Queries · q250 · VP Customer Experience · week 2, night 3
“Does urgent-ticket resolution specifically get worse in the same quarters where each merchant's ticket-per-agent load spiked?”
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 2
Subject: Good progress — now I want to understand the "why" behind the hotspots
Mateo — this is exactly the kind of baseline I needed, thank you. Two things from your memo and Mia's notes are sitting with me:
First, the speed→satisfaction link is strong and now confirmed two ways (yours and Mia's numbers line up almost exactly), so I believe it. But I don't yet know if slowness causes the dissatisfaction or if the worst merchants are simply worse across the board (harder products, different shopper expectations, something else) and speed is just the symptom we can see. That matters a lot for what I recommend as the fix.
Second, Mia's finding that three merchants account for a third of all tickets and the worst response times lines up with what you found independently — that's a strong signal, not a coincidence, and it's where I want to spend our next slots.
Questions for this week:
Let's prioritize #1 and #2 this week — those feel most actionable. #3 and #4 can wait if slots are tight.
Thanks, Daniel
The analyst · week 2, night 3, request 1 of 3
Night 2 results are decisive on complexity: Cragsley (1.36 tags/ticket, 3.46 messages/ticket) and Marrowby (1.43, 3.29) are actually at the LOW end of complexity among all 22 qualifying merchants - Studio Vrij is mid-pack (1.64, 3.71). So "harder issues" is refuted as an explanation; it really does look like a volume/staffing story. The SLA-target report also refutes the "unrealistic target" theory for the broad urgent-miss problem - every merchant evaluated uses the identical 8-hour urgent target, so the wide spread in miss rates (14%-100%) reflects genuine differences in execution speed, not target calibration. The quarterly load report is the key new finding: all three hotspots (and comparison peer Grind Collective) run a completely flat agent headcount all four quarters, but ticket volume surges hard in specific quarters - Cragsley and Marrowby both spike in Q4 (tickets-per-agent roughly doubles vs Q1-Q3), Studio Vrij spikes specifically in Q2 (the already-known fulfillment incident), and Grind Collective also spikes in Q4 even harder than Cragsley. That's a strong lead: nobody scales support staff for volume surges, and it's not unique to the three named merchants. I want to (1) confirm urgent-ticket performance specifically degrades in the same quarters where each merchant's load spiked (a within-merchant causal test), (2) check whether this "flat staffing through a seasonal/incident surge" pattern is common across the whole qualifying book, not just these four - that determines if it's a platform-level guidance opportunity, and (3) characterize whether the big three's inflated average (vs. median) urgent resolution time is a few catastrophic outlier tickets or generally slow handling, since that changes what the fix would look like (triage/escalation vs. core speed).
The engineer · 1 dry run on the empty schema, then the submission
WITH q AS ( SELECT 1 AS q_num, date '2025-01-01' AS q_start, date '2025-04-01' AS q_end UNION ALL SELECT 2, date '2025-04-01', date '2025-07-01' UNION ALL SELECT 3, date '2025-07-01', date '2025-10-01' UNION ALL SELECT 4, date '2025-10-01', date '2026-01-01' ), m AS ( SELECT id, name FROM tenants WHERE name IN ('Cragsley Outfitters','Marrowby & Vane','Studio Vrij','Grind Collective') ) SELECT m.name AS merchant_name, '2025-Q' || q.q_num AS quarter, count(tk.id) AS urgent_tickets_evaluated, round( 100.0 * sum( case when tk.id is not null and (tk.resolved_at is null or tk.resolved_at > tk.created_at + (sp.resolution_target_minutes || ' minutes')::interval) then 1 else 0 end )::numeric / nullif(count(tk.id), 0) , 1) AS urgent_miss_rate_pct, round( avg(case when tk.resolved_at is not null then extract(epoch from tk.resolved_at - tk.created_at) / 3600.0 end)::numeric , 1) AS avg_actual_resolution_hours, round( percentile_cont(0.5) within group ( order by case when tk.resolved_at is not null then extract(epoch from tk.resolved_at - tk.created_at) / 3600.0 end )::numeric , 1) AS median_actual_resolution_hours FROM m CROSS JOIN q LEFT JOIN tickets tk ON tk.tenant_id = m.id AND tk.priority = 'urgent' AND tk.sla_policy_id IS NOT NULL AND tk.created_at >= q.q_start AND tk.created_at < q.q_end LEFT JOIN sla_policies sp ON sp.id = tk.sla_policy_id GROUP BY m.name, q.q_num ORDER BY m.name ASC, q.q_num ASC
“Query compiles and returns the expected columns. Submitting.”
