Queries · q288 · VP Product · week 2, night 1
“Is the cart-stage drop-off getting better, worse, or staying flat over the past year?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
Week 1
We are redesigning the shopper journey for 2026. We need to understand the whole path, from the first visit to checkout, delivery, returns and the next purchase, and where shoppers drop out and why.
The manager, at the start of week 2
Subject: Good start — let's dig into the cart leak
Emeka, this is exactly the kind of clarity I needed. The cart-stage bleed is the headline, and I agree we should lead with it — but a two-thirds drop-off that lingers for days, converting at under 20%, isn't something we can act on until we know why people stall. "Add cart recovery emails" is a guess until we understand the shape of the hesitation. That's my main ask for this week.
I also want to flag one thing before we build a roadmap slide on repeat purchase and returns: the return-vs-repeat gap and the delivery/returns pattern are both flagged as not-yet-causal, confounded by vertical. I don't want to present an 8-point "returns hurt loyalty" number to the exec team if it's really just "apparel behaves differently." Let's firm that up before it goes in a deck, but it's not urgent this week.
Questions for this week:
Keep the returns/delivery vertical-control work and the causal retention question on the list for whenever a slot frees up, but the cart story is what I want to walk into the roadmap review with real conviction on.
Thanks, Emeka — strong week.
Jonas
The analyst · week 2, night 1, request 2 of 3
Jonas's email has an implicit priority: Q1 (what's in a stalled cart) and Q2 (is the bleed trending worse) are "my main ask for this week"; Q3 (organic recovery) is also wanted but reads as a natural follow-up once we understand the shape; Q4 is explicitly "a quick gut-check... no need for deep investigation." So tonight I'm doing Q1, Q2, and a lightweight version of Q4, saving Q3 for night 2 since it benefits from what we learn tonight (e.g., whether stalled carts later convert with a discount attached is itself a clue about organic recovery).
Design notes:
All three use the 6 retail tenants (Q1, Q2) or 4 subscription tenants (Q4) established in last week's work, so the engineer has a concrete, named population to filter on rather than re-deriving "top merchants."
The engineer · 2 dry runs on the empty schema, then the submission
SELECT to_char(date_trunc('month', c.created_at), 'YYYY-MM') AS month, COUNT(*) AS carts_created, ROUND(100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*), 1) AS overall_conversion_pct, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*), 1) AS pct_resolved_within_1_hour, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*), 1) AS pct_resolved_1_7_days, ROUND(CAST(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (c.converted_at - c.created_at))/86400.0) FILTER (WHERE c.status = 'converted') AS numeric), 1) AS median_days_to_convert FROM carts c JOIN tenants t ON t.id = c.tenant_id WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01' GROUP BY date_trunc('month', c.created_at) ORDER BY date_trunc('month', c.created_at)
SELECT to_char(date_trunc('month', c.created_at), 'YYYY-MM') AS month, cd.month_label AS month, COUNT(*) AS carts_created, ROUND(100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*), 1) AS overall_conversion_pct, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*), 1) AS pct_resolved_within_1_hour, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*), 1) AS pct_resolved_1_7_days, ROUND(CAST(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (c.converted_at - c.created_at))/86400.0) FILTER (WHERE c.status = 'converted') AS numeric), 1) AS median_days_to_convertFROM carts cJOIN tenants t ON t.id = c.tenant_idJOIN calendar_days cd ON cd.calendar_date = CAST(c.created_at AS date)WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01'GROUP BY date_trunc('month', c.created_at)ORDER BY date_trunc('month', c.created_at)GROUP BY cd.month_labelORDER BY cd.month_label
“This compiles correctly with the expected columns. Submitting.”
“Submitted. The report groups 2025 carts for the six named retail tenants by calendar month (via calendar_days.month_label), with carts created, overall cart→order conversion %, share resolved within 1 hour, share resolved in 1–7 days, and the median days-to-convert for converted carts — ordered January through December 2025.”
