Queries · q290 · VP Product · week 2, night 2
“Did cart conversion dip during the week cart-reminder emails broke (week of Nov 24, 2025), suggesting those reminders meaningfully nudge stalled carts toward purchase?”
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 2, request 1 of 3
Reading last night's reports:
Report 1 (cart contents) is the headline for Q1: fast-converting carts (<1hr) average 2.79 items and €242 (median €131), while stalled carts (1-24h, 1-7d) average only 1.6-1.8 items and €169-210 (median €75-84). So hesitation concentrates in smaller, lower-value, mostly single-item carts — not "sticker shock" on big purchases. Interestingly, per-item price is actually higher in stalled carts (~€97-117/item) than fast carts (~€87/item), suggesting the stalling archetype may be "one pricier item, deliberated over" rather than "big basket, abandoned." I want one more cut on price tier specifically to confirm this before I put it in front of Jonas. Caveat: the discount-presence column is only observable among carts that converted (abandoned carts never reach an order, so there's no discount record) — it's a real but narrower sub-question than "stalled vs fast," and I've noted that.
Report 2 (monthly trend) shows a genuinely flat cart→order conversion rate (34-38%) Jan-Oct — good evidence the bleed is structural, not worsening, which is itself useful for Jonas. But Nov (37.7%) and especially Dec's lower 1-7-day resolution rate (62.5% vs typical 69-73%) are suspect: cart volume nearly doubled in Nov/Dec (holiday season) and any cart created in the back half of December simply hasn't had a full 7 days to resolve against our Dec-31 data cutoff — that's right-censoring, not a real behavior change, and I should not over-read it. More interesting: last week's company channel flagged that cart-reminder emails broke for exactly the week of Nov 24 at 10 merchants including Cragsley Outfitters (one of our 6 retail tenants). That's a natural experiment sitting right there, and it answers two of Jonas's questions at once — Q2 ("what changed") and Q3 ("is a nudge already happening") — so I'm prioritizing it tonight.
Report 3 (subscription tracking gap) flipped my expectation: "after first order" session coverage is actually substantial (42-70% of customers have a session after, averaging 2.4-4.1 of them) — these shoppers do keep coming back and browsing. The real gap is "before first order" (only 16-30% have a tracked session pre-signup), which looks more like a tracking gap at the signup moment than a behavioral finding. That's a sufficient "quick gut-check" per Jonas's ask — I'm not spending more slots on this this week.
Tonight's 3 requests: (1) the Nov 24 cart-reminder-outage natural experiment at Cragsley Outfitters, (2) a price-tier cut to sharpen the Q1 "why" story, (3) a direct empirical test of whether an email nudge correlates with recovery among stalled carts generally (not just the outage week) — the clearest way to answer Jonas's "is a nudge already happening that we could learn from."
The engineer · 1 dry run on the empty schema, then the submission
with tenant as ( select id as tenant_id from tenants where name = 'Cragsley Outfitters' ), cart_weeks as ( select c.id, c.status, c.created_at, c.converted_at, cd.week_start from carts c join tenant t on c.tenant_id = t.tenant_id join calendar_days cd on cd.calendar_date = c.created_at::date where cd.week_start >= date '2025-10-06' and cd.week_start <= date '2025-12-22' ) select week_start, count(*) as carts_created, round(100.0 * sum(case when status = 'converted' then 1 else 0 end) / count(*), 1) as overall_conversion_pct, round(100.0 * sum(case when status = 'converted' and converted_at is not null and extract(epoch from (converted_at - created_at)) / 3600.0 <= 1 then 1 else 0 end) / count(*), 1) as pct_converted_within_1_hour, round(100.0 * sum(case when status = 'converted' and converted_at is not null and extract(epoch from (converted_at - created_at)) / 3600.0 >= 24 and extract(epoch from (converted_at - created_at)) / 3600.0 <= 168 then 1 else 0 end) / count(*), 1) as pct_converted_1_to_7_days from cart_weeks group by week_start order by week_start asc
“Query compiles cleanly with the expected columns.”
“Submitted. The report gives one row per ISO week (Oct 6 – Dec 22, 2025, 12 weeks) for Cragsley Outfitters with carts created, overall conversion %, and the two conversion-speed buckets, so the Nov 24 week sits in the middle of the table for easy before/after comparison.”
