6.7 |                     16.5
Trousers       |               44 |              37 |             11 |            25.0 |           815.39 |                          6.2 |                     13.4
Sandals        |               35 |              24 |             10 |            28.6 |           674.14 |                          5.2 |                     13.0
Dresses        |               32 |              22 |              7 |            21.9 |           630.07 |                          4.8 |                     10.6
Tops           |               51 |              40 |             12 |            23.5 |           549.49 |                          4.2 |                     12.4
Knitwear       |               28 |              21 |              8 |            28.6 |           521.81 |                          4.0 |                     14.3
Trousers       |               42 |              33 |              6 |            14.3 |           461.67 |                          3.5 |                     15.0
Loungewear     |               20 |              17 |              4 |            20.0 |           413.36 |                          3.2 |                     15.0
Loungewear     |               21 |              16 |              7 |            33.3 |           387.82 |                          3.0 |                     16.0
Scarves & Hats |               82 |              53 |              7 |             8.5 |            307.8 |                          2.4 |                     15.4
Knitwear       |               14 |              14 |              3 |            21.4 |            210.5 |                          1.6 |                     18.7
Socks          |               45 |              33 |             14 |            31.1 |           201.02 |                          1.5 |                     14.3
Tops           |               18 |              15 |              6 |            33.3 |           104.07 |                          0.8 |                     13.5
Shirts         |                9 |               8 |              2 |            22.2 |            97.77 |                          0.7 |                     16.5
Tops           |               66 |              34 |              7 |            10.6 |            89.84 |                          0.7 |                     10.3
(18 rows)

  6. What came of it

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

    It builds on:

