Queries · q297 · VP Product · week 2, night 4
“Is the email that correlates with cart recovery a deliberate cart-reminder/recovery mechanism, or just general marketing/engagement email that happens to land in that window?”
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 4, request 2 of 3
Reading last night's reports — this is the last night before the memo, so I'm using these 3 slots to close the most important open questions rather than open new ones.
Report 1 (email nudge, guest vs. logged-in) clarifies a lot: guest carts (null user_id) converted at exactly 0.0% (0 of 7,540) — once I think about it, this is almost certainly structural: orders.user_id is not-null in the schema, so a cart can only ever reach status='converted' once it's tied to a logged-in user. That reframes the story — the real "cliff" isn't the 1-7 day window per se, it's whether the shopper ever logs in/creates an account at all. Among carts that are tied to a logged-in user, received_email converts at 56.0% vs. 20.9% with no email — still a big, consistent gap (and the math cross-checks cleanly against last week's blended 8.1% no-email figure), but I want to know what kind of email that actually is before calling it a proven "nudge effect," since it could just be that more-engaged customers both get more email and convert more.
Report 2 (Cragsley daily, Nov 17-Dec 1) does show Nov 24-25 (36.7%, 31.25%) a bit below the Nov 17-23 average (~41%), with recovery starting Nov 26 as Black Friday demand kicks in — suggestive of a real but small dip, but daily samples are only ~35-55 carts, too noisy to trust without a longer weekday-matched baseline.
Report 3 (single vs. multi-item, by tenant) holds up across all 6 tenants (gap 16-34 points) — good, this isn't a one-merchant artifact. But given report 1's guest-conversion finding, I now need to check whether "single-item carts convert worse" is really just another face of "guest carts convert worse" (if guests disproportionately add just one item), since that would change the headline recommendation from "nudge small-basket shoppers" to "get people to log in."
Tonight's 3 requests: (1) the guest-vs-logged-in confound check on the item-count finding — the most important one, since it could change the Q1 headline; (2) what template/channel is actually behind the "received_email" recovery effect, to judge whether it's a deliberate cart-recovery mechanism or just general engagement; (3) a weekday-matched baseline for Cragsley (more Mondays/Tuesdays) to get a more statistically grounded read on whether Nov 24-25 was really unusual.
The engineer · 2 dry runs on the empty schema, then the submission
WITH t0 AS ( -- stalled carts: created in window, not converted within first hour SELECT c0.id AS cart_id, c0.tenant_id AS tenant_id, c0.user_id AS user_id, c0.created_at AS created_at FROM carts c0 JOIN tenants t0t ON t0t.id = c0.tenant_id WHERE t0t.name IN ( 'Kibblewick', 'Studio Vrij', 'Petalcourt Beauty', 'Marrowby & Vane', 'Cragsley Outfitters', 'Ampfeld Elektronik' ) AND c0.created_at >= DATE '2025-01-01' AND c0.created_at < DATE '2025-12-01' AND NOT ( c0.status = 'converted' AND c0.converted_at IS NOT NULL AND c0.converted_at <= c0.created_at + INTERVAL '1 hour' ) ), t1 AS ( -- distinct matched email_log rows per stalled cart owner, in the recovery window SELECT DISTINCT e1.id AS email_log_id, e1.tenant_id AS tenant_id, e1.template_id AS template_id FROM email_log e1 JOIN t0 ON t0.tenant_id = e1.tenant_id AND t0.user_id = e1.user_id AND e1.sent_at >= t0.created_at + INTERVAL '1 hour' AND e1.sent_at <= t0.created_at + INTERVAL '48 hour' ), t2 AS ( -- email counts per (tenant, template) SELECT t1.tenant_id AS tenant_id, t1.template_id AS template_id, COUNT(*) AS email_count FROM t1 GROUP BY t1.tenant_id, t1.template_id ), t3 AS ( -- grand total of matched emails, for pct calc SELECT COUNT(*) AS total_matched FROM t1 ) SELECT COALESCE(tn.name, 'multiple merchants') AS merchant, COALESCE(nt.code, 'no template / ad hoc') AS template_code, COALESCE(nt.name, 'no template / ad hoc') AS template_name, COALESCE(nt.channel, 'n/a') AS channel, t2.email_count AS email_count, ROUND(100.0 * t2.email_count / NULLIF(t3.total_matched, 0), 2) AS pct_of_matched_emails FROM t2 CROSS JOIN t3 LEFT JOIN notification_templates nt ON nt.id = t2.template_id LEFT JOIN tenants tn ON tn.id = t2.tenant_id AND nt.id IS NOT NULL ORDER BY t2.email_count DESC, template_code, merchant
