Queries · q056 · Head of Growth Marketing · week 1, night 3
“How much campaign messaging goes out, and is anyone opening or clicking — is this a strength or a weak spot across the platform?”
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 2 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 messages AS ( SELECT cm.sent_at, cm.delivered_at, cm.opened_at, cm.clicked_at, cm.bounced_at, cm.unsubscribed_at, c.channel FROM campaign_messages cm JOIN campaigns c ON c.id = cm.campaign_id WHERE cm.created_at >= DATE '2025-01-01' AND cm.created_at < DATE '2026-01-01' ), agg AS ( SELECT channel, COUNT(*) FILTER (WHERE sent_at IS NOT NULL) AS messages_sent, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL) AS delivered_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND opened_at IS NOT NULL) AS opened_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND clicked_at IS NOT NULL) AS clicked_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND bounced_at IS NOT NULL) AS bounced_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND unsubscribed_at IS NOT NULL) AS unsubscribed_count FROM messages GROUP BY channel ) SELECT channel, messages_sent, ROUND(100.0 * delivered_count / NULLIF(messages_sent, 0), 1) AS delivered_rate_pct, ROUND(100.0 * opened_count / NULLIF(delivered_count, 0), 1) AS open_rate_pct, ROUND(100.0 * clicked_count / NULLIF(delivered_count, 0), 1) AS click_rate_pct, ROUND(100.0 * bounced_count / NULLIF(messages_sent, 0), 1) AS bounce_rate_pct, ROUND(100.0 * unsubscribed_count / NULLIF(messages_sent, 0), 1) AS unsubscribe_rate_pct FROM agg ORDER BY messages_sent DESC, channel ASC
“Query compiles and matches the requested grain and columns. Submitting.”
“Submitted. The report gives one row per campaign channel with messages sent in 2025 and the delivered/open/click/bounce/unsubscribe rates as specified, ordered by volume so the Head of Growth Marketing can quickly see which channels carry the most engagement risk or strength.”
As Sofie Nilsen saw it, from the small dataset the agents worked on
channel | messages_sent | delivered_rate_pct | open_rate_pct | click_rate_pct | bounce_rate_pct | unsubscribe_rate_pct --------+---------------+--------------------+---------------+----------------+-----------------+--------------------- email | 106141 | 99.5 | 29.9 | 2.4 | 0.3 | 0.2 (1 rows)
Changed only where the benchmark needs the same result on every system
WITH messages AS ( SELECT cm.sent_at, cm.delivered_at, cm.opened_at, cm.clicked_at, cm.bounced_at, cm.unsubscribed_at, c.channel cm.sent_at AS sent_at, cm.delivered_at AS delivered_at, cm.opened_at AS opened_at, cm.clicked_at AS clicked_at, cm.bounced_at AS bounced_at, cm.unsubscribed_at AS unsubscribed_at, c.channel AS channel FROM campaign_messages cm JOIN campaigns c ON c.id = cm.campaign_id WHERE cm.created_at >= DATE '2025-01-01' AND cm.created_at < DATE '2026-01-01'),agg AS ( SELECT channel, COUNT(*) FILTER (WHERE sent_at IS NOT NULL) AS messages_sent, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL) AS delivered_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND opened_at IS NOT NULL) AS opened_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND clicked_at IS NOT NULL) AS clicked_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND bounced_at IS NOT NULL) AS bounced_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND unsubscribed_at IS NOT NULL) AS unsubscribed_count FROM messages GROUP BY channel)SELECT channel, messages_sent, ROUND(100.0 * delivered_count / NULLIF(messages_sent, 0), 1) AS delivered_rate_pct, ROUND(100.0 * opened_count / NULLIF(delivered_count, 0), 1) AS open_rate_pct, ROUND(100.0 * clicked_count / NULLIF(delivered_count, 0), 1) AS click_rate_pct, ROUND(100.0 * bounced_count / NULLIF(messages_sent, 0), 1) AS bounce_rate_pct, ROUND(100.0 * unsubscribed_count / NULLIF(messages_sent, 0), 1) AS unsubscribe_rate_pct 100.0 * delivered_count / NULLIF(messages_sent, 0) AS delivered_rate_pct, 100.0 * opened_count / NULLIF(delivered_count, 0) AS open_rate_pct, 100.0 * clicked_count / NULLIF(delivered_count, 0) AS click_rate_pct, 100.0 * bounced_count / NULLIF(messages_sent, 0) AS bounce_rate_pct, 100.0 * unsubscribed_count / NULLIF(messages_sent, 0) AS unsubscribe_rate_pctFROM aggORDER BY messages_sent DESC, channel ASCORDER BY messages_sent DESC NULLS FIRST, channel ASC NULLS LAST