  7. It becomes benchmark query q361

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

    • determinismtie-breaker: category id
    • numericsdocs/NUMERICS.md: AVG over an integer computed as a decimal: AVG(CAST(x AS NUMERIC(18,6)))
    • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
    • numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6)
    • made explicita selected t.col gets AS col, its standard name made explicit
    • placeholderdate diff days
    • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (3×)
    The benchmark text, against the submitted SQL
    WITH t0 AS (  SELECT d.id AS discount_id, d.tenant_id  SELECT d.id AS discount_id, d.tenant_id AS tenant_id  FROM discounts d  WHERE d.name = 'Welcome 10% off'),t1 AS (  SELECT    t0.discount_id AS discount_id,    COUNT(DISTINCT o.id) AS order_count_2025  FROM t0  JOIN order_discounts od ON od.discount_id = t0.discount_id  JOIN orders o ON o.id = od.order_id  WHERE o.status <> 'cancelled'    AND o.placed_at >= DATE '2025-01-01'    AND o.placed_at < DATE '2026-01-01'  GROUP BY t0.discount_id),t2 AS (  SELECT t1.discount_id AS discount_id  FROM t1  WHERE t1.order_count_2025 = 260),t3 AS (  SELECT DISTINCT o.id AS order_id, o.placed_at AS order_placed_at  FROM order_discounts od  JOIN t2 ON t2.discount_id = od.discount_id  JOIN orders o ON o.id = od.order_id  WHERE o.status <> 'cancelled'    AND o.placed_at >= DATE '2025-01-01'    AND o.placed_at < DATE '2026-01-01'),t4 AS (  SELECT    oi.id AS order_item_id,    oi.order_id AS order_id,    t3.order_placed_at AS order_placed_at,    COALESCE(cat.id, -1) AS category_id,    COALESCE(cat.name, 'Uncategorized') AS category_name  FROM order_items oi  JOIN t3 ON t3.order_id = oi.order_id  JOIN product_variants pv ON pv.id = oi.product_variant_id  JOIN products p ON p.id = pv.product_id  LEFT JOIN categories cat ON cat.id = p.primary_category_id),t5 AS (  SELECT DISTINCT t4.category_id AS category_id, t4.category_name AS category_name  FROM t4),t6 AS (  SELECT    ri.id AS return_item_id,    ri.order_item_id AS order_item_id,    ri.refund_amount AS refund_amount,    ri.currency_code AS currency_code,    ret.requested_at AS requested_at,    ret.refunded_at AS refunded_at  FROM return_items ri  JOIN order_returns ret ON ret.id = ri.return_id  JOIN t4 ON t4.order_item_id = ri.order_item_id),t7 AS (  SELECT    t6.return_item_id AS return_item_id,    t6.order_item_id AS order_item_id,    t6.refund_amount AS refund_amount,    t6.requested_at AS requested_at,    er.rate AS eur_rate  FROM t6  LEFT JOIN exchange_rates er    ON er.base_code = 'EUR'   AND er.quote_code = t6.currency_code   AND er.as_of = CAST(COALESCE(t6.requested_at, t6.refunded_at) AS date)),t8 AS (  SELECT    t4.category_id AS category_id,    t7.return_item_id AS return_item_id,    t7.refund_amount / NULLIF(t7.eur_rate, 0) AS refund_amount_eur,    CAST(t7.requested_at AS date) - CAST(t4.order_placed_at AS date) AS days_to_return    CAST(t7.refund_amount / NULLIF(t7.eur_rate, 0) AS NUMERIC(18,6)) AS refund_amount_eur,    {{date_diff_days(t4.order_placed_at, t7.requested_at)}} AS days_to_return  FROM t7  JOIN t4 ON t4.order_item_id = t7.order_item_id),t9 AS (  SELECT    t4.category_id AS category_id,    COUNT(*) AS items_sold,    COUNT(DISTINCT t4.order_id) AS distinct_orders  FROM t4  GROUP BY t4.category_id),t10 AS (  SELECT    t8.category_id AS category_id,    COUNT(*) AS items_returned,    SUM(t8.refund_amount_eur) AS total_refund_eur,    AVG(t8.days_to_return) AS avg_days_to_return    AVG(CAST(t8.days_to_return AS NUMERIC(18,6))) AS avg_days_to_return  FROM t8  GROUP BY t8.category_id),t11 AS (  SELECT SUM(t8.refund_amount_eur) AS grand_total_refund_eur  FROM t8)SELECT  t5.category_name AS category_name,  COALESCE(t9.items_sold, 0) AS order_items_sold,  COALESCE(t9.distinct_orders, 0) AS distinct_orders,  COALESCE(t10.items_returned, 0) AS items_returned,  ROUND(    100.0 * COALESCE(t10.items_returned, 0) / NULLIF(COALESCE(t9.items_sold, 0), 0),    1  ) AS return_rate_pct,  ROUND(COALESCE(t10.total_refund_eur, 0), 2) AS total_refund_eur,  ROUND(    100.0 * COALESCE(t10.total_refund_eur, 0) / NULLIF(t11.grand_total_refund_eur, 0),    1  ) AS share_of_instance_refund_pct,  ROUND(t10.avg_days_to_return, 1) AS avg_days_order_to_return  100.0 * COALESCE(t10.items_returned, 0) / NULLIF(COALESCE(t9.items_sold, 0), 0) AS return_rate_pct,  COALESCE(t10.total_refund_eur, 0) AS total_refund_eur,  100.0 * COALESCE(t10.total_refund_eur, 0) / NULLIF(t11.grand_total_refund_eur, 0) AS share_of_instance_refund_pct,  t10.avg_days_to_return AS avg_days_order_to_returnFROM t5LEFT JOIN t9 ON t9.category_id = t5.category_idLEFT JOIN t10 ON t10.category_id = t5.category_idCROSS JOIN t11ORDER BY total_refund_eur DESC, category_name ASCORDER BY total_refund_eur DESC NULLS FIRST, category_name ASC NULLS LAST, t5.category_id NULLS LAST