As Emeka Obi saw it, from the small dataset the agents worked on
week_start | carts_created | overall_conversion_pct | pct_converted_within_1_hour | pct_converted_1_to_7_days -----------+---------------+------------------------+-----------------------------+-------------------------- 2025-10-06 | 213 | 31.5 | 24.9 | 5.6 2025-10-13 | 236 | 32.2 | 28.0 | 3.0 2025-10-20 | 231 | 29.0 | 23.4 | 4.3 2025-10-27 | 207 | 38.6 | 32.9 | 5.3 2025-11-03 | 250 | 36.0 | 27.2 | 7.2 2025-11-10 | 241 | 31.1 | 24.1 | 5.0 2025-11-17 | 304 | 41.1 | 33.9 | 5.9 2025-11-24 | 524 | 43.5 | 34.9 | 7.4 2025-12-01 | 478 | 38.5 | 31.4 | 6.1 2025-12-08 | 345 | 38.3 | 29.0 | 8.7 2025-12-15 | 333 | 37.5 | 27.9 | 7.8 2025-12-22 | 253 | 40.3 | 33.2 | 6.3 (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
with tenant as ( select id as tenant_id from tenants where name = 'Cragsley Outfitters'),cart_weeks as ( select c.id, c.status, c.created_at, c.converted_at, cd.week_start c.id AS id, c.status AS status, c.created_at AS created_at, c.converted_at AS converted_at, cd.week_start AS week_start from carts c join tenant t on c.tenant_id = t.tenant_id join calendar_days cd on cd.calendar_date = c.created_at::date join calendar_days cd on cd.calendar_date = CAST(c.created_at AS date) where cd.week_start >= date '2025-10-06' and cd.week_start <= date '2025-12-22')select week_start, count(*) as carts_created, round(100.0 * sum(case when status = 'converted' then 1 else 0 end) / count(*), 1) as overall_conversion_pct, round(100.0 * sum(case when status = 'converted' and converted_at is not null and extract(epoch from (converted_at - created_at)) / 3600.0 <= 1 then 1 else 0 end) / count(*), 1) as pct_converted_within_1_hour, round(100.0 * sum(case when status = 'converted' and converted_at is not null and extract(epoch from (converted_at - created_at)) / 3600.0 >= 24 and extract(epoch from (converted_at - created_at)) / 3600.0 <= 168 then 1 else 0 end) / count(*), 1) as pct_converted_1_to_7_days 100.0 * sum(case when status = 'converted' then 1 else 0 end) / count(*) as overall_conversion_pct, 100.0 * sum(case when status = 'converted' and converted_at is not null and {{seconds_between(created_at, converted_at)}} / 3600.0 <= 1 then 1 else 0 end) / count(*) as pct_converted_within_1_hour, 100.0 * sum(case when status = 'converted' and converted_at is not null and {{seconds_between(created_at, converted_at)}} / 3600.0 >= 24 and {{seconds_between(created_at, converted_at)}} / 3600.0 <= 168 then 1 else 0 end) / count(*) as pct_converted_1_to_7_daysfrom cart_weeksgroup by week_startorder by week_start ascorder by week_start asc NULLS LAST
with tenant as ( select id as tenant_id from tenants where name = 'Cragsley Outfitters' ), cart_weeks as ( select c.id AS id, c.status AS status, c.created_at AS created_at, c.converted_at AS converted_at, cd.week_start AS week_start from carts c join tenant t on c.tenant_id = t.tenant_id join calendar_days cd on cd.calendar_date = CAST(c.created_at AS date) where cd.week_start >= date '2025-10-06' and cd.week_start <= date '2025-12-22' ) select week_start, count(*) as carts_created, 100.0 * sum(case when status = 'converted' then 1 else 0 end) / count(*) as overall_conversion_pct, 100.0 * sum(case when status = 'converted' and converted_at is not null and CAST(EXTRACT(EPOCH FROM (converted_at - created_at)) AS NUMERIC(18,6)) / 3600.0 <= 1 then 1 else 0 end) / count(*) as pct_converted_within_1_hour, 100.0 * sum(case when status = 'converted' and converted_at is not null and CAST(EXTRACT(EPOCH FROM (converted_at - created_at)) AS NUMERIC(18,6)) / 3600.0 >= 24 and CAST(EXTRACT(EPOCH FROM (converted_at - created_at)) AS NUMERIC(18,6)) / 3600.0 <= 168 then 1 else 0 end) / count(*) as pct_converted_1_to_7_days from cart_weeks group by week_start order by week_start asc NULLS LAST
