Queries · q142 · Head of Merchandising · week 1, night 3
“Within Home & Living, is the December 2025 rating decline concentrated in a few merchants or spread across the vertical?”
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 1
Subject: My first-week priorities — catalog questions
Hi Zara,
Great to have you helping me get oriented this week. I know slots are tight, so here's what I most want to understand first. Pick the ones you think will give us the clearest picture given the report budget — happy to defer the rest to next week.
Let me know what's realistic to cover by Friday, and flag if any of these need to be split into smaller questions on your end.
Thanks, Freya
The analyst · week 1, night 3, request 1 of 3
Night 2 takeaways: (1) Even within Apparel & Footwear alone, the richness story is not a clean monotonic 'more images = more sales' curve — the big jump is between 0 images (109 EUR/product, and these products also have far fewer variants, ~4 vs ~13, suggesting they're incomplete/new listings) and 1+ images (355-399 EUR/product); beyond that, 2-3/4-6/7+ bounce around without a clear ladder. So the honest message for Freya is 'baseline completeness matters a lot, marginal richness less so' rather than 'more is always better'. (2) Reviews-per-100-orders is noisy but flat (~10-20%) across the whole 3-year window with no trend — so the rising raw review counts are explained by rising order volume, not growing review engagement; December 2025's high avg_rating=3.71 dip is a ratings-quality story, not a volume-of-reviews story (its reviews-per-100-orders, 15.16, is unremarkable). (3) The vertical breakdown shows Home & Living had the sharpest, most volume-credible decline (3.86→3.36, n=42→59, 35.6% 1-2 star), while Apparel & Footwear's decline is milder (-0.11) but carries about half of all December reviews (219 of ~454), so it matters in absolute terms too. Tonight I want to localize the Home & Living dip to specific merchants (one bad actor vs widespread), get a concrete worst-rated-products list platform-wide to ground Q3's 'trouble spots' ask, and open up Q4 (collections/bundles adoption vs performance), which I haven't touched yet. I'm holding the few-reviews-despite-good-sales angle and the dedicated top-vs-long-tail structural comparison (Q5) for night 4, since night 3's findings may reshape what's worth asking there.
The engineer · 1 dry run on the empty schema, then the submission
SELECT t.name AS merchant_name, d.review_count AS dec2025_review_count, d.avg_rating AS dec2025_avg_rating, d.pct_1_2_star AS dec2025_pct_1_2_star, j.avg_rating AS jan_to_nov_2025_avg_rating, ROUND(d.avg_rating - j.avg_rating, 2) AS rating_change_pts FROM tenants t JOIN ( SELECT p.tenant_id, COUNT(*) AS review_count, ROUND(AVG(pr.rating), 2) AS avg_rating, ROUND(100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_1_2_star FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' GROUP BY p.tenant_id ) d ON d.tenant_id = t.id JOIN ( SELECT p.tenant_id, ROUND(AVG(pr.rating), 2) AS avg_rating FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-01-01' AND pr.created_at < DATE '2025-12-01' GROUP BY p.tenant_id ) j ON j.tenant_id = t.id WHERE t.industry = 'Home & Living' ORDER BY rating_change_pts ASC, d.review_count DESC
“Query compiles correctly. Submitting now.”
“Submitted. The report joins product_reviews → products → tenants, scoped to approved reviews in the Home & Living industry, with separate December 2025 and January–November 2025 aggregates per merchant, ordered by biggest rating decline first.”
As Zara Ahmed saw it, from the small dataset the agents worked on
merchant_name | dec2025_review_count | dec2025_avg_rating | dec2025_pct_1_2_star | jan_to_nov_2025_avg_rating | rating_change_pts --------------------+----------------------+--------------------+----------------------+----------------------------+------------------ Willow & Wren | 4 | 1.5 | 75.0 | 3.54 | -2.04 Haven Linen | 1 | 2.0 | 100.0 | 4.0 | -2.0 Wohnglück | 3 | 3.0 | 33.3 | 4.57 | -1.57 Fern & Fable | 1 | 3.0 | 0.0 | 4.14 | -1.14 Driftwood Home | 1 | 2.0 | 100.0 | 3.0 | -1.0 Fennel & Flint | 14 | 3.14 | 35.7 | 4.11 | -0.97 Coastline Interiors | 6 | 3.67 | 33.3 | 4.29 | -0.62 Wiesgrund Wohnen | 12 | 3.5 | 41.7 | 4.1 | -0.6 Hus & Hem | 1 | 3.0 | 0.0 | 3.4 | -0.4 Linden & Loom | 8 | 3.88 | 25.0 | 4.18 | -0.3 Lamplight Living | 1 | 4.0 | 0.0 | 4.2 | -0.2 Kettle & Key | 3 | 3.67 | 33.3 | 3.67 | 0.0 Tiny Atelier | 1 | 5.0 | 0.0 | 4.25 | 0.75 (13 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 t.name AS merchant_name, d.review_count AS dec2025_review_count, d.avg_rating AS dec2025_avg_rating, d.pct_1_2_star AS dec2025_pct_1_2_star, j.avg_rating AS jan_to_nov_2025_avg_rating, ROUND(d.avg_rating - j.avg_rating, 2) AS rating_change_pts d.avg_rating - j.avg_rating AS rating_change_ptsFROM tenants tJOIN ( SELECT p.tenant_id, SELECT p.tenant_id AS tenant_id, COUNT(*) AS review_count, ROUND(AVG(pr.rating), 2) AS avg_rating, ROUND(100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_1_2_star AVG(CAST(pr.rating AS NUMERIC(18,6))) AS avg_rating, 100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*) AS pct_1_2_star FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' GROUP BY p.tenant_id) d ON d.tenant_id = t.idJOIN ( SELECT p.tenant_id, ROUND(AVG(pr.rating), 2) AS avg_rating SELECT p.tenant_id AS tenant_id, AVG(CAST(pr.rating AS NUMERIC(18,6))) AS avg_rating FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-01-01' AND pr.created_at < DATE '2025-12-01' GROUP BY p.tenant_id) j ON j.tenant_id = t.idWHERE t.industry = 'Home & Living'ORDER BY rating_change_pts ASC, d.review_count DESCORDER BY rating_change_pts ASC NULLS LAST, d.review_count DESC NULLS FIRST, t.id ASC NULLS LAST