WITH t0 AS ( -- stalled carts: created in window, not converted within first hour SELECT c0.id AS cart_id, c0.tenant_id AS tenant_id, c0.user_id AS user_id, c0.created_at AS created_at FROM carts c0 JOIN tenants t0t ON t0t.id = c0.tenant_id WHERE t0t.name IN ( 'Kibblewick', 'Studio Vrij', 'Petalcourt Beauty', 'Marrowby & Vane', 'Cragsley Outfitters', 'Ampfeld Elektronik' ) AND c0.created_at >= DATE '2025-01-01' AND c0.created_at < DATE '2025-12-01' AND NOT ( c0.status = 'converted' AND c0.converted_at IS NOT NULL AND c0.converted_at <= c0.created_at + INTERVAL '1 hour' )),t1 AS ( -- distinct matched email_log rows per stalled cart owner, in the recovery window SELECT DISTINCT e1.id AS email_log_id, e1.tenant_id AS tenant_id, e1.template_id AS template_id FROM email_log e1 JOIN t0 ON t0.tenant_id = e1.tenant_id AND t0.user_id = e1.user_id AND e1.sent_at >= t0.created_at + INTERVAL '1 hour' AND e1.sent_at <= t0.created_at + INTERVAL '48 hour'),t2 AS ( -- email counts per (tenant, template)t2 AS ( -- email counts per template (NULL template_id rows collapse into one 'ad hoc' group) SELECT t1.tenant_id AS tenant_id, t1.template_id AS template_id, COUNT(*) AS email_count FROM t1 GROUP BY t1.tenant_id, t1.template_id GROUP BY t1.template_id),t3 AS ( -- grand total of matched emails, for pct calc SELECT COUNT(*) AS total_matched FROM t1)SELECT COALESCE(tn.name, 'multiple merchants') AS merchant, COALESCE(nt.code, 'no template / ad hoc') AS template_code, COALESCE(nt.name, 'no template / ad hoc') AS template_name, COALESCE(nt.channel, 'n/a') AS channel, t2.email_count AS email_count, ROUND(100.0 * t2.email_count / NULLIF(t3.total_matched, 0), 2) AS pct_of_matched_emailsFROM t2CROSS JOIN t3LEFT JOIN notification_templates nt ON nt.id = t2.template_idLEFT JOIN tenants tn ON tn.id = t2.tenant_id AND nt.id IS NOT NULLORDER BY t2.email_count DESC, template_code, merchantORDER BY t2.email_count DESC, template_code
“This compiles cleanly with the right grain: one row per template (plus a single collapsed "no template / ad hoc" row), with code, name, channel, count and share of matched emails. Submitting.”
“Submitted. Summary of what the report will show: one row per notification template (plus a single collapsed "no template / ad hoc" row) used in Tidewick's email_log, scoped to the six named retail merchants, counting only emails sent 1–48 hours after creation of a stalled cart (created Jan–Nov 2025, not converted within the first hour). Columns are template code, template name, channel, email count, and that template's share of all matched emails — which should let the VP Product see whether recovery-correlated email is dominated by a dedicated cart-abandonment template or by general marketing sends.”
As Emeka Obi saw it, from the small dataset the agents worked on
template_code | template_name | channel | email_count | pct_of_matched_emails ----------------------+-----------------------+---------+-------------+---------------------- order_confirmation | Order confirmation | email | 193 | 24.9 order_confirmation | Order confirmation | email | 90 | 11.61 shipping_confirmation | Shipping confirmation | email | 85 | 10.97 delivered | Delivery confirmation | email | 63 | 8.13 shipping_confirmation | Shipping confirmation | email | 51 | 6.58 delivered | Delivery confirmation | email | 39 | 5.03 order_confirmation | Order confirmation | email | 36 | 4.65 order_confirmation | Order confirmation | email | 34 | 4.39 refund_issued | Refund issued | email | 24 | 3.1 order_confirmation | Order confirmation | email | 19 | 2.45 order_confirmation | Order confirmation | email | 19 | 2.45 shipping_confirmation | Shipping confirmation | email | 18 | 2.32 delivered | Delivery confirmation | email | 16 | 2.06 delivered | Delivery confirmation | email | 15 | 1.94 shipping_confirmation | Shipping confirmation | email | 12 | 1.55 shipping_confirmation | Shipping confirmation | email | 12 | 1.55 subscription_receipt | Subscription receipt | email | 11 | 1.42 delivered | Delivery confirmation | email | 10 | 1.29 refund_issued | Refund issued | email | 8 | 1.03 shipping_confirmation | Shipping confirmation | email | 8 | 1.03 delivered | Delivery confirmation | email | 5 | 0.65 refund_issued | Refund issued | email | 4 | 0.52 password_reset | Password reset | email | 1 | 0.13 password_reset | Password reset | email | 1 | 0.13 refund_issued | Refund issued | email | 1 | 0.13 (25 rows)
The memo and the queries that followed; links found by title, so inferred
Emeka Obi's memo to Jonas Feld at the end of the week:
3. I need to retract last week's "email nudge" framing — there is no cart-recovery email running today. I dug into which emails actually landed in that 56%-conversion group, and they're order confirmations, shipping/delivery confirmations, and refund notices — i.e., emails that go out because someone already transacted, not reminders sent to stalled shoppers. Report: "What kind of email reaches shoppers who recover a stalled cart?" The correlation I flagged last week was concurrent purchase activity, not a nudge causing recovery. This changes my answer to your Q3: no organic recovery mechanism currently exists to learn from — we'd be building one from scratch.
