Queries · q294 · VP Product · week 2, night 3
“Is there a visible conversion dip in the first few days after cart-reminder emails broke (Nov 24), before Black Friday/Cyber Monday demand likely swamps the signal?”
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 3, request 2 of 3
Reading last night's reports:
Report 1 (Cragsley weekly conversion around the Nov 24 outage) undercuts the simple story: conversion didn't dip during the broken-reminder week — it hit 43.5%, the highest week in the whole window, up from a 29-41% range beforehand. The obvious explanation is that Nov 24-30, 2025 is Black Friday/Cyber Monday week — a massive demand spike that would swamp any negative effect from broken reminder emails. So this natural experiment is confounded by timing and can't cleanly tell us whether the reminders matter; I don't think it's worth forcing further at the weekly grain, but a daily cut around the exact outage start (before Black Friday really kicks in on Nov 28) might still show something, so I'll try that once.
Report 2 (price tier vs. conversion) reinforces the Q1 story on a different cut: higher-priced carts (which are also higher-item-count: 1.3 items at <€50 vs 3.2 items at €200+) convert better and faster (51.4% / 44.1% within 1hr for €200+ vs 23.3% / 16.1% for <€50) — consistent with last night's finding that small, likely single-item, lower-value carts are where hesitation concentrates, not pricier "considered" purchases. Two independent cuts now agree, so I consider Q1 well-answered. Before I put "small single-item carts stall" in the memo as a general pattern, I want one quick robustness check that it's not just one merchant (e.g. Cragsley, which dominates volume) driving the aggregate.
Report 3 (email nudge) shows a striking gap — 56.0% eventual conversion for carts whose user got an email vs. 8.1% for no-email — but it's likely confounded: 7,540 of the 12,280 "no_email" carts are guest carts that structurally can never receive an email (no user_id to target), and guests plausibly convert worse regardless of email. I want to split "no_email" into guest vs. logged-in before trusting this as evidence the nudge itself works, since the fair comparison is received_email vs. logged-in-but-not-emailed.
Tonight's 3 requests: (1) the cleaner 3-way email-nudge split, (2) a daily drill-down at Cragsley right at the outage start to see if Black Friday is really masking a real dip, (3) a per-tenant robustness check that the single-item-cart-stalls pattern isn't just one merchant's effect.
The engineer · 1 dry run on the empty schema, then the submission
SELECT c.created_at::date AS cart_date, COUNT(*) AS carts_created, ROUND(100.0 * SUM(CASE WHEN c.status = 'converted' THEN 1 ELSE 0 END) / COUNT(*), 2) AS overall_conversion_pct, ROUND(100.0 * SUM(CASE WHEN c.status = 'converted' AND c.converted_at IS NOT NULL AND c.converted_at <= c.created_at + INTERVAL '1 hour' THEN 1 ELSE 0 END) / COUNT(*), 2) AS pct_converted_within_1_hour FROM carts c JOIN tenants t ON t.id = c.tenant_id WHERE t.name = 'Cragsley Outfitters' AND c.created_at >= DATE '2025-11-17' AND c.created_at < DATE '2025-12-02' GROUP BY c.created_at::date ORDER BY cart_date ASC
“Query compiles and returns the expected columns.”
“Submitted. The report computes, per calendar day of cart creation for Cragsley Outfitters (Nov 17–Dec 1, 2025): carts created, overall conversion % (status = 'converted'), and % converted within 1 hour of creation — ordered by date ascending, 15 rows total.”
