Queries · q353 · Head of Merchandising · week 2, night 3
“Since delivery performance doesn't explain Wiesgrund Wohnen's December dip, which specific products drove it?”
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 3, request 1 of 3
Night 2 findings: (1) The shipping-delay hypothesis for the Dec dip holds for Fennel & Flint with a plausible lag — their on-time delivery crashed to 53.7% in November (from ~85-88% normally) and their bad reviews peaked the following month, December (14 reviews, 3.14 avg, 35.7% 1-2 star). But it does NOT explain Wiesgrund Wohnen at all: their delivery performance stayed strong all through Nov-Dec (90.9%, 93.8%) and they used 0% Albion Parcel every single month — yet they had an equally bad December (12 reviews, 3.5 avg, 41.7% 1-2 star). So there are two different causes behind what looked like one 'Home & Living dip,' and I need to find Wiesgrund Wohnen's actual driver. (2) The image-bucket report, run without the active-only filter this time, overturned my 'maybe they're just new/unfinished listings' theory: the 85 zero-image products are actually the OLDEST on average (1,136 days vs ~1,040 for others) and have fewer variants (3.4 vs ~5.5) — these are neglected legacy active listings, not pending-content new ones, and notably zero B2B merchants have any zero-image products (B2B is always at least minimally photographed). So the zero-to-one-image effect looks like a real, if small and old, content gap worth cleaning up, not a proxy for recency. (3) The bundle-user list makes the pattern obvious just from eyeballing the 50 rows: every single bundle-using merchant is in Beauty, Food & Coffee, Books & Stationery, Jewelry, Consumer Electronics, or Home & Living — none are Apparel & Footwear, Sports & Outdoor, or B2B wholesale, and they skew toward low-variant, giftable, small-revenue merchants. I want one clean aggregate to confirm this is a true vertical pattern and not an artifact of which merchants happened to print. Tonight: drill into Wiesgrund Wohnen's actual December driver at the product level, check whether both merchants' December dips are a recurring yearly pattern (like Cragsley's) or unique to 2025, and confirm the bundle-vertical clustering with hard numbers.
The engineer · 1 dry run on the empty schema, then the submission
SELECT p.name as product_name, c.name as category_name, r.review_count, r.avg_rating, r.pct_1_2_star, (SELECT COALESCE(SUM(oi.quantity),0) FROM order_items oi JOIN orders o ON o.id = oi.order_id WHERE o.tenant_id = p.tenant_id AND o.status <> 'cancelled' AND o.placed_at >= timestamp '2025-01-01' AND o.placed_at < timestamp '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 JOIN categories c ON c.id = p.primary_category_id JOIN ( SELECT pr.product_id, COUNT(*) AS review_count, ROUND(AVG(pr.rating),2) AS avg_rating, ROUND(100.0 * SUM(CASE WHEN pr.rating IN (1,2) THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_1_2_star FROM product_reviews pr WHERE pr.is_approved = true AND pr.created_at >= timestamp '2025-12-01' AND pr.created_at < timestamp '2026-01-01' AND pr.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') GROUP BY pr.product_id ) r ON r.product_id = p.id WHERE p.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') ORDER BY r.review_count DESC, r.avg_rating ASC, p.name ASC
“Query compiles and returns the expected columns. Submitting.”
“Submitted. The report returns one row per Wiesgrund Wohnen product with at least one approved December 2025 review, showing review volume/rating stats alongside trailing-12-month unit sales for context, ordered by review count (desc) then average rating (asc).”