It builds on:
Changed only where the benchmark needs the same result on every system
WITH t0 AS ( -- stalled carts: created in window, not converted within first hour SELECT c0.id AS cart_id, c0.tenant_id AS tenant_id, c0.user_id AS user_id, c0.created_at AS created_at FROM carts c0 JOIN tenants t0t ON t0t.id = c0.tenant_id WHERE t0t.name IN ( 'Kibblewick', 'Studio Vrij', 'Petalcourt Beauty', 'Marrowby & Vane', 'Cragsley Outfitters', 'Ampfeld Elektronik' ) AND c0.created_at >= DATE '2025-01-01' AND c0.created_at < DATE '2025-12-01' AND NOT ( c0.status = 'converted' AND c0.converted_at IS NOT NULL AND c0.converted_at <= c0.created_at + INTERVAL '1 hour' )),t1 AS ( -- distinct matched email_log rows per stalled cart owner, in the recovery window SELECT DISTINCT e1.id AS email_log_id, e1.template_id AS template_id FROM email_log e1 JOIN t0 ON t0.tenant_id = e1.tenant_id AND t0.user_id = e1.user_id AND e1.sent_at >= t0.created_at + INTERVAL '1 hour' AND e1.sent_at <= t0.created_at + INTERVAL '48 hour'),t2 AS ( -- email counts per template (NULL template_id rows collapse into one 'ad hoc' group) SELECT t1.template_id AS template_id, COUNT(*) AS email_count FROM t1 GROUP BY t1.template_id),t3 AS ( -- grand total of matched emails, for pct calc SELECT COUNT(*) AS total_matched FROM t1)SELECT COALESCE(nt.code, 'no template / ad hoc') AS template_code, COALESCE(nt.name, 'no template / ad hoc') AS template_name, COALESCE(nt.channel, 'n/a') AS channel, t2.email_count AS email_count, ROUND(100.0 * t2.email_count / NULLIF(t3.total_matched, 0), 2) AS pct_of_matched_emails 100.0 * t2.email_count / NULLIF(t3.total_matched, 0) AS pct_of_matched_emailsFROM t2CROSS JOIN t3LEFT JOIN notification_templates nt ON nt.id = t2.template_idORDER BY t2.email_count DESC, template_codeORDER BY t2.email_count DESC NULLS FIRST, template_code NULLS LAST, t2.template_id NULLS LAST
WITH t0 AS ( -- stalled carts: created in window, not converted within first hour SELECT c0.id AS cart_id, c0.tenant_id AS tenant_id, c0.user_id AS user_id, c0.created_at AS created_at FROM carts c0 JOIN tenants t0t ON t0t.id = c0.tenant_id WHERE t0t.name IN ( 'Kibblewick', 'Studio Vrij', 'Petalcourt Beauty', 'Marrowby & Vane', 'Cragsley Outfitters', 'Ampfeld Elektronik' ) AND c0.created_at >= DATE '2025-01-01' AND c0.created_at < DATE '2025-12-01' AND NOT ( c0.status = 'converted' AND c0.converted_at IS NOT NULL AND c0.converted_at <= c0.created_at + INTERVAL '1 hour' ) ), t1 AS ( -- distinct matched email_log rows per stalled cart owner, in the recovery window SELECT DISTINCT e1.id AS email_log_id, e1.template_id AS template_id FROM email_log e1 JOIN t0 ON t0.tenant_id = e1.tenant_id AND t0.user_id = e1.user_id AND e1.sent_at >= t0.created_at + INTERVAL '1 hour' AND e1.sent_at <= t0.created_at + INTERVAL '48 hour' ), t2 AS ( -- email counts per template (NULL template_id rows collapse into one 'ad hoc' group) SELECT t1.template_id AS template_id, COUNT(*) AS email_count FROM t1 GROUP BY t1.template_id ), t3 AS ( -- grand total of matched emails, for pct calc SELECT COUNT(*) AS total_matched FROM t1 ) SELECT COALESCE(nt.code, 'no template / ad hoc') AS template_code, COALESCE(nt.name, 'no template / ad hoc') AS template_name, COALESCE(nt.channel, 'n/a') AS channel, t2.email_count AS email_count, 100.0 * t2.email_count / NULLIF(t3.total_matched, 0) AS pct_of_matched_emails FROM t2 CROSS JOIN t3 LEFT JOIN notification_templates