“Submitted. The report builds a complete 4-tenant × 4-quarter (2025-Q1..Q4) grid via a cross join, left-joining urgent/evaluable tickets so quarters with zero evaluable urgent tickets still appear with explicit zero/NULL values rather than being dropped — satisfying the "expect 16 rows, note zero-ticket quarters" requirement directly in the data rather than as a caveat.”
As Mateo Ruiz saw it, from the small dataset the agents worked on
merchant_name | quarter | urgent_tickets_evaluated | urgent_miss_rate_pct | avg_actual_resolution_hours | median_actual_resolution_hours --------------------+---------+--------------------------+----------------------+-----------------------------+------------------------------- Cragsley Outfitters | 2025-Q1 | 14 | 71.4 | 30.0 | 10.5 Cragsley Outfitters | 2025-Q2 | 13 | 53.8 | 18.0 | 14.0 Cragsley Outfitters | 2025-Q3 | 8 | 50.0 | 23.1 | 9.8 Cragsley Outfitters | 2025-Q4 | 15 | 80.0 | 36.6 | 21.5 Grind Collective | 2025-Q1 | 1 | 0.0 | 0.8 | 0.8 Grind Collective | 2025-Q2 | 1 | 0.0 | 0.9 | 0.9 Grind Collective | 2025-Q3 | 1 | 0.0 | 1.0 | 1.0 Grind Collective | 2025-Q4 | 3 | 33.3 | 7.0 | 7.1 Marrowby & Vane | 2025-Q1 | 8 | 62.5 | 43.6 | 24.8 Marrowby & Vane | 2025-Q2 | 2 | 50.0 | 16.9 | 16.9 Marrowby & Vane | 2025-Q3 | 6 | 50.0 | 8.8 | 6.4 Marrowby & Vane | 2025-Q4 | 17 | 76.5 | 139.5 | 87.2 Studio Vrij | 2025-Q1 | 4 | 50.0 | 17.4 | 10.7 Studio Vrij | 2025-Q2 | 2 | 100.0 | 219.5 | 219.5 Studio Vrij | 2025-Q3 | 2 | 100.0 | 109.0 | 109.0 Studio Vrij | 2025-Q4 | 3 | 33.3 | 3.9 | 1.2 (16 rows)
The memo and the queries that followed; links found by title, so inferred
Later queries that build on this report:
It builds on:
Changed only where the benchmark needs the same result on every system
n * INTERVAL '1' MINUTE is not supported by MySQL, T-SQL or SQLite either, so adding minutes to a timestamp is a placeholder whose default is the standard form (docs/RUNNER.md, Dialects).WITH q AS ( SELECT 1 AS q_num, date '2025-01-01' AS q_start, date '2025-04-01' AS q_end UNION ALL SELECT 2, date '2025-04-01', date '2025-07-01' UNION ALL SELECT 3, date '2025-07-01', date '2025-10-01' UNION ALL SELECT 4, date '2025-10-01', date '2026-01-01'),m AS ( SELECT id, name FROM tenants WHERE name IN ('Cragsley Outfitters','Marrowby & Vane','Studio Vrij','Grind Collective'))SELECT m.name AS merchant_name, '2025-Q' || q.q_num AS quarter, count(tk.id) AS urgent_tickets_evaluated, round( 100.0 * sum( case when tk.id is not null and (tk.resolved_at is null or tk.resolved_at > tk.created_at + (sp.resolution_target_minutes || ' minutes')::interval) then 1 else 0 end )::numeric / nullif(count(tk.id), 0) , 1) AS urgent_miss_rate_pct, round( avg(case when tk.resolved_at is not null then extract(epoch from tk.resolved_at - tk.created_at) / 3600.0 end)::numeric , 1) AS avg_actual_resolution_hours, round( percentile_cont(0.5) within group ( order by case when tk.resolved_at is not null then extract(epoch from tk.resolved_at - tk.created_at) / 3600.0 end )::numeric , 1) AS median_actual_resolution_hours 100.0 * CAST(sum( case when tk.id is not null and (tk.resolved_at is null or tk.resolved_at > {{add_minutes(tk.created_at, sp.resolution_target_minutes)}}) then 1 else 0 end ) AS numeric(18,6)) / nullif(count(tk.id), 0) AS