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

    WITH t0 AS (
  SELECT d.id AS discount_id, d.tenant_id AS tenant_id
  FROM discounts d
  WHERE d.name = 'Welcome 10% off'
),
t1 AS (
  SELECT
    t0.discount_id AS discount_id,
    COUNT(DISTINCT o.id) AS order_count_2025
  FROM t0
  JOIN order_discounts od ON od.discount_id = t0.discount_id
  JOIN orders o ON o.id = od.order_id
  WHERE o.status <> 'cancelled'
    AND o.placed_at >= DATE '2025-01-01'
    AND o.placed_at < DATE '2026-01-01'
  GROUP BY t0.discount_id
),
t2 AS (
  SELECT t1.discount_id AS discount_id
  FROM t1
  WHERE t1.order_count_2025 = 260
),
t3 AS (
  SELECT DISTINCT o.id AS order_id, o.placed_at AS order_placed_at
  FROM order_discounts od
  JOIN t2 ON t2.discount_id = od.discount_id
  JOIN orders o ON o.id = od.order_id
  WHERE o.status <> 'cancelled'
    AND o.placed_at >= DATE '2025-01-01'
    AND o.placed_at < DATE '2026-01-01'
),
t4 AS (
  SELECT
    oi.id AS order_item_id,
    oi.order_id AS order_id,
    t3.order_placed_at AS order_placed_at,
    COALESCE(cat.id, -1) AS category_id,
    COALESCE(cat.name, 'Uncategorized') AS category_name
  FROM order_items oi
  JOIN t3 ON t3.order_id = oi.order_id
  JOIN product_variants pv ON pv.id = oi.product_variant_id
  JOIN products p ON p.id = pv.product_id
  LEFT JOIN categories cat ON cat.id = p.primary_category_id
),
t5 AS (
  SELECT DISTINCT t4.category_id AS category_id, t4.category_name AS category_name
  FROM t4
),
t6 AS (
  SELECT
    ri.id AS return_item_id,
    ri.order_item_id AS order_item_id,
    ri.refund_amount AS refund_amount,
    ri.currency_code AS currency_code,
    ret.requested_at AS requested_at,
    ret.refunded_at AS refunded_at
  FROM return_items ri
  JOIN order_returns ret ON ret.id = ri.return_id
  JOIN t4 ON t4.order_item_id = ri.order_item_id
),
t7 AS (
  SELECT
    t6.return_item_id AS return_item_id,
    t6.order_item_id AS order_item_id,
    t6.refund_amount AS refund_amount,
    t6.requested_at AS requested_at,
    er.rate AS eur_rate
  FROM t6
  LEFT JOIN exchange_rates er
    ON er.base_code = 'EUR'
   AND er.quote_code = t6.currency_code
   AND er.as_of = CAST(COALESCE(t6.requested_at, t6.refunded_at) AS date)
),
t8 AS (
  SELECT
    t4.category_id AS category_id,
    t7.return_item_id AS return_item_id,
    CAST(t7.refund_amount / NULLIF(t7.eur_rate, 0) AS NUMERIC(18,6)) AS refund_amount_eur,
    DATE_DIFF('day', t4.order_placed_at, t7.requested_at) AS days_to_return
  FROM t7
  JOIN t4 ON t4.order_item_id = t7.order_item_id
),
t9 AS (
  SELECT
    t4.category_id AS category_id,
    COUNT(*) AS items_sold,
    COUNT(DISTINCT t4.order_id) AS distinct_orders
  FROM t4
  GROUP BY t4.category_id
),
t10 AS (
  SELECT
    t8.category_id AS category_id,
    COUNT(*) AS items_returned,
    SUM(t8.refund_amount_eur) AS total_refund_eur,
    AVG(CAST(t8.days_to_return AS NUMERIC(18,6))) AS avg_days_to_return
  FROM t8
  GROUP BY t8.category_id
),
t11 AS (
  SELECT SUM(t8.refund_amount_eur) AS grand_total_refund_eur
  FROM t8
)
SELECT
  t5.category_name AS category_name,
  COALESCE(t9.items_sold, 0) AS order_items_sold,
  COALESCE(t9.distinct_orders, 0) AS distinct_orders,
  COALESCE(t10.items_returned, 0) AS items_returned,
  100.0 * COALESCE(t10.items_returned, 0) / NULLIF(COALESCE(t9.items_sold, 0), 0) AS return_rate_pct,
  COALESCE(t10.total_refund_eur, 0) AS total_refund_eur,
  100.0 * COALESCE(t10.total_refund_eur, 0) / NULLIF(t11.grand_total_refund_eur, 0) AS share_of_instance_refund_pct,
  t10.avg_days_to_return AS avg_days_order_to_return
FROM t5
LEFT JOIN t9 ON t9.category_id = t5.category_id
LEFT JOIN t10 ON t10.category_id = t5.category_id
CROSS JOIN t11
ORDER BY total_refund_eur DESC NULLS FIRST, category_name ASC NULLS LAST, t5.category_id NULLS LAST
    100 ms1 s10 sHyper194 msDuckDB477 msGizmoSQL4.12 sclickhouse_brew371 msFirebolterrorDataFusion782 msClickHouseerror
    • Firebolt{ "errors": [ { "description": "Line 78, Column 27: 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": "ff9bf980-6dc1-431f-b789-2ee9421750d7", "query_lab
    • ClickHouseCode: 69. DB::Exception: Decimal result's scale is less than argument's one: while executing function divide on arguments __table33.refund_amount Decimal(14, 4) Decimal64(size = 0), nullIf(__table33.eur_rate, 0_UInt8) Nullable(Decimal(12, 6)) Nullable(size = 0, Decimal64(size = 0), UInt8(size = 0)).