nt ON nt.id = t2.template_id ORDER BY t2.email_count DESC NULLS FIRST, template_code NULLS LAST, t2.template_id 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: What kind of email reaches shoppers who recover a stalled cart? Question: Is the email that correlates with cart recovery a deliberate cart-reminder/recovery mechanism, or just general marketing/engagement email that happens to land in that window? Grain: one row per notification template (or 'no template / ad hoc' bucket) Columns: - template code or name - channel - email count - pct of matched emails Details: Tenants: the 6 retail merchants (Kibblewick, Studio Vrij, Petalcourt Beauty, Marrowby & Vane, Cragsley Outfitters, Ampfeld Elektronik). Population: the same email_log rows that qualified a cart as 'received_email' in the prior report — i.e., email_log rows (tenant_id, user_id matching a stalled cart's owner) with sent_at between the cart's created_at+1h and created_at+48h, for carts created 2025-01-01 to 2025-11-30 that were not already converted within the first hour. Join email_log.template_id to notification_templates to get the template's code/name and channel; group by template (bucket rows with a null template_id as 'no template / ad hoc'). email_count = count of matching email_log rows for that template; pct_of_matched_emails = share of all matched emails. Order rows by email_count descending. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH t0 AS ( -- stalled carts: created in window, not converted within first hour SELECT c0.id AS cart_id, c0.tenant_id AS tenant_id, c0.user_id AS user_id, c0.created_at AS created_at FROM carts c0 JOIN tenants t0t ON t0t.id = c0.tenant_id WHERE t0t.name IN ( 'Kibblewick', 'Studio Vrij', 'Petalcourt Beauty', 'Marrowby & Vane', 'Cragsley Outfitters', 'Ampfeld Elektronik' ) AND c0.created_at >= DATE '2025-01-01' AND c0.created_at < DATE '2025-12-01' AND NOT ( c0.status = 'converted' AND c0.converted_at IS NOT NULL AND c0.converted_at <= c0.created_at + INTERVAL '1 hour' ) ), t1 AS ( -- distinct matched email_log rows per stalled cart owner, in the recovery window SELECT DISTINCT e1.id AS email_log_id, e1.tenant_id AS tenant_id, e1.template_id AS template_id FROM email_log e1 JOIN t0 ON t0.tenant_id = e1.tenant_id AND t0.user_id = e1.user_id AND e1.sent_at >= t0.created_at + INTERVAL '1 hour' AND e1.sent_at <= t0.created_at + INTERVAL '48 hour' ), t2 AS ( -- email counts per (tenant, template) SELECT t1.tenant_id AS tenant_id, t1.template_id AS template_id, COUNT(*) AS email_count FROM t1 GROUP BY t1.tenant_id, t1.template_id ), t3 AS ( -- grand total of matched emails, for pct calc SELECT COUNT(*) AS total_matched FROM t1 ) SELECT COALESCE(tn.name, 'multiple merchants') AS merchant, COALESCE(nt.code, 'no template / ad hoc') AS template_code, COALESCE(nt.name, 'no template / ad hoc') AS template_name, COALESCE(nt.channel, 'n/a') AS channel, t2.email_count AS email_count, ROUND(100.0 * t2.email_count / NULLIF(t3.total_matched, 0), 2) AS pct_of_matched_emails FROM t2 CROSS JOIN t3 LEFT JOIN notification_templates nt ON nt.id = t2.template_id LEFT JOIN tenants tn ON tn.id = t2.tenant_id AND nt.id IS NOT NULL ORDER BY t2.email_count DESC, template_code, merchant
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: merchant, template_code, template_name, channel, email_count, pct_of_matched_emails