SELECT t.name AS merchant_name, d.review_count AS dec2025_review_count, d.avg_rating AS dec2025_avg_rating, d.pct_1_2_star AS dec2025_pct_1_2_star, j.avg_rating AS jan_to_nov_2025_avg_rating, d.avg_rating - j.avg_rating AS rating_change_pts FROM tenants t JOIN ( SELECT p.tenant_id AS tenant_id, COUNT(*) AS review_count, AVG(CAST(pr.rating AS NUMERIC(18,6))) AS avg_rating, 100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*) AS pct_1_2_star FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' GROUP BY p.tenant_id ) d ON d.tenant_id = t.id JOIN ( SELECT p.tenant_id AS tenant_id, AVG(CAST(pr.rating AS NUMERIC(18,6))) AS avg_rating FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-01-01' AND pr.created_at < DATE '2025-12-01' GROUP BY p.tenant_id ) j ON j.tenant_id = t.id WHERE t.industry = 'Home & Living' ORDER BY rating_change_pts ASC NULLS LAST, d.review_count DESC NULLS FIRST, t.id 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 Zara Ahmed (analyst for the Head of Merchandising): Title: Home & Living December 2025 Rating Dip: Merchant Breakdown Question: Within Home & Living, is the December 2025 rating decline concentrated in a few merchants or spread across the vertical? Grain: one row per merchant (tenant) in the Home & Living industry Columns: - merchant_name - dec2025_review_count - dec2025_avg_rating - dec2025_pct_1_2_star - jan_to_nov_2025_avg_rating - rating_change_pts Details: Join product_reviews (is_approved = true) to products to tenants, filtered to tenants.industry = 'Home & Living'. dec2025_review_count / dec2025_avg_rating / dec2025_pct_1_2_star are computed over approved reviews with created_at in December 2025 (2025-12-01 to 2025-12-31) for that merchant. jan_to_nov_2025_avg_rating is the same merchant's mean approved-review rating for created_at in January-November 2025 (2025-01-01 to 2025-11-30), used as a pre-dip baseline. rating_change_pts = dec2025_avg_rating minus jan_to_nov_2025_avg_rating, rounded to 2 decimals (more negative = bigger relative decline). Only include merchants with at least 1 review in December 2025. Round avg ratings to 2 decimals, pct to 1 decimal. Order by rating_change_pts ascending (biggest decline first), tie-break by dec2025_review_count descending. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT t.name AS merchant_name, d.review_count AS dec2025_review_count, d.avg_rating AS dec2025_avg_rating, d.pct_1_2_star AS dec2025_pct_1_2_star, j.avg_rating AS jan_to_nov_2025_avg_rating, ROUND(d.avg_rating - j.avg_rating, 2) AS rating_change_pts FROM tenants t JOIN ( SELECT p.tenant_id, COUNT(*) AS review_count, ROUND(AVG(pr.rating), 2) AS avg_rating, ROUND(100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_1_2_star FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' GROUP BY p.tenant_id ) d ON d.tenant_id = t.id JOIN ( SELECT p.tenant_id, ROUND(AVG(pr.rating), 2) AS avg_rating FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-01-01' AND pr.created_at < DATE '2025-12-01' GROUP BY p.tenant_id ) j ON j.tenant_id = t.id WHERE t.industry = 'Home & Living' ORDER BY rating_change_pts ASC, d.review_count DESC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: merchant_name, dec2025_review_count, dec2025_avg_rating, dec2025_pct_1_2_star, jan_to_nov_2025_avg_rating, rating_change_pts
submitSELECT t.name AS merchant_name, d.review_count AS dec2025_review_count, d.avg_rating AS dec2025_avg_rating, d.pct_1_2_star AS dec2025_pct_1_2_star, j.avg_rating AS jan_to_nov_2025_avg_rating, ROUND(d.avg_rating - j.avg_rating, 2) AS rating_change_pts FROM tenants t JOIN ( SELECT p.tenant_id, COUNT(*) AS review_count, ROUND(AVG(pr.rating), 2) AS avg_rating, ROUND(100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_1_2_star FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' GROUP BY p.tenant_id ) d ON d.tenant_id = t.id JOIN ( SELECT p.tenant_id, ROUND(AVG(pr.rating), 2) AS avg_rating FROM product_reviews pr JOIN products p ON pr.product_id = p.id WHERE pr.is_approved = true AND pr.created_at >= DATE '2025-01-01' AND pr.created_at < DATE '2025-12-01' GROUP BY p.tenant_id ) j ON j.tenant_id = t.id WHERE t.industry = 'Home & Living' ORDER BY rating_change_pts ASC, d.review_count DESC
Submitted to tonight's batch. Your work on this request is done.