As Emeka Obi saw it, from the small dataset the agents worked on
month | carts_created | overall_conversion_pct | pct_resolved_within_1_hour | pct_resolved_1_7_days | median_days_to_convert --------+---------------+------------------------+----------------------------+-----------------------+----------------------- 2025-01 | 1637 | 37.9 | 30.9 | 68.5 | 0.0 2025-02 | 1136 | 35.1 | 26.5 | 73.0 | 0.0 2025-03 | 1388 | 35.4 | 30.0 | 69.5 | 0.0 2025-04 | 1367 | 35.5 | 28.6 | 71.2 | 0.0 2025-05 | 1691 | 35.7 | 29.1 | 70.4 | 0.0 2025-06 | 1337 | 35.2 | 29.8 | 69.8 | 0.0 2025-07 | 1640 | 34.3 | 27.9 | 71.3 | 0.0 2025-08 | 1345 | 34.0 | 28.0 | 71.7 | 0.0 2025-09 | 1546 | 35.8 | 29.6 | 69.9 | 0.0 2025-10 | 1919 | 32.4 | 28.6 | 71.1 | 0.0 2025-11 | 2898 | 37.7 | 32.0 | 67.5 | 0.0 2025-12 | 3293 | 36.0 | 30.0 | 62.5 | 0.0 (12 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
SELECT cd.month_label AS month, COUNT(*) AS carts_created, ROUND(100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*), 1) AS overall_conversion_pct, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*), 1) AS pct_resolved_within_1_hour, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*), 1) AS pct_resolved_1_7_days, ROUND(CAST(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (c.converted_at - c.created_at))/86400.0) FILTER (WHERE c.status = 'converted') AS numeric), 1) AS median_days_to_convert 100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*) AS overall_conversion_pct, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*) AS pct_resolved_within_1_hour, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*) AS pct_resolved_1_7_days, {{percentile_cont_filter(0.5, {{seconds_between(c.created_at, c.converted_at)}}/86400.0, c.status = 'converted')}} AS median_days_to_convertFROM carts cJOIN tenants t ON t.id = c.tenant_idJOIN calendar_days cd ON cd.calendar_date = CAST(c.created_at AS date)WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01'GROUP BY cd.month_labelORDER BY cd.month_labelORDER BY cd.month_label NULLS LAST
SELECT cd.month_label AS month, COUNT(*) AS carts_created, 100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*) AS overall_conversion_pct, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*) AS pct_resolved_within_1_hour, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*) AS pct_resolved_1_7_days, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(EXTRACT(EPOCH FROM (c.converted_at - c.created_at)) AS NUMERIC(18,6))/86400.0) FILTER (WHERE c.status = 'converted') AS median_days_to_convert FROM carts c JOIN tenants t ON t.id = c.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(c.created_at AS date) WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01' GROUP BY cd.month_label ORDER BY cd.month_label NULLS LAST
SELECT cd.month_label AS month, COUNT(*) AS carts_created, 100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*) AS overall_conversion_pct, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*) AS pct_resolved_within_1_hour, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*) AS pct_resolved_1_7_days, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CASE WHEN c.status = 'converted' THEN CAST(EXTRACT(EPOCH FROM (c.converted_at - c.created_at)) AS NUMERIC(18,6))/86400.0 END) AS median_days_to_convert FROM carts c JOIN tenants t ON t.id = c.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(c.created_at AS date) WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01' GROUP BY cd.month_label ORDER BY cd.month_label NULLS LAST
SELECT cd.month_label AS month, COUNT(*) AS carts_created, 100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*) AS overall_conversion_pct, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*) AS pct_resolved_within_1_hour, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*) AS pct_resolved_1_7_days, PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(CAST(EXTRACT(EPOCH FROM (c.converted_at - c.created_at)) AS NUMERIC(18,6))/86400.0 AS DOUBLE PRECISION)) FILTER (WHERE c.status = 'converted') AS median_days_to_convert FROM carts c JOIN tenants t ON t.id = c.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(c.created_at AS date) WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01' GROUP BY cd.month_label ORDER BY cd.month_label NULLS LAST