dry_runWITH t0 AS ( -- stalled carts: created in window, not converted within first hour SELECT c0.id AS cart_id, c0.tenant_id AS tenant_id, c0.user_id AS user_id, c0.created_at AS created_at FROM carts c0 JOIN tenants t0t ON t0t.id = c0.tenant_id WHERE t0t.name IN ( 'Kibblewick', 'Studio Vrij', 'Petalcourt Beauty', 'Marrowby & Vane', 'Cragsley Outfitters', 'Ampfeld Elektronik' ) AND c0.created_at >= DATE '2025-01-01' AND c0.created_at < DATE '2025-12-01' AND NOT ( c0.status = 'converted' AND c0.converted_at IS NOT NULL AND c0.converted_at <= c0.created_at + INTERVAL '1 hour' ) ), t1 AS ( -- distinct matched email_log rows per stalled cart owner, in the recovery window SELECT DISTINCT e1.id AS email_log_id, e1.template_id AS template_id FROM email_log e1 JOIN t0 ON t0.tenant_id = e1.tenant_id AND t0.user_id = e1.user_id AND e1.sent_at >= t0.created_at + INTERVAL '1 hour' AND e1.sent_at <= t0.created_at + INTERVAL '48 hour' ), t2 AS ( -- email counts per template (NULL template_id rows collapse into one 'ad hoc' group) SELECT t1.template_id AS template_id, COUNT(*) AS email_count FROM t1 GROUP BY t1.template_id ), t3 AS ( -- grand total of matched emails, for pct calc SELECT COUNT(*) AS total_matched FROM t1 ) SELECT COALESCE(nt.code, 'no template / ad hoc') AS template_code, COALESCE(nt.name, 'no template / ad hoc') AS template_name, COALESCE(nt.channel, 'n/a') AS channel, t2.email_count AS email_count, ROUND(100.0 * t2.email_count / NULLIF(t3.total_matched, 0), 2) AS pct_of_matched_emails FROM t2 CROSS JOIN t3 LEFT JOIN notification_templates nt ON nt.id = t2.template_id ORDER BY t2.email_count DESC, template_code
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: template_code, template_name, channel, email_count, pct_of_matched_emails
submitWITH t0 AS ( -- stalled carts: created in window, not converted within first hour SELECT c0.id AS cart_id, c0.tenant_id AS tenant_id, c0.user_id AS user_id, c0.created_at AS created_at FROM carts c0 JOIN tenants t0t ON t0t.id = c0.tenant_id WHERE t0t.name IN ( 'Kibblewick', 'Studio Vrij', 'Petalcourt Beauty', 'Marrowby & Vane', 'Cragsley Outfitters', 'Ampfeld Elektronik' ) AND c0.created_at >= DATE '2025-01-01' AND c0.created_at < DATE '2025-12-01' AND NOT ( c0.status = 'converted' AND c0.converted_at IS NOT NULL AND c0.converted_at <= c0.created_at + INTERVAL '1 hour' ) ), t1 AS ( -- distinct matched email_log rows per stalled cart owner, in the recovery window SELECT DISTINCT e1.id AS email_log_id, e1.template_id AS template_id FROM email_log e1 JOIN t0 ON t0.tenant_id = e1.tenant_id AND t0.user_id = e1.user_id AND e1.sent_at >= t0.created_at + INTERVAL '1 hour' AND e1.sent_at <= t0.created_at + INTERVAL '48 hour' ), t2 AS ( -- email counts per template (NULL template_id rows collapse into one 'ad hoc' group) SELECT t1.template_id AS template_id, COUNT(*) AS email_count FROM t1 GROUP BY t1.template_id ), t3 AS ( -- grand total of matched emails, for pct calc SELECT COUNT(*) AS total_matched FROM t1 ) SELECT COALESCE(nt.code, 'no template / ad hoc') AS template_code, COALESCE(nt.name, 'no template / ad hoc') AS template_name, COALESCE(nt.channel, 'n/a') AS channel, t2.email_count AS email_count, ROUND(100.0 * t2.email_count / NULLIF(t3.total_matched, 0), 2) AS pct_of_matched_emails FROM t2 CROSS JOIN t3 LEFT JOIN notification_templates nt ON nt.id = t2.template_id ORDER BY t2.email_count DESC, template_code
Submitted to tonight's batch. Your work on this request is done.