    Scale factor 1000, c8a.24xlarge, after the last load. q361 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 Oliver Grant (analyst for the Head of Pricing and Promotions):

Title: What's driving Welcome10's elevated refund pattern: category concentration and return timing
Question: Is the Welcome10 instance's outsized refund total concentrated in particular product categories, and does the time between order and return look like a fit/expectation-mismatch problem (fast returns) or something else?
Grain: one row per product category, restricted to the single Welcome10 discount instance with 260 orders (coupon_code='WELCOME10') identified in last week's report
Columns:
- category name (products.primary_category_id -> categories.name)
- order_item count sold under this discount instance
- distinct orders
- items returned (return_items count)
- return rate (% of items sold that were returned)
- total refund amount for this category (EUR)
- this category's share of the instance's total refund amount (%)
- avg days between order placed_at and the return's requested_at (for returned items)
Details: Source: re-identify the same discount_id as last week by discounts.name='Welcome 10% off' whose 2025 non-cancelled order count via order_discounts equals exactly 260. For those 260 orders, join order_items (quantity, product_variant_id) -> product_variants -> products (primary_category_id) -> categories (name); join return_items (via order_item_id) -> order_returns (status, requested_at, refund_amount or order_returns.refund_total apportioned) for returns tied to those order_items. Convert refund amounts to EUR via exchange_rates (base_code='EUR', quote_code=currency_code, as_of=order_returns.requested_at or refunded_at). Order by total refund amount (EUR) descending, category name as tie-break. Print all categories.

