Queries · q358 · Head of Merchandising · week 2, night 4
“Is Fennel & Flint's declining rating trend concentrated in specific products, or spread broadly across its catalog, in parallel to what we found at Wiesgrund Wohnen?”
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 3 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 · 1 dry run on the empty schema, then the submission
SELECT p.name AS product_name, c.name AS category_name, (SELECT COUNT(*) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS review_count, (SELECT ROUND(AVG(pr.rating)::numeric, 2) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS avg_rating, (SELECT ROUND(100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*), 1) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS pct_1_2_star, (SELECT COALESCE(SUM(oi.quantity), 0) FROM order_items oi JOIN orders o ON o.id = oi.order_id WHERE oi.tenant_id = p.tenant_id AND o.tenant_id = p.tenant_id AND o.status != 'cancelled' AND o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND oi.product_variant_id IN (SELECT pv.id FROM product_variants pv WHERE pv.product_id = p.id) ) AS units_sold_last_12mo FROM products p LEFT JOIN categories c ON c.id = p.primary_category_id WHERE p.tenant_id = (SELECT t.id FROM tenants t WHERE t.name = 'Fennel & Flint') AND EXISTS ( SELECT 1 FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) ORDER BY review_count DESC, avg_rating ASC
“Query compiles cleanly. Submitting.”
“Submitted. The report returns one row per Fennel & Flint product with at least one approved December 2025 review, showing product name, category, review count, average rating, share of 1–2 star reviews, and units sold in 2025 (non-cancelled orders), ordered by review count descending then average rating ascending — matching the grain and definitions used in the Wiesgrund Wohnen report.”
As Zara Ahmed saw it, from the small dataset the agents worked on
product_name | category_name | review_count | avg_rating | pct_1_2_star | units_sold_last_12mo -----------------------------------------+---------------+--------------+------------+--------------+--------------------- Sea Salt Candle | Decor | 1 | 1.00 | 100.0 | 6 Washed Organic Cotton Duvet Cover | Bedding | 1 | 1.00 | 100.0 | 27 Fluted Recycled Glass Vase | Decor | 1 | 1.00 | 100.0 | 10 Reactive Glaze Set of 4 Dinner Plate Set | Tableware | 1 | 1.00 | 100.0 | 10 Retro Kettle | Appliances | 1 | 2.00 | 100.0 | 3 Cedar & Smoke Candle | Decor | 1 | 3.00 | 0.0 | 14 Fringed Throw | Textiles | 1 | 3.00 | 0.0 | 19 Round 24cm Enamelled Cast Iron Casserole | Cookware | 1 | 3.00 | 0.0 | 4 Carbon Steel 26cm Frying Pan | Cookware | 1 | 4.00 | 0.0 | 27 Santoku 18cm Chef's Knife | Cookware | 1 | 5.00 | 0.0 | 3 Plush Organic Cotton Bath Towel | Towels | 1 | 5.00 | 0.0 | 13 Tumblers Set of 4 Glassware Set | Glassware | 1 | 5.00 | 0.0 | 26 Round 24cm Cast Iron Casserole Dish | Cookware | 1 | 5.00 | 0.0 | 6 Percale Organic Cotton Duvet Cover | Bedding | 1 | 5.00 | 0.0 | 5 (14 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
SELECT p.name AS product_name, c.name AS category_name, (SELECT COUNT(*) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS review_count, (SELECT ROUND(AVG(pr.rating)::numeric, 2) FROM product_reviews pr (SELECT AVG(CAST(pr.rating AS NUMERIC(18,6))) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS avg_rating, (SELECT ROUND(100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*), 1) FROM product_reviews pr (SELECT 100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS pct_1_2_star, (SELECT COALESCE(SUM(oi.quantity), 0) FROM order_items oi JOIN orders o ON o.id = oi.order_id WHERE oi.tenant_id = p.tenant_id AND o.tenant_id = p.tenant_id AND o.status != 'cancelled' AND o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND oi.product_variant_id IN (SELECT pv.id FROM product_variants pv WHERE pv.product_id = p.id) ) AS units_sold_last_12moFROM products pLEFT JOIN categories c ON c.id = p.primary_category_idWHERE p.tenant_id = (SELECT t.id FROM tenants t WHERE t.name = 'Fennel & Flint')WHERE p.tenant_id = (SELECT t.id AS id FROM tenants t WHERE t.name = 'Fennel & Flint') AND EXISTS ( SELECT 1 FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' )ORDER BY review_count DESC, avg_rating ASCORDER BY review_count DESC NULLS FIRST, avg_rating ASC NULLS LAST, p.id ASC NULLS LAST
