Queries · q044 · VP Customer Experience · week 1, night 3
“Does the returns/refund experience vary a lot across merchants, and does any merchant stand out as a problem spot the way Studio Vrij did on support tickets?”
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: Where I'd like to start — support & shopper experience
Hi Mateo,
Good to have you on this with me. I'm still building my map of the business, so I want to keep this week broad — I'd rather get a solid first read on a few big questions than a deep dive on one. Here's what's on my mind, roughly in priority order:
What I need these for: I'm meeting merchant success and the exec team soon and want a credible, numbers-backed view of shopper experience — not just war stories. I also want to start spotting the two or three things that, if we fixed them, would move the needle most broadly across merchants, rather than chasing individual complaints.
Given the slot constraints, let's prioritize #1 through #3 this week if we can't fit all five — those feel most urgent to me. Happy to talk through any of this before you lock in requests.
Thanks, Daniel
The analyst · week 1, night 3, request 2 of 3
Report 1 (night 2) shows the May-Jun 2025 anomaly is strongly concentrated in delivery-tagged tickets specifically (response/resolution hours for delivery tickets roughly 2-3x worse than 'other' tickets in those two months, whereas Nov/Dec spikes hit both groups evenly - that looks like genuine seasonal volume, not a delivery-specific problem). Report 2 pins the May-Jun spike almost entirely on one merchant: Studio Vrij had 86 tickets vs an expected ~16 (5.38x their normal rate), 83.7% delivery-tagged, and it was also the slowest-resolving merchant in the whole-year peer table from night 1. Every other merchant with a high spike ratio is tiny in absolute volume (noise), and the platform's other big merchants (Cragsley, Marrowby) were actually below their own baseline rate in May-Jun. So this looks like a single-merchant fulfillment incident, not a platform-wide issue - I want to confirm that with real delivery/shipment data before I write it into the memo as a finding. Report 3 (returns trend) is reassuring: resolve time is flat around ~8 days and rejection rate flat regardless of volume swings; the return-rate spikes track the same Nov/Dec seasonality as everything else. I want one merchant-level peer cut on returns (mirroring the support-speed peer report) to see if any merchant stands out the way Studio Vrij did on tickets. With priorities #1-3 now well covered, I'm using the third slot tonight to open priority #4 (CSAT) so there's something in hand for Friday's memo if priority #5 doesn't fit.
The engineer · 1 dry run on the empty schema, then the submission
SELECT t.name as merchant_name, ord.orders_placed, ret.returns_requested, round(100.0 * ret.returns_requested / ord.orders_placed, 1) as return_rate_pct, rf.avg_days, rf.median_days, round(100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested, 1) as returns_rejected_pct FROM tenants t JOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= '2025-01-01' AND placed_at < '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.id JOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= '2025-01-01' AND requested_at < '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.id LEFT JOIN (SELECT tenant_id, round(avg(days),1) as avg_days, round(cast(percentile_cont(0.5) within group (order by days) as numeric),1) as median_days FROM (SELECT tenant_id, extract(epoch from (refunded_at - requested_at))/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= '2025-01-01' AND requested_at < '2026-01-01') x GROUP BY tenant_id) rf ON rf.tenant_id = t.id LEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= '2025-01-01' AND requested_at < '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.id WHERE ret.returns_requested >= 20 ORDER BY ret.returns_requested DESC, t.name ASC LIMIT 50
“Query compiles and returns exactly the requested columns. Submitting.”
“Submitted. The report returns one row per qualifying merchant (≥20 returns requested in 2025), ordered by returns requested descending, with orders placed, returns requested, return rate %, avg/median days to refund, and rejection rate — all computed via per-tenant subqueries joined to tenants, following the stated date-window and rounding rules.”