Check your query with the warehouse's dry_run tool, then hand it in with submit.
    • Bea calls dry_run
      WITH t0 AS (
  SELECT d.id AS discount_id, d.tenant_id
  FROM discounts d
  WHERE d.name = 'Welcome 10% off'
),
t1 AS (
  SELECT
    t0.discount_id AS discount_id,
    COUNT(DISTINCT o.id) AS order_count_2025
  FROM t0
  JOIN order_discounts od ON od.discount_id = t0.discount_id
  JOIN orders o ON o.id = od.order_id
  WHERE o.status <> 'cancelled'
    AND o.placed_at >= DATE '2025-01-01'
    AND o.placed_at < DATE '2026-01-01'
  GROUP BY t0.discount_id
),
t2 AS (
  SELECT t1.discount_id AS discount_id
  FROM t1
  WHERE t1.order_count_2025 = 260
),
t3 AS (
  SELECT DISTINCT o.id AS order_id, o.placed_at AS order_placed_at
  FROM order_discounts od
  JOIN t2 ON t2.discount_id = od.discount_id
  JOIN orders o ON o.id = od.order_id
  WHERE o.status <> 'cancelled'
    AND o.placed_at >= DATE '2025-01-01'
    AND o.placed_at < DATE '2026-01-01'
),
t4 AS (
  SELECT
    oi.id AS order_item_id,
    oi.order_id AS order_id,
    t3.order_placed_at AS order_placed_at,
    COALESCE(cat.id, -1) AS category_id,
    COALESCE(cat.name, 'Uncategorized') AS category_name
  FROM order_items oi
  JOIN t3 ON t3.order_id = oi.order_id
  JOIN product_variants pv ON pv.id = oi.product_variant_id
  JOIN products p ON p.id = pv.product_id
  LEFT JOIN categories cat ON cat.id = p.primary_category_id
),
t5 AS (
  SELECT DISTINCT t4.category_id AS category_id, t4.category_name AS category_name
  FROM t4
),
t6 AS (
  SELECT
    ri.id AS return_item_id,
    ri.order_item_id AS order_item_id,
    ri.refund_amount AS refund_amount,
    ri.currency_code AS currency_code,
    ret.requested_at AS requested_at,
    ret.refunded_at AS refunded_at
  FROM return_items ri
  JOIN order_returns ret ON ret.id = ri.return_id
  JOIN t4 ON t4.order_item_id = ri.order_item_id
),
t7 AS (
  SELECT
    t6.return_item_id AS return_item_id,
    t6.order_item_id AS order_item_id,
    t6.refund_amount AS refund_amount,
    t6.requested_at AS requested_at,
    er.rate AS eur_rate
  FROM t6
  LEFT JOIN exchange_rates er
    ON er.base_code = 'EUR'
   AND er.quote_code = t6.currency_code
   AND er.as_of = CAST(COALESCE(t6.requested_at, t6.refunded_at) AS date)
),
t8 AS (
  SELECT
    t4.category_id AS category_id,
    t7.return_item_id AS return_item_id,
    t7.refund_amount / NULLIF(t7.eur_rate, 0) AS refund_amount_eur,
    CAST(t7.requested_at AS date) - CAST(t4.order_placed_at AS date) AS days_to_return
  FROM t7
  JOIN t4 ON t4.order_item_id = t7.order_item_id
),
t9 AS (
  SELECT
    t4.category_id AS category_id,
    COUNT(*) AS items_sold,
    COUNT(DISTINCT t4.order_id) AS distinct_orders
  FROM t4
  GROUP BY t4.category_id
),
t10 AS (
  SELECT
    t8.category_id AS category_id,
    COUNT(*) AS items_returned,
    SUM(t8.refund_amount_eur) AS total_refund_eur,
    AVG(t8.days_to_return) AS avg_days_to_return
  FROM t8
  GROUP BY t8.category_id
),
t11 AS (
  SELECT SUM(t8.refund_amount_eur) AS grand_total_refund_eur
  FROM t8
)
SELECT
  t5.category_name AS category_name,
  COALESCE(t9.items_sold, 0) AS order_items_sold,
  COALESCE(t9.distinct_orders, 0) AS distinct_orders,
  COALESCE(t10.items_returned, 0) AS items_returned,
  ROUND(
    100.0 * COALESCE(t10.items_returned, 0) / NULLIF(COALESCE(t9.items_sold, 0), 0),
    1
  ) AS return_rate_pct,
  ROUND(COALESCE(t10.total_refund_eur, 0), 2) AS total_refund_eur,
  ROUND(
    100.0 * COALESCE(t10.total_refund_eur, 0) / NULLIF(t11.grand_total_refund_eur, 0),
    1
  ) AS share_of_instance_refund_pct,
  ROUND(t10.avg_days_to_return, 1) AS avg_days_order_to_return
FROM t5
LEFT JOIN t9 ON t9.category_id = t5.category_id
LEFT JOIN t10 ON t10.category_id = t5.category_id
CROSS JOIN t11
ORDER BY total_refund_eur DESC, category_name ASC
    • The warehouse answers
      STATUS: COMPILES (dry run on the empty schema, no data read)
COLUMNS: category_name, order_items_sold, distinct_orders, items_returned, return_rate_pct, total_refund_eur, share_of_instance_refund_pct, avg_days_order_to_return
    • BeaThe query compiles and returns the expected 8 columns at the category grain. Submitting.
    • Bea calls submit
      WITH t0 AS (
  SELECT d.id AS discount_id, d.tenant_id
  FROM discounts d
  WHERE d.name = 'Welcome 10% off'
),
t1 AS (
  SELECT
    t0.discount_id AS discount_id,
    COUNT(DISTINCT o.id) AS order_count_2025
  FROM t0
  JOIN order_discounts od ON od.discount_id = t0.discount_id
  JOIN orders o ON o.id = od.order_id
  WHERE o.status <> 'cancelled'
    AND o.placed_at >= DATE '2025-01-01'
    AND o.placed_at < DATE '2026-01-01'
  GROUP BY t0.discount_id
),
t2 AS (