As Emeka Obi saw it, from the small dataset the agents worked on
cart_date | carts_created | overall_conversion_pct | pct_converted_within_1_hour -----------+---------------+------------------------+---------------------------- 2025-11-17 | 51 | 45.1 | 35.29 2025-11-18 | 38 | 52.63 | 44.74 2025-11-19 | 37 | 37.84 | 29.73 2025-11-20 | 33 | 36.36 | 30.3 2025-11-21 | 39 | 41.03 | 35.9 2025-11-22 | 57 | 36.84 | 28.07 2025-11-23 | 49 | 38.78 | 34.69 2025-11-24 | 49 | 36.73 | 30.61 2025-11-25 | 48 | 31.25 | 29.17 2025-11-26 | 74 | 43.24 | 29.73 2025-11-27 | 63 | 47.62 | 30.16 2025-11-28 | 109 | 43.12 | 36.7 2025-11-29 | 83 | 48.19 | 43.37 2025-11-30 | 98 | 46.94 | 37.76 2025-12-01 | 122 | 45.08 | 36.89 (15 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 c.created_at::date AS cart_date, CAST(c.created_at AS date) AS cart_date, COUNT(*) AS carts_created, ROUND(100.0 * SUM(CASE WHEN c.status = 'converted' THEN 1 ELSE 0 END) / COUNT(*), 2) AS overall_conversion_pct, ROUND(100.0 * SUM(CASE WHEN c.status = 'converted' AND c.converted_at IS NOT NULL AND c.converted_at <= c.created_at + INTERVAL '1 hour' THEN 1 ELSE 0 END) / COUNT(*), 2) AS pct_converted_within_1_hour 100.0 * SUM(CASE WHEN c.status = 'converted' THEN 1 ELSE 0 END) / COUNT(*) AS overall_conversion_pct, 100.0 * SUM(CASE WHEN c.status = 'converted' AND c.converted_at IS NOT NULL AND c.converted_at <= c.created_at + INTERVAL '1 hour' THEN 1 ELSE 0 END) / COUNT(*) AS pct_converted_within_1_hourFROM carts cJOIN tenants t ON t.id = c.tenant_idWHERE t.name = 'Cragsley Outfitters' AND c.created_at >= DATE '2025-11-17' AND c.created_at < DATE '2025-12-02'GROUP BY c.created_at::dateORDER BY cart_date ASCGROUP BY CAST(c.created_at AS date)ORDER BY cart_date ASC NULLS LAST
SELECT CAST(c.created_at AS date) AS cart_date, COUNT(*) AS carts_created, 100.0 * SUM(CASE WHEN c.status = 'converted' THEN 1 ELSE 0 END) / COUNT(*) AS overall_conversion_pct, 100.0 * SUM(CASE WHEN c.status = 'converted' AND c.converted_at IS NOT NULL AND c.converted_at <= c.created_at + INTERVAL '1 hour' THEN 1 ELSE 0 END) / COUNT(*) AS pct_converted_within_1_hour FROM carts c JOIN tenants t ON t.id = c.tenant_id WHERE t.name = 'Cragsley Outfitters' AND c.created_at >= DATE '2025-11-17' AND c.created_at < DATE '2025-12-02' GROUP BY CAST(c.created_at AS date) ORDER BY cart_date 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: Daily cart conversion at Cragsley Outfitters, Nov 17 – Dec 1 2025 Question: Is there a visible conversion dip in the first few days after cart-reminder emails broke (Nov 24), before Black Friday/Cyber Monday demand likely swamps the signal? Grain: one row per calendar day of cart creation, Cragsley Outfitters only Columns: - date - carts created - overall conversion pct - pct converted within 1 hour Details: Tenant: Cragsley Outfitters. Population: carts with created_at on each day from 2025-11-17 through 2025-12-01 inclusive (15 days), bracketing one week before the outage (Nov 17-23) against the outage week and the days just after (Nov 24-Dec 1, including Black Friday Nov 28 and Cyber Monday Dec 1). Use the same overall_conversion_pct and pct_converted_within_1_hour definitions as the prior weekly report. Order rows by date ascending. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT c.created_at::date AS cart_date, COUNT(*) AS carts_created, ROUND(100.0 * SUM(CASE WHEN c.status = 'converted' THEN 1 ELSE 0 END) / COUNT(*), 2) AS overall_conversion_pct, ROUND(100.0 * SUM(CASE WHEN c.status = 'converted' AND c.converted_at IS NOT NULL AND c.converted_at <= c.created_at + INTERVAL '1 hour' THEN 1 ELSE 0 END) / COUNT(*), 2) AS pct_converted_within_1_hour FROM carts c JOIN tenants t ON t.id = c.tenant_id WHERE t.name = 'Cragsley Outfitters' AND c.created_at >= DATE '2025-11-17' AND c.created_at < DATE '2025-12-02' GROUP BY c.created_at::date ORDER BY cart_date ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: cart_date, carts_created, overall_conversion_pct, pct_converted_within_1_hour
submitSELECT c.created_at::date AS cart_date, COUNT(*) AS carts_created, ROUND(100.0 * SUM(CASE WHEN c.status = 'converted' THEN 1 ELSE 0 END) / COUNT(*), 2) AS overall_conversion_pct, ROUND(100.0 * SUM(CASE WHEN c.status = 'converted' AND c.converted_at IS NOT NULL AND c.converted_at <= c.created_at + INTERVAL '1 hour' THEN 1 ELSE 0 END) / COUNT(*), 2) AS pct_converted_within_1_hour FROM carts c JOIN tenants t ON t.id = c.tenant_id WHERE t.name = 'Cragsley Outfitters' AND c.created_at >= DATE '2025-11-17' AND c.created_at < DATE '2025-12-02' GROUP BY c.created_at::date ORDER BY cart_date ASC
Submitted to tonight's batch. Your work on this request is done.