As Mateo Ruiz saw it, from the small dataset the agents worked on
merchant_name | orders_placed | returns_requested | return_rate_pct | avg_days | median_days | returns_rejected_pct ------------------------------+---------------+-------------------+-----------------+----------+-------------+--------------------- Cragsley Outfitters | 3635 | 1370 | 37.7 | 7.7 | 7.7 | 3.4 Marrowby & Vane | 1758 | 616 | 35.0 | 7.9 | 8.0 | 3.7 Studio Vrij | 631 | 264 | 41.8 | 7.9 | 7.8 | 2.3 Cinder Peak Sports Co. | 338 | 78 | 23.1 | 8.3 | 8.2 | 3.8 Rue Lafayette Studio | 187 | 69 | 36.9 | 7.4 | 6.9 | 8.7 Velvet Ash | 134 | 59 | 44.0 | 7.9 | 7.7 | 1.7 Gratlinie Outdoor | 232 | 58 | 25.0 | 7.7 | 7.8 | 8.6 Ampfeld Elektronik | 449 | 56 | 12.5 | 7.7 | 7.1 | 10.7 Petalcourt Beauty | 706 | 55 | 7.8 | 6.1 | 6.2 | 0.0 Fennel & Flint | 389 | 52 | 13.4 | 8.1 | 8.1 | 13.5 Oakhurst Tailoring | 165 | 48 | 29.1 | 7.6 | 7.7 | 10.4 Wiesgrund Wohnen | 296 | 44 | 14.9 | 7.5 | 7.3 | 6.8 Linden & Loom | 263 | 43 | 16.3 | 7.4 | 7.1 | 4.7 Brennquell Kaffee | 4195 | 40 | 1.0 | 8.1 | 8.6 | 2.5 Ostwind Mode | 122 | 37 | 30.3 | 8.1 | 8.4 | 0.0 Ballycorran Industrial Supply | 344 | 31 | 9.0 | 7.8 | 7.8 | 3.2 Ridgeback Gear | 108 | 26 | 24.1 | 8.0 | 8.1 | 0.0 Thistlecombe | 92 | 26 | 28.3 | 7.3 | 6.8 | 0.0 Fjordline Wear | 57 | 24 | 42.1 | 8.2 | 7.4 | 4.2 Coastline Interiors | 155 | 23 | 14.8 | 8.0 | 8.1 | 8.7 Atelier Nouveau | 64 | 21 | 32.8 | 8.2 | 8.2 | 4.8 Lumen Collective | 52 | 21 | 40.4 | 8.6 | 9.2 | 4.8 Noordzee Surfwear | 65 | 20 | 30.8 | 7.8 | 7.6 | 5.0 Strandgut | 47 | 20 | 42.6 | 7.6 | 7.0 | 10.0 (24 rows)
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
SELECT t.name as merchant_name, ord.orders_placed, ret.returns_requested, round(100.0 * ret.returns_requested / ord.orders_placed, 1) as return_rate_pct, rf.avg_days, rf.median_days, round(100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested, 1) as returns_rejected_pct ord.orders_placed AS orders_placed, ret.returns_requested AS returns_requested, 100.0 * ret.returns_requested / ord.orders_placed as return_rate_pct, rf.avg_days AS avg_days, rf.median_days AS median_days, 100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested as returns_rejected_pctFROM tenants tJOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= '2025-01-01' AND placed_at < '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.idJOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= '2025-01-01' AND requested_at < '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.idLEFT JOIN (SELECT tenant_id, round(avg(days),1) as avg_days, round(cast(percentile_cont(0.5) within group (order by days) as numeric),1) as median_days FROM (SELECT tenant_id, extract(epoch from (refunded_at - requested_at))/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= '2025-01-01' AND requested_at < '2026-01-01') xJOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= DATE '2025-01-01' AND placed_at < DATE '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.idJOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.idLEFT JOIN (SELECT tenant_id, avg(days) as avg_days, {{percentile_cont(0.5, days)}} as median_days FROM (SELECT tenant_id, {{seconds_between(requested_at, refunded_at)}}/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01') x GROUP BY tenant_id) rf ON rf.tenant_id = t.idLEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= '2025-01-01' AND requested_at < '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.idLEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.idWHERE ret.returns_requested >= 20ORDER BY ret.returns_requested DESC, t.name ASCLIMIT 50ORDER BY ret.returns_requested DESC NULLS FIRST, t.name ASC NULLS LAST, t.id NULLS LAST{{fetch_first(50)}}
