Queries · q356 · Head of Merchandising · week 2, night 4
“Does the suspiciously clean 100%/0% bundle adoption split by vertical look like genuine, ongoing merchant activity, or does it look like a one-time data-seeding event?”
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 start — let's dig into the risk areas
Hi Zara,
This is really useful, and a few things jumped out that I want to understand better before Friday.
The Cragsley Outfitters finding worries me most — our #2 merchant by revenue with real quality concentration, and some of those poorly-rated items are still selling briskly. That combination (strong sales, weak ratings, sizable product count) feels like the kind of thing that erodes trust quietly until it doesn't. I'd like to understand it better: is this concentrated in certain kinds of products, has it been building over time, and does it look like it's about to hit sales or is demand just lagging behind reputation?
Second, the December rating dip needs a clearer story. You flagged it as needing a second month to confirm — I agree, but I'd also like to understand whether there's something explainable behind it (seasonal returns, a particular product push, shipping delays around the holidays) rather than just watching and waiting. If Fennel & Flint and Wiesgrund Wohnen are worth a check-in, I want to go into that conversation knowing roughly what happened.
Third, I was surprised that going from zero to one image makes such a big difference but more images after that don't. Before I turn that into a merchant talking point, I want to know if we're actually looking at a real presentation effect or just a stand-in for something else — are the zero-image listings systematically different in some way, like older, or a different kind of product?
Finally, on bundles: you said it's white space but we can't test performance yet because usage is too rare. I'd like to understand it a bit more anecdotally before we design a pilot — among the handful of merchants that do use bundles, is there anything that distinguishes them, even descriptively, from the ones that don't?
Thanks again — this is exactly the kind of clarity I needed.
Freya
The analyst · week 2, night 4, request 1 of 3
Night 3 findings: (1) Wiesgrund Wohnen's December reviews are scattered thinly across 10 different products (mostly 1 review each) rather than concentrated on one bad item — but nearly all of them are fragile homeware: vases, a frying pan, dinner plates, a casserole dish, glassware. That points away from 'systemic quality problem' and toward 'breakage in transit,' which wouldn't show up in on-time-delivery stats (an item can arrive on schedule but shattered). (2) The 3-year December history splits the two merchants clearly: Fennel & Flint shows a real, monotonic 3-year rating decline (4.43 → 3.71 → 3.14) alongside order growth (28 → 45 → 69), consistent with a merchant whose quality control hasn't kept pace with scale; Wiesgrund Wohnen's history is noisier and non-monotonic (4.00 → 3.57 → 3.50, with 2023 already at 28.6% 1-2-star on a tiny sample), more consistent with episodic bad luck than a trend. (3) The bundle-by-vertical split is suspiciously clean — exactly 100% of merchants in six verticals (Beauty, Books & Stationery, Consumer Electronics, Food & Coffee, Home & Living, Jewelry) have made at least one bundle, and exactly 0% in the other five (Apparel & Footwear, both B2B verticals, Pet Supplies, Sports & Outdoor) have made any. A binary split with no partial adoption anywhere is unusual for organic merchant behavior and makes me suspect this may reflect how bundles were seeded/templated per vertical rather than a genuine merchandising choice — I want to check the creation-date distribution before I tell Freya it's a real behavioral pattern. For this final night: (A) check whether bundle creation clusters in one burst (seeding) or spreads out over time (organic), (B) test the fragile-goods-breakage theory with actual return/damage data across Home & Living, and (C) run the same product-level breakdown for Fennel & Flint's December reviews that we did for Wiesgrund, to see if their decline is broad-based or concentrated.
The engineer · 2 dry runs on the empty schema, then the submission
WITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01' ), bundles AS ( SELECT DATE_TRUNC('month', created_at)::date AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1 ) SELECT TO_CHAR(m.month_start, 'YYYY-MM') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundle FROM months m LEFT JOIN bundles b ON b.month_start = m.month_start ORDER BY m.month_start
WITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01'),bundles AS ( SELECT DATE_TRUNC('month', created_at)::date AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1)SELECT TO_CHAR(m.month_start, 'YYYY-MM') AS month, STRFTIME(m.month_start, '%Y-%m') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundleFROM months mLEFT JOIN bundles b ON b.month_start = m.month_startORDER BY m.month_start
“Query compiles correctly. Submitting.”
“Submitted. The report will show bundle creation counts and distinct tenant counts per month across the full 2023–2025 calendar, which should make it easy to see whether creation activity is spread out (organic) or piled into one or two months (seeding event).”
