Queries · q055 · Head of Growth Marketing · week 1, night 3
“Does the 'discounted orders have higher AOV, bigger baskets, and fewer returns' pattern hold in consumer verticals, or is it mainly an artifact of B2B wholesale contract pricing?”
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: What I need to get oriented this week
Hi Sofie,
Thrilled to be here — here's where my head's at. I know you've only got a handful of report slots a night, so I've ordered these by what I need most. Please push back if any of these are too broad or should split into more than one report.
What I need this for: I'm meeting the leadership team Friday and want a grounded, numbers-based view of growth marketing's current state before I propose anything. No need for polish — I just want to know what's really happening so I don't propose things that are already working, or miss things that are quietly broken.
Thanks for taking this on — let me know if you want me to cut this list down.
Chloé
The analyst · week 1, night 3, request 1 of 3
The Jan/Jul discount-rate report flagged that two B2B wholesalers (Castell Fasteners, OfficeHub Nordic) run discounts on 65-95% of orders even outside the spike months — that's almost certainly negotiated/contract pricing, not promotional codes. That's a real caveat to last night's 'discounted orders look healthier' finding (higher AOV, more items, fewer returns), since B2B bulk orders would naturally look that way regardless of discounting. Before I tell Chloé 'discounting is healthy,' I need to check it holds in consumer verticals, not just because B2B is mixed in. That's tonight's top priority. With that resolved, I'm moving on to orientation baselines for Q3 (email/campaign engagement) and Q4 (loyalty), since two nights remain and those questions haven't been touched yet. Direct-traffic concentration (Report 1 last night) came back as a straightforward big-merchant-volume story, not an anomaly, so I'm not spending a slot chasing it further.
The engineer · 1 dry run on the empty schema, then the submission
WITH order_items_agg AS ( SELECT order_id, SUM(quantity) AS item_qty FROM order_items GROUP BY order_id ), order_returns_flag AS ( SELECT DISTINCT order_id FROM order_returns ), base AS ( SELECT o.id AS order_id, t.industry AS industry, CASE WHEN o.discount_total > 0 THEN 'discounted' ELSE 'full price' END AS order_group, o.status AS status, o.grand_total AS grand_total, o.currency_code AS currency_code, o.placed_at AS placed_at, COALESCE(oi.item_qty, 0) AS item_qty, COALESCE(er.rate, CASE WHEN o.currency_code = 'EUR' THEN 1 END) AS eur_rate FROM orders o JOIN tenants t ON t.id = o.tenant_id LEFT JOIN order_items_agg oi ON oi.order_id = o.id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = CAST(o.placed_at AS date) WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' ) SELECT b.industry AS industry, b.order_group AS order_group, COUNT(*) AS orders_count, ROUND(AVG(b.grand_total / b.eur_rate), 0) AS avg_order_value_eur, ROUND(AVG(b.item_qty), 1) AS avg_items_per_order, ROUND(100.0 * SUM(CASE WHEN rf.order_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*), 1) AS return_rate_pct, ROUND(100.0 * SUM(CASE WHEN b.status = 'cancelled' THEN 1 ELSE 0 END) / COUNT(*), 1) AS cancelled_rate_pct FROM base b LEFT JOIN order_returns_flag rf ON rf.order_id = b.order_id GROUP BY b.industry, b.order_group ORDER BY b.industry ASC, b.order_group ASC
“Query compiles and returns the expected columns. Submitting.”
“Submitted. The report groups orders by tenants.industry × discounted/full-price, computing EUR-converted AOV, average items per order, return rate, and cancellation rate for 2025 orders — matching the prior night's methodology but crossed with vertical.”