SELECT t.name as merchant_name, ord.orders_placed AS orders_placed, ret.returns_requested AS returns_requested, 100.0 * ret.returns_requested / ord.orders_placed as return_rate_pct, rf.avg_days AS avg_days, rf.median_days AS median_days, 100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested as returns_rejected_pct FROM tenants t JOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= DATE '2025-01-01' AND placed_at < DATE '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.id JOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.id LEFT JOIN (SELECT tenant_id, avg(days) as avg_days, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY days) as median_days FROM (SELECT tenant_id, CAST(EXTRACT(EPOCH FROM (refunded_at - requested_at)) AS NUMERIC(18,6))/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01') x GROUP BY tenant_id) rf ON rf.tenant_id = t.id LEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.id WHERE ret.returns_requested >= 20 ORDER BY ret.returns_requested DESC NULLS FIRST, t.name ASC NULLS LAST, t.id NULLS LAST FETCH FIRST 50 ROWS ONLY
SELECT t.name as merchant_name, ord.orders_placed AS orders_placed, ret.returns_requested AS returns_requested, 100.0 * ret.returns_requested / ord.orders_placed as return_rate_pct, rf.avg_days AS avg_days, rf.median_days AS median_days, 100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested as returns_rejected_pct FROM tenants t JOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= DATE '2025-01-01' AND placed_at < DATE '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.id JOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.id LEFT JOIN (SELECT tenant_id, avg(days) as avg_days, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY days) as median_days FROM (SELECT tenant_id, CAST(EXTRACT(EPOCH FROM (refunded_at - requested_at)) AS NUMERIC(18,6))/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01') x GROUP BY tenant_id) rf ON rf.tenant_id = t.id LEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.id WHERE ret.returns_requested >= 20 ORDER BY ret.returns_requested DESC NULLS FIRST, t.name ASC NULLS LAST, t.id NULLS LAST LIMIT 50
SELECT t.name as merchant_name, ord.orders_placed AS orders_placed, ret.returns_requested AS returns_requested, 100.0 * ret.returns_requested / ord.orders_placed as return_rate_pct, rf.avg_days AS avg_days, rf.median_days AS median_days, 100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested as returns_rejected_pct FROM tenants t JOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= DATE '2025-01-01' AND placed_at < DATE '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.id JOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.id LEFT JOIN (SELECT tenant_id, avg(days) as avg_days, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(days AS DOUBLE PRECISION)) as median_days FROM (SELECT tenant_id, CAST(EXTRACT(EPOCH FROM (refunded_at - requested_at)) AS NUMERIC(18,6))/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01') x GROUP BY tenant_id) rf ON rf.tenant_id = t.id LEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.id WHERE ret.returns_requested >= 20 ORDER BY ret.returns_requested DESC NULLS FIRST, t.name ASC NULLS LAST, t.id NULLS LAST FETCH FIRST 50 ROWS ONLY
SELECT t.name as merchant_name, ord.orders_placed AS orders_placed, ret.returns_requested AS returns_requested, 100.0 * ret.returns_requested / ord.orders_placed as return_rate_pct, rf.avg_days AS avg_days, rf.median_days AS median_days, 100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested as returns_rejected_pct FROM tenants t JOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= DATE '2025-01-01' AND placed_at < DATE '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.id JOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.id LEFT JOIN (SELECT tenant_id, avg(days) as avg_days, quantileExactInclusive(0.5)(toFloat64(days)) as median_days FROM (SELECT tenant_id, CAST(divideDecimal(toDecimal128(dateDiff('microsecond', requested_at, refunded_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6)))/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01') x GROUP BY tenant_id) rf ON rf.tenant_id = t.id LEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= DATE '2025-01-01' AND requested_at < DATE '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.id WHERE ret.returns_requested >= 20 ORDER BY ret.returns_requested DESC NULLS FIRST, t.name ASC NULLS LAST, t.id NULLS LAST FETCH FIRST 50 ROWS ONLY