SELECT cd.month_label AS month, COUNT(*) AS carts_created, 100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*) AS overall_conversion_pct, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*) AS pct_resolved_within_1_hour, 100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*) AS pct_resolved_1_7_days, quantileExactInclusiveIf(0.5)(toFloat64(CAST(divideDecimal(toDecimal128(dateDiff('microsecond', c.created_at, c.converted_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6)))/86400.0), c.status = 'converted') AS median_days_to_convert FROM carts c JOIN tenants t ON t.id = c.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(c.created_at AS date) WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01' GROUP BY cd.month_label ORDER BY cd.month_label 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 Emeka Obi (analyst for the VP Product): Title: Cart bleed trend by month, 2025 Question: Is the cart-stage drop-off getting better, worse, or staying flat over the past year? Grain: one row per calendar month of cart creation, 2025 Columns: - month - carts created - overall cart-to-order conversion pct - pct resolved within 1 hour - pct resolved in 1-7 days - median days to convert (converted carts only) Details: Tenants: the 6 retail merchants (Kibblewick, Studio Vrij, Petalcourt Beauty, Marrowby & Vane, Cragsley Outfitters, Ampfeld Elektronik). Population: carts with created_at in 2025-01-01 to 2025-12-31, grouped by calendar month (use calendar_days or month_label-style grouping). overall_conversion_pct = share of carts in the month with status='converted', rounded 1 decimal. pct_resolved_within_1_hour and pct_resolved_1_7_days use the same resolution_time definition as the duration-bucket reports (coalesce(converted_at, abandoned_at, expires_at) - created_at), rounded 1 decimal. median_days_to_convert = median of (converted_at - created_at) in days, among carts with status='converted' in that month, rounded 1 decimal. Order rows by month ascending, January through December 2025. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT to_char(date_trunc('month', c.created_at), 'YYYY-MM') AS month, COUNT(*) AS carts_created, ROUND(100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*), 1) AS overall_conversion_pct, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*), 1) AS pct_resolved_within_1_hour, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*), 1) AS pct_resolved_1_7_days, ROUND(CAST(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (c.converted_at - c.created_at))/86400.0) FILTER (WHERE c.status = 'converted') AS numeric), 1) AS median_days_to_convert FROM carts c JOIN tenants t ON t.id = c.tenant_id WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01' GROUP BY date_trunc('month', c.created_at) ORDER BY date_trunc('month', c.created_at)
STATUS: REJECTED - does not compile: Catalog Error: Scalar Function with name to_char does not exist!
dry_runSELECT cd.month_label AS month, COUNT(*) AS carts_created, ROUND(100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*), 1) AS overall_conversion_pct, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*), 1) AS pct_resolved_within_1_hour, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*), 1) AS pct_resolved_1_7_days, ROUND(CAST(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (c.converted_at - c.created_at))/86400.0) FILTER (WHERE c.status = 'converted') AS numeric), 1) AS median_days_to_convert FROM carts c JOIN tenants t ON t.id = c.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(c.created_at AS date) WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01' GROUP BY cd.month_label ORDER BY cd.month_label
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: month, carts_created, overall_conversion_pct, pct_resolved_within_1_hour, pct_resolved_1_7_days, median_days_to_convert
submitSELECT cd.month_label AS month, COUNT(*) AS carts_created, ROUND(100.0 * COUNT(*) FILTER (WHERE c.status = 'converted') / COUNT(*), 1) AS overall_conversion_pct, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '1 hour') / COUNT(*), 1) AS pct_resolved_within_1_hour, ROUND(100.0 * COUNT(*) FILTER (WHERE COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at > INTERVAL '1 hour' AND COALESCE(c.converted_at, c.abandoned_at, c.expires_at) - c.created_at <= INTERVAL '7 days') / COUNT(*), 1) AS pct_resolved_1_7_days, ROUND(CAST(PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY EXTRACT(EPOCH FROM (c.converted_at - c.created_at))/86400.0) FILTER (WHERE c.status = 'converted') AS numeric), 1) AS median_days_to_convert FROM carts c JOIN tenants t ON t.id = c.tenant_id JOIN calendar_days cd ON cd.calendar_date = CAST(c.created_at AS date) WHERE t.name IN ('Kibblewick','Studio Vrij','Petalcourt Beauty','Marrowby & Vane','Cragsley Outfitters','Ampfeld Elektronik') AND c.created_at >= DATE '2025-01-01' AND c.created_at < DATE '2026-01-01' GROUP BY cd.month_label ORDER BY cd.month_label
Submitted to tonight's batch. Your work on this request is done.