As Sofie Nilsen saw it, from the small dataset the agents worked on
industry | order_group | orders_count | avg_order_value_eur | avg_items_per_order | return_rate_pct | cancelled_rate_pct --------------------------+-------------+--------------+---------------------+---------------------+-----------------+------------------- Apparel & Footwear | discounted | 1131 | 172.0 | 2.9 | 39.0 | 5.3 Apparel & Footwear | full price | 6044 | 186.0 | 2.6 | 34.5 | 4.4 Beauty | discounted | 346 | 68.0 | 2.8 | 7.8 | 4.6 Beauty | full price | 1220 | 104.0 | 2.6 | 4.1 | 4.7 Books & Stationery | discounted | 8 | 35.0 | 3.4 | 0.0 | 0.0 Books & Stationery | full price | 151 | 53.0 | 3.0 | 6.0 | 3.3 Consumer Electronics | discounted | 155 | 813.0 | 2.7 | 17.4 | 1.9 Consumer Electronics | full price | 421 | 1135.0 | 2.4 | 10.7 | 4.3 Food & Coffee | discounted | 4365 | 6.0 | 1.3 | 0.3 | 0.3 Food & Coffee | full price | 2868 | 67.0 | 2.1 | 1.8 | 3.9 Home & Living | discounted | 284 | 146.0 | 2.7 | 18.0 | 3.2 Home & Living | full price | 1379 | 150.0 | 2.3 | 12.8 | 4.1 Industrial Supplies (B2B) | discounted | 632 | 2223.0 | 44.3 | 9.7 | 1.6 Industrial Supplies (B2B) | full price | 142 | 419.0 | 8.1 | 2.8 | 1.4 Jewelry & Accessories | discounted | 15 | 212.0 | 2.5 | 13.3 | 0.0 Jewelry & Accessories | full price | 35 | 220.0 | 2.6 | 14.3 | 5.7 Office Supplies (B2B) | discounted | 381 | 3834.0 | 32.3 | 4.5 | 1.0 Office Supplies (B2B) | full price | 146 | 922.0 | 7.3 | 2.7 | 4.1 Pet Supplies | discounted | 679 | 8.0 | 1.5 | 0.1 | 0.9 Pet Supplies | full price | 774 | 62.0 | 2.2 | 2.5 | 4.9 Sports & Outdoor | discounted | 212 | 359.0 | 2.6 | 25.9 | 2.8 Sports & Outdoor | full price | 805 | 368.0 | 2.3 | 19.6 | 4.6 (22 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
WITH order_items_agg AS ( SELECT order_id, SUM(quantity) AS item_qty FROM order_items GROUP BY order_id),order_returns_flag AS ( SELECT DISTINCT order_id FROM order_returns),base AS ( SELECT o.id AS order_id, t.industry AS industry, CASE WHEN o.discount_total > 0 THEN 'discounted' ELSE 'full price' END AS order_group, o.status AS status, o.grand_total AS grand_total, o.currency_code AS currency_code, o.placed_at AS placed_at, COALESCE(oi.item_qty, 0) AS item_qty, COALESCE(er.rate, CASE WHEN o.currency_code = 'EUR' THEN 1 END) AS eur_rate FROM orders o JOIN tenants t ON t.id = o.tenant_id LEFT JOIN order_items_agg oi ON oi.order_id = o.id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = CAST(o.placed_at AS date) WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01')SELECT b.industry AS industry, b.order_group AS order_group, COUNT(*) AS orders_count, ROUND(AVG(b.grand_total / b.eur_rate), 0) AS avg_order_value_eur, ROUND(AVG(b.item_qty), 1) AS avg_items_per_order, ROUND(100.0 * SUM(CASE WHEN rf.order_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*), 1) AS return_rate_pct, ROUND(100.0 * SUM(CASE WHEN b.status = 'cancelled' THEN 1 ELSE 0 END) / COUNT(*), 1) AS cancelled_rate_pct AVG(CAST(b.grand_total / b.eur_rate AS NUMERIC(18,6))) AS avg_order_value_eur, AVG(CAST(b.item_qty AS NUMERIC(18,6))) AS avg_items_per_order, 100.0 * SUM(CASE WHEN rf.order_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*) AS return_rate_pct, 100.0 * SUM(CASE WHEN b.status = 'cancelled' THEN 1 ELSE 0 END) / COUNT(*) AS cancelled_rate_pctFROM base bLEFT JOIN order_returns_flag rf ON rf.order_id = b.order_idGROUP BY b.industry, b.order_groupORDER BY b.industry ASC, b.order_group ASCORDER BY b.industry ASC NULLS LAST, b.order_group ASC NULLS LAST
WITH order_items_agg AS ( SELECT order_id, SUM(quantity) AS item_qty FROM order_items GROUP BY order_id ), order_returns_flag AS ( SELECT DISTINCT order_id FROM order_returns ), base AS ( SELECT o.id AS order_id, t.industry AS industry, CASE WHEN o.discount_total > 0 THEN 'discounted' ELSE 'full price' END AS order_group, o.status AS status, o.grand_total AS grand_total, o.currency_code AS currency_code, o.placed_at AS placed_at, COALESCE(oi.item_qty, 0) AS item_qty, COALESCE(er.rate, CASE WHEN o.currency_code = 'EUR' THEN 1 END) AS eur_rate FROM orders o JOIN tenants t ON t.id = o.tenant_id LEFT JOIN order_items_agg oi ON oi.order_id = o.id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = CAST(o.placed_at AS date) WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' ) SELECT b.industry AS industry, b.order_group AS order_group, COUNT(*) AS orders_count, AVG(CAST(b.grand_total / b.eur_rate AS NUMERIC(18,6))) AS avg_order_value_eur, AVG(CAST(b.item_qty AS NUMERIC(18,6))) AS avg_items_per_order, 100.0 * SUM(CASE WHEN rf.order_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*) AS return_rate_pct, 100.0 * SUM(CASE WHEN b.status = 'cancelled' THEN 1 ELSE 0 END) / COUNT(*) AS cancelled_rate_pct FROM base b LEFT JOIN order_returns_flag rf ON rf.order_id = b.order_id GROUP BY b.industry, b.order_group ORDER BY b.industry ASC NULLS LAST, b.order_group 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 Sofie Nilsen (analyst for the Head of Growth Marketing):
Title: Are discounted orders healthier across verticals, or just in B2B?