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 Mateo Ruiz (analyst for the VP Customer Experience): Title: Returns and refunds experience by merchant, 2025 Question: Does the returns/refund experience vary a lot across merchants, and does any merchant stand out as a problem spot the way Studio Vrij did on support tickets? Grain: one row per tenant (merchant), among tenants with at least 20 returns requested in 2025 Columns: - merchant name - orders placed, 2025 - returns requested, 2025 - return rate (%) - avg days requested-to-refunded - median days requested-to-refunded - returns rejected (%) Details: Source: order_returns joined to tenants (for name) and orders (for the orders-placed denominator). Window: order_returns.requested_at and orders.placed_at both in 2025-01-01 through 2025-12-31. Return rate = tenant's returns requested in 2025 / tenant's orders placed in 2025, as a percentage rounded to 1 decimal (same approximation caveat as the platform-wide returns report: a given return may relate to an order placed outside the year, so treat this as a rough proxy, not a precise cohort rate). avg/median days requested-to-refunded = extract(epoch from refunded_at - requested_at)/86400 over that tenant's returns with status='refunded' and refunded_at not null in 2025, rounded to 1 decimal. returns rejected (%) = tenant's order_returns with status='rejected' in 2025 / tenant's returns requested in 2025, percentage rounded to 1 decimal. Only include tenants with >=20 returns requested in 2025. Order by returns requested descending, tie-break by merchant name ascending. Cap at 50 printed rows per house standard; note total qualifying tenant count in the footer. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT t.name as merchant_name, ord.orders_placed, ret.returns_requested, round(100.0 * ret.returns_requested / ord.orders_placed, 1) as return_rate_pct, rf.avg_days, rf.median_days, round(100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested, 1) as returns_rejected_pct FROM tenants t JOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= '2025-01-01' AND placed_at < '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.id JOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= '2025-01-01' AND requested_at < '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.id LEFT JOIN (SELECT tenant_id, round(avg(days),1) as avg_days, round(cast(percentile_cont(0.5) within group (order by days) as numeric),1) as median_days FROM (SELECT tenant_id, extract(epoch from (refunded_at - requested_at))/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= '2025-01-01' AND requested_at < '2026-01-01') x GROUP BY tenant_id) rf ON rf.tenant_id = t.id LEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= '2025-01-01' AND requested_at < '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.id WHERE ret.returns_requested >= 20 ORDER BY ret.returns_requested DESC, t.name ASC LIMIT 50
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: merchant_name, orders_placed, returns_requested, return_rate_pct, avg_days, median_days, returns_rejected_pct
submitSELECT t.name as merchant_name, ord.orders_placed, ret.returns_requested, round(100.0 * ret.returns_requested / ord.orders_placed, 1) as return_rate_pct, rf.avg_days, rf.median_days, round(100.0 * coalesce(rej.rejected_count,0) / ret.returns_requested, 1) as returns_rejected_pct FROM tenants t JOIN (SELECT tenant_id, count(*) as orders_placed FROM orders WHERE placed_at >= '2025-01-01' AND placed_at < '2026-01-01' GROUP BY tenant_id) ord ON ord.tenant_id = t.id JOIN (SELECT tenant_id, count(*) as returns_requested FROM order_returns WHERE requested_at >= '2025-01-01' AND requested_at < '2026-01-01' GROUP BY tenant_id) ret ON ret.tenant_id = t.id LEFT JOIN (SELECT tenant_id, round(avg(days),1) as avg_days, round(cast(percentile_cont(0.5) within group (order by days) as numeric),1) as median_days FROM (SELECT tenant_id, extract(epoch from (refunded_at - requested_at))/86400 as days FROM order_returns WHERE status='refunded' AND refunded_at IS NOT NULL AND requested_at >= '2025-01-01' AND requested_at < '2026-01-01') x GROUP BY tenant_id) rf ON rf.tenant_id = t.id LEFT JOIN (SELECT tenant_id, count(*) as rejected_count FROM order_returns WHERE status='rejected' AND requested_at >= '2025-01-01' AND requested_at < '2026-01-01' GROUP BY tenant_id) rej ON rej.tenant_id = t.id WHERE ret.returns_requested >= 20 ORDER BY ret.returns_requested DESC, t.name ASC LIMIT 50
Submitted to tonight's batch. Your work on this request is done.