urgent_miss_rate_pct, avg(case when tk.resolved_at is not null then {{seconds_between(tk.created_at, tk.resolved_at)}} / 3600.0 end) AS avg_actual_resolution_hours, {{percentile_cont(0.5, case when tk.resolved_at is not null then {{seconds_between(tk.created_at, tk.resolved_at)}} / 3600.0 end)}} AS median_actual_resolution_hoursFROM mCROSS JOIN qLEFT JOIN tickets tk ON tk.tenant_id = m.id AND tk.priority = 'urgent' AND tk.sla_policy_id IS NOT NULL AND tk.created_at >= q.q_start AND tk.created_at < q.q_endLEFT JOIN sla_policies sp ON sp.id = tk.sla_policy_idGROUP BY m.name, q.q_numORDER BY m.name ASC, q.q_num ASCORDER BY m.name ASC NULLS LAST, q.q_num ASC NULLS LAST
WITH q AS ( SELECT 1 AS q_num, date '2025-01-01' AS q_start, date '2025-04-01' AS q_end UNION ALL SELECT 2, date '2025-04-01', date '2025-07-01' UNION ALL SELECT 3, date '2025-07-01', date '2025-10-01' UNION ALL SELECT 4, date '2025-10-01', date '2026-01-01' ), m AS ( SELECT id, name FROM tenants WHERE name IN ('Cragsley Outfitters','Marrowby & Vane','Studio Vrij','Grind Collective') ) SELECT m.name AS merchant_name, '2025-Q' || q.q_num AS quarter, count(tk.id) AS urgent_tickets_evaluated, 100.0 * CAST(sum( case when tk.id is not null and (tk.resolved_at is null or tk.resolved_at > tk.created_at + sp.resolution_target_minutes * INTERVAL '1' MINUTE) then 1 else 0 end ) AS numeric(18,6)) / nullif(count(tk.id), 0) AS urgent_miss_rate_pct, avg(case when tk.resolved_at is not null then CAST(EXTRACT(EPOCH FROM (tk.resolved_at - tk.created_at)) AS NUMERIC(18,6)) / 3600.0 end) AS avg_actual_resolution_hours, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY case when tk.resolved_at is not null then CAST(EXTRACT(EPOCH FROM (tk.resolved_at - tk.created_at)) AS NUMERIC(18,6)) / 3600.0 end) AS median_actual_resolution_hours FROM m CROSS JOIN q LEFT JOIN tickets tk ON tk.tenant_id = m.id AND tk.priority = 'urgent' AND tk.sla_policy_id IS NOT NULL AND tk.created_at >= q.q_start AND tk.created_at < q.q_end LEFT JOIN sla_policies sp ON sp.id = tk.sla_policy_id GROUP BY m.name, q.q_num ORDER BY m.name ASC NULLS LAST, q.q_num ASC NULLS LAST
WITH q AS ( SELECT 1 AS q_num, date '2025-01-01' AS q_start, date '2025-04-01' AS q_end UNION ALL SELECT 2, date '2025-04-01', date '2025-07-01' UNION ALL SELECT 3, date '2025-07-01', date '2025-10-01' UNION ALL SELECT 4, date '2025-10-01', date '2026-01-01' ), m AS ( SELECT id, name FROM tenants WHERE name IN ('Cragsley Outfitters','Marrowby & Vane','Studio Vrij','Grind Collective') ) SELECT m.name AS merchant_name, '2025-Q' || q.q_num AS quarter, count(tk.id) AS urgent_tickets_evaluated, 100.0 * CAST(sum( case when tk.id is not null and (tk.resolved_at is null or tk.resolved_at > tk.created_at + sp.resolution_target_minutes * INTERVAL '1' MINUTE) then 1 else 0 end ) AS numeric(18,6)) / nullif(count(tk.id), 0) AS urgent_miss_rate_pct, avg(case when tk.resolved_at is not null then CAST(EXTRACT(EPOCH FROM (tk.resolved_at - tk.created_at)) AS NUMERIC(18,6)) / 3600.0 end) AS avg_actual_resolution_hours, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(case when tk.resolved_at is not null then CAST(EXTRACT(EPOCH FROM (tk.resolved_at - tk.created_at)) AS NUMERIC(18,6)) / 3600.0 end AS DOUBLE PRECISION)) AS median_actual_resolution_hours FROM m CROSS JOIN q LEFT JOIN tickets tk ON tk.tenant_id = m.id AND tk.priority = 'urgent' AND tk.sla_policy_id IS NOT NULL AND tk.created_at >= q.q_start AND tk.created_at < q.q_end LEFT JOIN sla_policies sp ON sp.id = tk.sla_policy_id GROUP BY m.name, q.q_num ORDER BY m.name ASC NULLS LAST, q.q_num ASC NULLS LAST