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 ------------------------------------+---------------+--------------+------------+--------------+--------------------- Stainless 24cm Frying Pan | Cookware | 2 | 4.0 | 0.0 | 7 20cm Chef's Knife | Cookware | 2 | 5.0 | 0.0 | 26 Fluted Stoneware Vase | Decor | 1 | 1.0 | 100.0 | 12 Carbon Steel 26cm Frying Pan | Cookware | 1 | 2.0 | 100.0 | 2 Fluted Ceramic Vase | Decor | 1 | 2.0 | 100.0 | 1 Set of 4 Stoneware Dinner Plate Set | Tableware | 1 | 2.0 | 100.0 | 11 Shallow 28cm Casserole Dish | Cookware | 1 | 2.0 | 100.0 | 18 Bud Recycled Glass Vase | Decor | 1 | 5.0 | 0.0 | 5 Plush Bath Towel | Towels | 1 | 5.0 | 0.0 | 24 Tumblers Set of 4 Glassware Set | Glassware | 1 | 5.0 | 0.0 | 20 (10 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 p.name as product_name, c.name as category_name, r.review_count, r.avg_rating, r.pct_1_2_star, r.review_count AS review_count, r.avg_rating AS avg_rating, r.pct_1_2_star 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 o.tenant_id = p.tenant_id AND o.status <> 'cancelled' AND o.placed_at >= timestamp '2025-01-01' AND o.placed_at < timestamp '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 pJOIN categories c ON c.id = p.primary_category_idJOIN ( SELECT pr.product_id, SELECT pr.product_id AS product_id, COUNT(*) AS review_count, ROUND(AVG(pr.rating),2) AS avg_rating, ROUND(100.0 * SUM(CASE WHEN pr.rating IN (1,2) 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 IN (1,2) THEN 1 ELSE 0 END) / COUNT(*) AS pct_1_2_star FROM product_reviews pr WHERE pr.is_approved = true AND pr.created_at >= timestamp '2025-12-01' AND pr.created_at < timestamp '2026-01-01' AND pr.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') GROUP BY pr.product_id) r ON r.product_id = p.idWHERE p.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen')ORDER BY r.review_count DESC, r.avg_rating ASC, p.name ASCORDER BY r.review_count DESC NULLS FIRST, r.avg_rating ASC NULLS LAST, p.name ASC NULLS LAST, p.id ASC NULLS LAST
SELECT p.name as product_name, c.name as category_name, r.review_count AS review_count, r.avg_rating AS avg_rating, r.pct_1_2_star 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 o.tenant_id = p.tenant_id AND o.status <> 'cancelled' AND o.placed_at >= timestamp '2025-01-01' AND o.placed_at < timestamp '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 JOIN categories c ON c.id = p.primary_category_id JOIN ( SELECT pr.product_id AS product_id, COUNT(*) AS review_count, AVG(CAST(pr.rating AS NUMERIC(18,6))) AS avg_rating, 100.0 * SUM(CASE WHEN pr.rating IN (1,2) THEN 1 ELSE 0 END) / COUNT(*) AS pct_1_2_star FROM product_reviews pr WHERE pr.is_approved = true AND pr.created_at >= timestamp '2025-12-01' AND pr.created_at < timestamp '2026-01-01' AND pr.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') GROUP BY pr.product_id ) r ON r.product_id = p.id WHERE p.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') ORDER BY r.review_count DESC NULLS FIRST, r.avg_rating ASC NULLS LAST, p.name 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: Wiesgrund Wohnen December 2025: Product-Level Review Breakdown Question: Since delivery performance doesn't explain Wiesgrund Wohnen's December dip, which specific products drove it? Grain: one row per product, for Wiesgrund Wohnen 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: Filter to tenant = Wiesgrund Wohnen. review_count/avg_rating/pct_1_2_star computed over approved product_reviews with created_at between 2025-12-01 and 2025-12-31, per product. category_name from products.primary_category_id -> categories.name. units_sold_last_12mo = sum of order_items.quantity for that product's variants from orders placed 2025-01-01 to 2025-12-31 with status not cancelled, for context on whether it's a popular or niche item. 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, r.review_count, r.avg_rating, r.pct_1_2_star, (SELECT COALESCE(SUM(oi.quantity),0) FROM order_items oi JOIN orders o ON o.id = oi.order_id WHERE o.tenant_id = p.tenant_id AND o.status <> 'cancelled' AND o.placed_at >= timestamp '2025-01-01' AND o.placed_at < timestamp '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 JOIN categories c ON c.id = p.primary_category_id JOIN ( SELECT pr.product_id, COUNT(*) AS review_count, ROUND(AVG(pr.rating),2) AS avg_rating, ROUND(100.0 * SUM(CASE WHEN pr.rating IN (1,2) THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_1_2_star FROM product_reviews pr WHERE pr.is_approved = true AND pr.created_at >= timestamp '2025-12-01' AND pr.created_at < timestamp '2026-01-01' AND pr.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') GROUP BY pr.product_id ) r ON r.product_id = p.id WHERE p.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') ORDER BY r.review_count DESC, r.avg_rating ASC, p.name 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, r.review_count, r.avg_rating, r.pct_1_2_star, (SELECT COALESCE(SUM(oi.quantity),0) FROM order_items oi JOIN orders o ON o.id = oi.order_id WHERE o.tenant_id = p.tenant_id AND o.status <> 'cancelled' AND o.placed_at >= timestamp '2025-01-01' AND o.placed_at < timestamp '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 JOIN categories c ON c.id = p.primary_category_id JOIN ( SELECT pr.product_id, COUNT(*) AS review_count, ROUND(AVG(pr.rating),2) AS avg_rating, ROUND(100.0 * SUM(CASE WHEN pr.rating IN (1,2) THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_1_2_star FROM product_reviews pr WHERE pr.is_approved = true AND pr.created_at >= timestamp '2025-12-01' AND pr.created_at < timestamp '2026-01-01' AND pr.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') GROUP BY pr.product_id ) r ON r.product_id = p.id WHERE p.tenant_id = (SELECT id FROM tenants WHERE name = 'Wiesgrund Wohnen') ORDER BY r.review_count DESC, r.avg_rating ASC, p.name ASC
Submitted to tonight's batch. Your work on this request is done.