  SELECT t1.discount_id AS discount_id
  FROM t1
  WHERE t1.order_count_2025 = 260
),
t3 AS (
  SELECT DISTINCT o.id AS order_id, o.placed_at AS order_placed_at
  FROM order_discounts od
  JOIN t2 ON t2.discount_id = od.discount_id
  JOIN orders o ON o.id = od.order_id
  WHERE o.status <> 'cancelled'
    AND o.placed_at >= DATE '2025-01-01'
    AND o.placed_at < DATE '2026-01-01'
),
t4 AS (
  SELECT
    oi.id AS order_item_id,
    oi.order_id AS order_id,
    t3.order_placed_at AS order_placed_at,
    COALESCE(cat.id, -1) AS category_id,
    COALESCE(cat.name, 'Uncategorized') AS category_name
  FROM order_items oi
  JOIN t3 ON t3.order_id = oi.order_id
  JOIN product_variants pv ON pv.id = oi.product_variant_id
  JOIN products p ON p.id = pv.product_id
  LEFT JOIN categories cat ON cat.id = p.primary_category_id
),
t5 AS (
  SELECT DISTINCT t4.category_id AS category_id, t4.category_name AS category_name
  FROM t4
),
t6 AS (
  SELECT
    ri.id AS return_item_id,
    ri.order_item_id AS order_item_id,
    ri.refund_amount AS refund_amount,
    ri.currency_code AS currency_code,
    ret.requested_at AS requested_at,
    ret.refunded_at AS refunded_at
  FROM return_items ri
  JOIN order_returns ret ON ret.id = ri.return_id
  JOIN t4 ON t4.order_item_id = ri.order_item_id
),
t7 AS (
  SELECT
    t6.return_item_id AS return_item_id,
    t6.order_item_id AS order_item_id,
    t6.refund_amount AS refund_amount,
    t6.requested_at AS requested_at,
    er.rate AS eur_rate
  FROM t6
  LEFT JOIN exchange_rates er
    ON er.base_code = 'EUR'
   AND er.quote_code = t6.currency_code
   AND er.as_of = CAST(COALESCE(t6.requested_at, t6.refunded_at) AS date)
),
t8 AS (
  SELECT
    t4.category_id AS category_id,
    t7.return_item_id AS return_item_id,
    t7.refund_amount / NULLIF(t7.eur_rate, 0) AS refund_amount_eur,
    CAST(t7.requested_at AS date) - CAST(t4.order_placed_at AS date) AS days_to_return
  FROM t7
  JOIN t4 ON t4.order_item_id = t7.order_item_id
),
t9 AS (
  SELECT
    t4.category_id AS category_id,
    COUNT(*) AS items_sold,
    COUNT(DISTINCT t4.order_id) AS distinct_orders
  FROM t4
  GROUP BY t4.category_id
),
t10 AS (
  SELECT
    t8.category_id AS category_id,
    COUNT(*) AS items_returned,
    SUM(t8.refund_amount_eur) AS total_refund_eur,
    AVG(t8.days_to_return) AS avg_days_to_return
  FROM t8
  GROUP BY t8.category_id
),
t11 AS (
  SELECT SUM(t8.refund_amount_eur) AS grand_total_refund_eur
  FROM t8
)
SELECT
  t5.category_name AS category_name,
  COALESCE(t9.items_sold, 0) AS order_items_sold,
  COALESCE(t9.distinct_orders, 0) AS distinct_orders,
  COALESCE(t10.items_returned, 0) AS items_returned,
  ROUND(
    100.0 * COALESCE(t10.items_returned, 0) / NULLIF(COALESCE(t9.items_sold, 0), 0),
    1
  ) AS return_rate_pct,
  ROUND(COALESCE(t10.total_refund_eur, 0), 2) AS total_refund_eur,
  ROUND(
    100.0 * COALESCE(t10.total_refund_eur, 0) / NULLIF(t11.grand_total_refund_eur, 0),
    1
  ) AS share_of_instance_refund_pct,
  ROUND(t10.avg_days_to_return, 1) AS avg_days_order_to_return
FROM t5
LEFT JOIN t9 ON t9.category_id = t5.category_id
LEFT JOIN t10 ON t10.category_id = t5.category_id
CROSS JOIN t11
ORDER BY total_refund_eur DESC, category_name ASC
    • The warehouse answers
      Submitted to tonight's batch. Your work on this request is done.
    • BeaSubmitted. Summary of what the report does: - **Re-identifies the discount instance**: finds the `discounts` row named "Welcome 10% off" whose 2025 non-cancelled orders (via `order_discounts`) total exactly 260, matching last week's finding. - **Grain**: one row per product category (via `products.primary_category_id -> categories`), covering all order items sold under that discount instance. - **Measures**: items sold, distinct orders, items returned, return rate %, total refund in EUR, share of the instance's total refund %, and average days between order placement and return request — to help distinguish fast "fit/expectation mismatch" returns from slower, other-cause returns. - **Conversion**: refund amounts (from `return_items.refund_amount`) converted to EUR using the exchange rate as of the return's `requested_at` (falling back to `refunded_at`). One caveat flagged to the analyst: all return_items are included regardless of the return's status (no filter on `order_returns.status`), since the request didn't specify restricting to e.g. "refunded" only — this may include a few returns still in progress.

    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 (297 KB) · Oliver Grant's transcript (972 KB) · Marco Bianchi's transcript (259 KB) · the night's plan (7 KB) · the night's reports (9 KB) · the week's memo (3 KB)