As Zara Ahmed saw it, from the small dataset the agents worked on
month | bundles_created_count | distinct_tenants_creating_a_bundle --------+-----------------------+----------------------------------- 2023-01 | 2 | 1 2023-02 | 0 | 0 2023-03 | 6 | 2 2023-04 | 4 | 1 2023-05 | 4 | 1 2023-06 | 1 | 1 2023-07 | 0 | 0 2023-08 | 0 | 0 2023-09 | 0 | 0 2023-10 | 0 | 0 2023-11 | 1 | 1 2023-12 | 2 | 1 2024-01 | 1 | 1 2024-02 | 0 | 0 2024-03 | 0 | 0 2024-04 | 0 | 0 2024-05 | 3 | 1 2024-06 | 2 | 1 2024-07 | 1 | 1 2024-08 | 5 | 3 2024-09 | 0 | 0 2024-10 | 9 | 3 2024-11 | 0 | 0 2024-12 | 3 | 1 2025-01 | 0 | 0 2025-02 | 0 | 0 2025-03 | 3 | 1 2025-04 | 2 | 1 2025-05 | 8 | 4 2025-06 | 1 | 1 2025-07 | 1 | 1 2025-08 | 6 | 2 2025-09 | 7 | 3 2025-10 | 0 | 0 2025-11 | 0 | 0 2025-12 | 0 | 0 (36 rows)
The memo and the queries that followed; links found by title, so inferred
It builds on:
Changed only where the benchmark needs the same result on every system
WITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01'),bundles AS ( SELECT DATE_TRUNC('month', created_at)::date AS month_start, CAST(DATE_TRUNC('month', created_at) AS date) AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1)SELECT STRFTIME(m.month_start, '%Y-%m') AS month, {{year_month(m.month_start)}} AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundleFROM months mLEFT JOIN bundles b ON b.month_start = m.month_startORDER BY m.month_startORDER BY m.month_start NULLS LAST
WITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01' ), bundles AS ( SELECT CAST(DATE_TRUNC('month', created_at) AS date) AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1 ) SELECT strftime(m.month_start, '%Y-%m') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundle FROM months m LEFT JOIN bundles b ON b.month_start = m.month_start ORDER BY m.month_start NULLS LAST
WITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01' ), bundles AS ( SELECT CAST(DATE_TRUNC('month', created_at) AS date) AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1 ) SELECT to_char(m.month_start, 'YYYY-MM') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundle FROM months m LEFT JOIN bundles b ON b.month_start = m.month_start ORDER BY m.month_start NULLS LAST
WITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01' ), bundles AS ( SELECT CAST(DATE_TRUNC('month', created_at) AS date) AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1 ) SELECT to_char(CAST(m.month_start AS timestamp), 'YYYY-MM') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundle FROM months m LEFT JOIN bundles b ON b.month_start = m.month_start ORDER BY m.month_start NULLS LAST
WITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01' ), bundles AS ( SELECT CAST(DATE_TRUNC('month', created_at) AS date) AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1 ) SELECT formatDateTime(m.month_start, '%Y-%m') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundle FROM months m LEFT JOIN bundles b ON b.month_start = m.month_start ORDER BY m.month_start 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 Zara Ahmed (analyst for the Head of Merchandising): Title: Bundle Creation Timing: Seeded in a Batch or Built Up Organically? Question: Does the suspiciously clean 100%/0% bundle adoption split by vertical look like genuine, ongoing merchant activity, or does it look like a one-time data-seeding event? Grain: one row per calendar month, 2023-01 through 2025-12 Columns: - month - bundles_created_count - distinct_tenants_creating_a_bundle Details: Table: product_bundles. bundles_created_count = count of product_bundles rows with created_at in that month, across all tenants. distinct_tenants_creating_a_bundle = count of distinct tenant_id values among those rows. Build off the full calendar so every month appears even with zero bundles created. Order by month ascending. If the counts cluster heavily into one or two months, that points to a seeding/backfill event rather than organic, ongoing adoption. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01' ), bundles AS ( SELECT DATE_TRUNC('month', created_at)::date AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1 ) SELECT TO_CHAR(m.month_start, 'YYYY-MM') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundle FROM months m LEFT JOIN bundles b ON b.month_start = m.month_start ORDER BY m.month_start
STATUS: REJECTED - does not compile: Catalog Error: Scalar Function with name to_char does not exist!
dry_runWITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01' ), bundles AS ( SELECT DATE_TRUNC('month', created_at)::date AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1 ) SELECT STRFTIME(m.month_start, '%Y-%m') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundle FROM months m LEFT JOIN bundles b ON b.month_start = m.month_start ORDER BY m.month_start
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: month, bundles_created_count, distinct_tenants_creating_a_bundle
submitWITH months AS ( SELECT DISTINCT month_start FROM calendar_days WHERE month_start >= DATE '2023-01-01' AND month_start <= DATE '2025-12-01' ), bundles AS ( SELECT DATE_TRUNC('month', created_at)::date AS month_start, COUNT(*) AS bundles_created_count, COUNT(DISTINCT tenant_id) AS distinct_tenants_creating_a_bundle FROM product_bundles GROUP BY 1 ) SELECT STRFTIME(m.month_start, '%Y-%m') AS month, COALESCE(b.bundles_created_count, 0) AS bundles_created_count, COALESCE(b.distinct_tenants_creating_a_bundle, 0) AS distinct_tenants_creating_a_bundle FROM months m LEFT JOIN bundles b ON b.month_start = m.month_start ORDER BY m.month_start
Submitted to tonight's batch. Your work on this request is done.