with tenant as ( select id as tenant_id from tenants where name = 'Cragsley Outfitters' ), cart_weeks as ( select c.id AS id, c.status AS status, c.created_at AS created_at, c.converted_at AS converted_at, cd.week_start AS week_start from carts c join tenant t on c.tenant_id = t.tenant_id join calendar_days cd on cd.calendar_date = CAST(c.created_at AS date) where cd.week_start >= date '2025-10-06' and cd.week_start <= date '2025-12-22' ) select week_start, count(*) as carts_created, 100.0 * sum(case when status = 'converted' then 1 else 0 end) / count(*) as overall_conversion_pct, 100.0 * sum(case when status = 'converted' and converted_at is not null and CAST(divideDecimal(toDecimal128(dateDiff('microsecond', created_at, converted_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0 <= 1 then 1 else 0 end) / count(*) as pct_converted_within_1_hour, 100.0 * sum(case when status = 'converted' and converted_at is not null and CAST(divideDecimal(toDecimal128(dateDiff('microsecond', created_at, converted_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0 >= 24 and CAST(divideDecimal(toDecimal128(dateDiff('microsecond', created_at, converted_at), 0), toDecimal128(1000000, 0), 6) AS Nullable(Decimal(18, 6))) / 3600.0 <= 168 then 1 else 0 end) / count(*) as pct_converted_1_to_7_days from cart_weeks group by week_start order by week_start 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 Emeka Obi (analyst for the VP Product): Title: Cart conversion by week at Cragsley Outfitters around the Nov 24 reminder-email outage Question: Did cart conversion dip during the week cart-reminder emails broke (week of Nov 24, 2025), suggesting those reminders meaningfully nudge stalled carts toward purchase? Grain: one row per ISO week of cart creation, for Cragsley Outfitters only Columns: - week start date - carts created - overall conversion pct (cart eventually became an order) - pct converted within 1 hour - pct converted in 1-7 days Details: Tenant: Cragsley Outfitters only (look up tenant_id by name). Population: carts with created_at falling in ISO weeks starting 2025-10-06 through 2025-12-22 inclusive (12 weeks) — stop at week-start 2025-12-22 so every included cart has a full 7-day window to resolve before the 2025-12-31 data cutoff (avoids right-censoring bias). overall_conversion_pct = % of that week's carts with status='converted' (no time limit on when). pct_converted_within_1_hour and pct_converted_in_1_7_days use resolution_time = converted_at - created_at (hours/days), same definitions as prior cart-timing reports. Order rows by week start ascending. The week of 2025-11-24 should be visually easy to compare against the surrounding weeks. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runwith tenant as ( select id as tenant_id from tenants where name = 'Cragsley Outfitters' ), cart_weeks as ( select c.id, c.status, c.created_at, c.converted_at, cd.week_start from carts c join tenant t on c.tenant_id = t.tenant_id join calendar_days cd on cd.calendar_date = c.created_at::date where cd.week_start >= date '2025-10-06' and cd.week_start <= date '2025-12-22' ) select week_start, count(*) as carts_created, round(100.0 * sum(case when status = 'converted' then 1 else 0 end) / count(*), 1) as overall_conversion_pct, round(100.0 * sum(case when status = 'converted' and converted_at is not null and extract(epoch from (converted_at - created_at)) / 3600.0 <= 1 then 1 else 0 end) / count(*), 1) as pct_converted_within_1_hour, round(100.0 * sum(case when status = 'converted' and converted_at is not null and extract(epoch from (converted_at - created_at)) / 3600.0 >= 24 and extract(epoch from (converted_at - created_at)) / 3600.0 <= 168 then 1 else 0 end) / count(*), 1) as pct_converted_1_to_7_days from cart_weeks group by week_start order by week_start asc
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: week_start, carts_created, overall_conversion_pct, pct_converted_within_1_hour, pct_converted_1_to_7_days
submitwith tenant as ( select id as tenant_id from tenants where name = 'Cragsley Outfitters' ), cart_weeks as ( select c.id, c.status, c.created_at, c.converted_at, cd.week_start from carts c join tenant t on c.tenant_id = t.tenant_id join calendar_days cd on cd.calendar_date = c.created_at::date where cd.week_start >= date '2025-10-06' and cd.week_start <= date '2025-12-22' ) select week_start, count(*) as carts_created, round(100.0 * sum(case when status = 'converted' then 1 else 0 end) / count(*), 1) as overall_conversion_pct, round(100.0 * sum(case when status = 'converted' and converted_at is not null and extract(epoch from (converted_at - created_at)) / 3600.0 <= 1 then 1 else 0 end) / count(*), 1) as pct_converted_within_1_hour, round(100.0 * sum(case when status = 'converted' and converted_at is not null and extract(epoch from (converted_at - created_at)) / 3600.0 >= 24 and extract(epoch from (converted_at - created_at)) / 3600.0 <= 168 then 1 else 0 end) / count(*), 1) as pct_converted_1_to_7_days from cart_weeks group by week_start order by week_start asc
Submitted to tonight's batch. Your work on this request is done.