Question: Does the 'discounted orders have higher AOV, bigger baskets, and fewer returns' pattern hold in consumer verticals, or is it mainly an artifact of B2B wholesale contract pricing?
Grain: one row per industry vertical and order group (discounted vs full price)
Columns:
- industry - tenants.industry
- order_group - 'discounted' (orders.discount_total > 0) or 'full price' (= 0)
- orders_count - count of orders placed_at in 2025 for that industry/group
- avg_order_value_eur - average orders.grand_total converted to EUR, rounded to 0 decimals
- avg_items_per_order - average of sum(order_items.quantity) per order, rounded to 1 decimal
- return_rate_pct - share of orders in that cell with at least one linked order_returns row, *100, rounded to 1 decimal
- cancelled_rate_pct - share of orders with status='cancelled', *100, rounded to 1 decimal
Details: Source: orders joined to tenants (for industry) and order_items (summed quantity per order); filter placed_at in 2025-01-01..2026-01-01 exclusive, no status filter otherwise. Same discounted/full-price split and same EUR conversion approach as last night's 'Do discounted orders look healthy' report, but now crossed with tenants.industry. Order by industry ascending, then order_group ascending ('discounted' before 'full price'). Expect roughly 8-9 industries x 2 rows (~16-18 rows total), well under the limit.
Check your query with the warehouse's dry_run tool, then hand it in with submit.dry_runWITH order_items_agg AS ( SELECT order_id, SUM(quantity) AS item_qty FROM order_items GROUP BY order_id ), order_returns_flag AS ( SELECT DISTINCT order_id FROM order_returns ), base AS ( SELECT o.id AS order_id, t.industry AS industry, CASE WHEN o.discount_total > 0 THEN 'discounted' ELSE 'full price' END AS order_group, o.status AS status, o.grand_total AS grand_total, o.currency_code AS currency_code, o.placed_at AS placed_at, COALESCE(oi.item_qty, 0) AS item_qty, COALESCE(er.rate, CASE WHEN o.currency_code = 'EUR' THEN 1 END) AS eur_rate FROM orders o JOIN tenants t ON t.id = o.tenant_id LEFT JOIN order_items_agg oi ON oi.order_id = o.id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = CAST(o.placed_at AS date) WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' ) SELECT b.industry AS industry, b.order_group AS order_group, COUNT(*) AS orders_count, ROUND(AVG(b.grand_total / b.eur_rate), 0) AS avg_order_value_eur, ROUND(AVG(b.item_qty), 1) AS avg_items_per_order, ROUND(100.0 * SUM(CASE WHEN rf.order_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*), 1) AS return_rate_pct, ROUND(100.0 * SUM(CASE WHEN b.status = 'cancelled' THEN 1 ELSE 0 END) / COUNT(*), 1) AS cancelled_rate_pct FROM base b LEFT JOIN order_returns_flag rf ON rf.order_id = b.order_id GROUP BY b.industry, b.order_group ORDER BY b.industry ASC, b.order_group ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: industry, order_group, orders_count, avg_order_value_eur, avg_items_per_order, return_rate_pct, cancelled_rate_pct
submitWITH order_items_agg AS ( SELECT order_id, SUM(quantity) AS item_qty FROM order_items GROUP BY order_id ), order_returns_flag AS ( SELECT DISTINCT order_id FROM order_returns ), base AS ( SELECT o.id AS order_id, t.industry AS industry, CASE WHEN o.discount_total > 0 THEN 'discounted' ELSE 'full price' END AS order_group, o.status AS status, o.grand_total AS grand_total, o.currency_code AS currency_code, o.placed_at AS placed_at, COALESCE(oi.item_qty, 0) AS item_qty, COALESCE(er.rate, CASE WHEN o.currency_code = 'EUR' THEN 1 END) AS eur_rate FROM orders o JOIN tenants t ON t.id = o.tenant_id LEFT JOIN order_items_agg oi ON oi.order_id = o.id LEFT JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = CAST(o.placed_at AS date) WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01' ) SELECT b.industry AS industry, b.order_group AS order_group, COUNT(*) AS orders_count, ROUND(AVG(b.grand_total / b.eur_rate), 0) AS avg_order_value_eur, ROUND(AVG(b.item_qty), 1) AS avg_items_per_order, ROUND(100.0 * SUM(CASE WHEN rf.order_id IS NOT NULL THEN 1 ELSE 0 END) / COUNT(*), 1) AS return_rate_pct, ROUND(100.0 * SUM(CASE WHEN b.status = 'cancelled' THEN 1 ELSE 0 END) / COUNT(*), 1) AS cancelled_rate_pct FROM base b LEFT JOIN order_returns_flag rf ON rf.order_id = b.order_id GROUP BY b.industry, b.order_group ORDER BY b.industry ASC, b.order_group ASC
Submitted to tonight's batch. Your work on this request is done.