SELECT p.name AS product_name, c.name AS category_name, (SELECT COUNT(*) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS review_count, (SELECT AVG(CAST(pr.rating AS NUMERIC(18,6))) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS avg_rating, (SELECT 100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS pct_1_2_star, (SELECT COALESCE(SUM(oi.quantity), 0) FROM order_items oi JOIN orders o ON o.id = oi.order_id WHERE oi.tenant_id = p.tenant_id AND o.tenant_id = p.tenant_id AND o.status != 'cancelled' AND o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND oi.product_variant_id IN (SELECT pv.id FROM product_variants pv WHERE pv.product_id = p.id) ) AS units_sold_last_12mo FROM products p LEFT JOIN categories c ON c.id = p.primary_category_id WHERE p.tenant_id = (SELECT t.id AS id FROM tenants t WHERE t.name = 'Fennel & Flint') AND EXISTS ( SELECT 1 FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) ORDER BY review_count DESC NULLS FIRST, avg_rating ASC NULLS LAST, p.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: Fennel & Flint December 2025: Product-Level Review Breakdown Question: Is Fennel & Flint's declining rating trend concentrated in specific products, or spread broadly across its catalog, in parallel to what we found at Wiesgrund Wohnen? Grain: one row per product, for Fennel & Flint only, among products with at least one approved review created in December 2025 Columns: - product_name - category_name - review_count - avg_rating - pct_1_2_star - units_sold_last_12mo Details: Same definitions as the Wiesgrund Wohnen December 2025: Product-Level Review Breakdown report, filtered to tenant = Fennel & Flint instead: review_count/avg_rating/pct_1_2_star over approved product_reviews with created_at in December 2025, category_name from primary_category_id, units_sold_last_12mo from order_items/orders placed in 2025 with status not cancelled. Round avg_rating to 2 decimals, pct to 1 decimal. Order by review_count descending, tie-break by avg_rating ascending. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT p.name AS product_name, c.name AS category_name, (SELECT COUNT(*) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS review_count, (SELECT ROUND(AVG(pr.rating)::numeric, 2) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS avg_rating, (SELECT ROUND(100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*), 1) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS pct_1_2_star, (SELECT COALESCE(SUM(oi.quantity), 0) FROM order_items oi JOIN orders o ON o.id = oi.order_id WHERE oi.tenant_id = p.tenant_id AND o.tenant_id = p.tenant_id AND o.status != 'cancelled' AND o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND oi.product_variant_id IN (SELECT pv.id FROM product_variants pv WHERE pv.product_id = p.id) ) AS units_sold_last_12mo FROM products p LEFT JOIN categories c ON c.id = p.primary_category_id WHERE p.tenant_id = (SELECT t.id FROM tenants t WHERE t.name = 'Fennel & Flint') AND EXISTS ( SELECT 1 FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) ORDER BY review_count DESC, avg_rating ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: product_name, category_name, review_count, avg_rating, pct_1_2_star, units_sold_last_12mo
submitSELECT p.name AS product_name, c.name AS category_name, (SELECT COUNT(*) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS review_count, (SELECT ROUND(AVG(pr.rating)::numeric, 2) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS avg_rating, (SELECT ROUND(100.0 * SUM(CASE WHEN pr.rating <= 2 THEN 1 ELSE 0 END) / COUNT(*), 1) FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) AS pct_1_2_star, (SELECT COALESCE(SUM(oi.quantity), 0) FROM order_items oi JOIN orders o ON o.id = oi.order_id WHERE oi.tenant_id = p.tenant_id AND o.tenant_id = p.tenant_id AND o.status != 'cancelled' AND o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' AND oi.product_variant_id IN (SELECT pv.id FROM product_variants pv WHERE pv.product_id = p.id) ) AS units_sold_last_12mo FROM products p LEFT JOIN categories c ON c.id = p.primary_category_id WHERE p.tenant_id = (SELECT t.id FROM tenants t WHERE t.name = 'Fennel & Flint') AND EXISTS ( SELECT 1 FROM product_reviews pr WHERE pr.product_id = p.id AND pr.tenant_id = p.tenant_id AND pr.is_approved = true AND pr.created_at >= DATE '2025-12-01' AND pr.created_at < DATE '2026-01-01' ) ORDER BY review_count DESC, avg_rating ASC
Submitted to tonight's batch. Your work on this request is done.