WITH q AS ( SELECT 1 AS q_num, date '2025-01-01' AS q_start, date '2025-04-01' AS q_end UNION ALL SELECT 2, date '2025-04-01', date '2025-07-01' UNION ALL SELECT 3, date '2025-07-01', date '2025-10-01' UNION ALL SELECT 4, date '2025-10-01', date '2026-01-01' ), m AS ( SELECT id, name FROM tenants WHERE name IN ('Cragsley Outfitters','Marrowby & Vane','Studio Vrij','Grind Collective') ) SELECT m.name AS merchant_name, '2025-Q' || q.q_num AS quarter, count(tk.id) AS urgent_tickets_evaluated, 100.0 * CAST(sum( case when tk.id is not null and (tk.resolved_at is null or tk.resolved_at > addMinutes(tk.created_at, sp.resolution_target_minutes)) then 1 else 0 end ) AS numeric(18,6)) / nullif(count(tk.id), 0) AS urgent_miss_rate_pct, avg(case when tk.resolved_at is not null then CAST(divideDecimal(toDecimal128(dateDiff('microsecond', tk.created_at, tk.resolved_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0 end) AS avg_actual_resolution_hours, quantileExactInclusive(0.5)(toFloat64(case when tk.resolved_at is not null then CAST(divideDecimal(toDecimal128(dateDiff('microsecond', tk.created_at, tk.resolved_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0 end)) AS median_actual_resolution_hours FROM m CROSS JOIN q LEFT JOIN tickets tk ON tk.tenant_id = m.id AND tk.priority = 'urgent' AND tk.sla_policy_id IS NOT NULL AND tk.created_at >= q.q_start AND tk.created_at < q.q_end LEFT JOIN sla_policies sp ON sp.id = tk.sla_policy_id GROUP BY m.name, q.q_num ORDER BY m.name ASC NULLS LAST, q.q_num 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 Mateo Ruiz (analyst for the VP Customer Experience): Title: Quarterly urgent-ticket performance, hotspot merchants vs comparison peer Question: Does urgent-ticket resolution specifically get worse in the same quarters where each merchant's ticket-per-agent load spiked? Grain: one row per tenant and calendar quarter in 2025, for Cragsley Outfitters, Marrowby & Vane, Studio Vrij, and Grind Collective, urgent-priority tickets only Columns: - merchant name - quarter (2025-Q1..Q4) - urgent tickets evaluated (sla_policy_id not null) - urgent miss rate (%) - avg actual resolution hours (urgent) - median actual resolution hours (urgent) Details: Source: tickets (priority='urgent') joined to sla_policies via tickets.sla_policy_id, filtered to the four named tenants. Grain the quarter on tickets.created_at within 2025. Exclude tickets with null sla_policy_id from all columns (same 'evaluable' definition as prior urgent-SLA reports). 'Miss rate' = tickets missing the resolution target (resolved_at null as of 2025-12-31, or resolved_at later than created_at + resolution_target_minutes), as a percentage of evaluable urgent tickets that quarter, rounded to 1 decimal. avg/median actual resolution hours = extract(epoch from resolved_at - created_at)/3600 over evaluable urgent tickets with resolved_at not null that quarter, rounded to 1 decimal (show NULL if zero such tickets). Order by merchant name ascending, then quarter ascending. Expect 16 rows; note if any quarter has zero evaluable urgent tickets for a tenant. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH q AS ( SELECT 1 AS q_num, date '2025-01-01' AS q_start, date '2025-04-01' AS q_end UNION ALL SELECT 2, date '2025-04-01', date '2025-07-01' UNION ALL SELECT 3, date '2025-07-01', date '2025-10-01' UNION ALL SELECT 4, date '2025-10-01', date '2026-01-01' ), m AS ( SELECT id, name FROM tenants WHERE name IN ('Cragsley Outfitters','Marrowby & Vane','Studio Vrij','Grind Collective') ) SELECT m.name AS merchant_name, '2025-Q' || q.q_num AS quarter, count(tk.id) AS urgent_tickets_evaluated, round( 100.0 * sum( case when tk.id is not null and (tk.resolved_at is null or tk.resolved_at > tk.created_at + (sp.resolution_target_minutes || ' minutes')::interval) then 1 else 0 end )::numeric / nullif(count(tk.id), 0) , 1) AS urgent_miss_rate_pct, round( avg(case when tk.resolved_at is not null then extract(epoch from tk.resolved_at - tk.created_at) / 3600.0 end)::numeric , 1) AS avg_actual_resolution_hours, round( percentile_cont(0.5) within group ( order by case when tk.resolved_at is not null then extract(epoch from tk.resolved_at - tk.created_at) / 3600.0 end )::numeric , 1) AS median_actual_resolution_hours FROM m CROSS JOIN q LEFT JOIN tickets tk ON tk.tenant_id = m.id AND tk.priority = 'urgent' AND tk.sla_policy_id IS NOT NULL AND tk.created_at >= q.q_start AND tk.created_at < q.q_end LEFT JOIN sla_policies sp ON sp.id = tk.sla_policy_id GROUP BY m.name, q.q_num ORDER BY m.name ASC, q.q_num ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: merchant_name, quarter, urgent_tickets_evaluated, urgent_miss_rate_pct, avg_actual_resolution_hours, median_actual_resolution_hours
submitWITH q AS ( SELECT 1 AS q_num, date '2025-01-01' AS q_start, date '2025-04-01' AS q_end UNION ALL SELECT 2, date '2025-04-01', date '2025-07-01' UNION ALL SELECT 3, date '2025-07-01', date '2025-10-01' UNION ALL SELECT 4, date '2025-10-01', date '2026-01-01' ), m AS ( SELECT id, name FROM tenants WHERE name IN ('Cragsley Outfitters','Marrowby & Vane','Studio Vrij','Grind Collective') ) SELECT m.name AS merchant_name, '2025-Q' || q.q_num AS quarter, count(tk.id) AS urgent_tickets_evaluated, round( 100.0 * sum( case when tk.id is not null and (tk.resolved_at is null or tk.resolved_at > tk.created_at + (sp.resolution_target_minutes || ' minutes')::interval) then 1 else 0 end )::numeric / nullif(count(tk.id), 0) , 1) AS urgent_miss_rate_pct, round( avg(case when tk.resolved_at is not null then extract(epoch from tk.resolved_at - tk.created_at) / 3600.0 end)::numeric , 1) AS avg_actual_resolution_hours, round( percentile_cont(0.5) within group ( order by case when tk.resolved_at is not null then extract(epoch from tk.resolved_at - tk.created_at) / 3600.0 end )::numeric , 1) AS median_actual_resolution_hours FROM m CROSS JOIN q LEFT JOIN tickets tk ON tk.tenant_id = m.id AND tk.priority = 'urgent' AND tk.sla_policy_id IS NOT NULL AND tk.created_at >= q.q_start AND tk.created_at < q.q_end LEFT JOIN sla_policies sp ON sp.id = tk.sla_policy_id GROUP BY m.name, q.q_num ORDER BY m.name ASC, q.q_num ASC
Submitted to tonight's batch. Your work on this request is done.