WITH messages AS ( SELECT cm.sent_at AS sent_at, cm.delivered_at AS delivered_at, cm.opened_at AS opened_at, cm.clicked_at AS clicked_at, cm.bounced_at AS bounced_at, cm.unsubscribed_at AS unsubscribed_at, c.channel AS channel FROM campaign_messages cm JOIN campaigns c ON c.id = cm.campaign_id WHERE cm.created_at >= DATE '2025-01-01' AND cm.created_at < DATE '2026-01-01' ), agg AS ( SELECT channel, COUNT(*) FILTER (WHERE sent_at IS NOT NULL) AS messages_sent, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL) AS delivered_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND opened_at IS NOT NULL) AS opened_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND clicked_at IS NOT NULL) AS clicked_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND bounced_at IS NOT NULL) AS bounced_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND unsubscribed_at IS NOT NULL) AS unsubscribed_count FROM messages GROUP BY channel ) SELECT channel, messages_sent, 100.0 * delivered_count / NULLIF(messages_sent, 0) AS delivered_rate_pct, 100.0 * opened_count / NULLIF(delivered_count, 0) AS open_rate_pct, 100.0 * clicked_count / NULLIF(delivered_count, 0) AS click_rate_pct, 100.0 * bounced_count / NULLIF(messages_sent, 0) AS bounce_rate_pct, 100.0 * unsubscribed_count / NULLIF(messages_sent, 0) AS unsubscribe_rate_pct FROM agg ORDER BY messages_sent DESC NULLS FIRST, channel 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: Email/campaign engagement baseline by channel Question: How much campaign messaging goes out, and is anyone opening or clicking — is this a strength or a weak spot across the platform? Grain: one row per campaign channel (campaigns.channel) Columns: - channel - campaigns.channel - messages_sent - count of campaign_messages with sent_at not null, created_at in 2025 - delivered_rate_pct - share of sent messages with delivered_at not null, *100, rounded to 1 decimal - open_rate_pct - share of delivered messages with opened_at not null, *100, rounded to 1 decimal - click_rate_pct - share of delivered messages with clicked_at not null, *100, rounded to 1 decimal - bounce_rate_pct - share of sent messages with bounced_at not null, *100, rounded to 1 decimal - unsubscribe_rate_pct - share of sent messages with unsubscribed_at not null, *100, rounded to 1 decimal Details: Source: campaign_messages joined to campaigns on campaign_id for channel. Filter campaign_messages.created_at between 2025-01-01 and 2026-01-01 (platform-wide, no tenant filter). messages_sent = sent_at is not null; all rate denominators as specified (delivered/open/click relative to delivered count where noted, bounce/unsubscribe relative to sent count). Order by messages_sent descending, tie-break channel ascending. Expect a small number of distinct channel values, well under the row limit. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH messages AS ( SELECT cm.sent_at, cm.delivered_at, cm.opened_at, cm.clicked_at, cm.bounced_at, cm.unsubscribed_at, c.channel FROM campaign_messages cm JOIN campaigns c ON c.id = cm.campaign_id WHERE cm.created_at >= DATE '2025-01-01' AND cm.created_at < DATE '2026-01-01' ), agg AS ( SELECT channel, COUNT(*) FILTER (WHERE sent_at IS NOT NULL) AS messages_sent, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL) AS delivered_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND opened_at IS NOT NULL) AS opened_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND clicked_at IS NOT NULL) AS clicked_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND bounced_at IS NOT NULL) AS bounced_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND unsubscribed_at IS NOT NULL) AS unsubscribed_count FROM messages GROUP BY channel ) SELECT channel, messages_sent, ROUND(100.0 * delivered_count / NULLIF(messages_sent, 0), 1) AS delivered_rate_pct, ROUND(100.0 * opened_count / NULLIF(delivered_count, 0), 1) AS open_rate_pct, ROUND(100.0 * clicked_count / NULLIF(delivered_count, 0), 1) AS click_rate_pct, ROUND(100.0 * bounced_count / NULLIF(messages_sent, 0), 1) AS bounce_rate_pct, ROUND(100.0 * unsubscribed_count / NULLIF(messages_sent, 0), 1) AS unsubscribe_rate_pct FROM agg ORDER BY messages_sent DESC, channel ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: channel, messages_sent, delivered_rate_pct, open_rate_pct, click_rate_pct, bounce_rate_pct, unsubscribe_rate_pct
submitWITH messages AS ( SELECT cm.sent_at, cm.delivered_at, cm.opened_at, cm.clicked_at, cm.bounced_at, cm.unsubscribed_at, c.channel FROM campaign_messages cm JOIN campaigns c ON c.id = cm.campaign_id WHERE cm.created_at >= DATE '2025-01-01' AND cm.created_at < DATE '2026-01-01' ), agg AS ( SELECT channel, COUNT(*) FILTER (WHERE sent_at IS NOT NULL) AS messages_sent, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL) AS delivered_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND opened_at IS NOT NULL) AS opened_count, COUNT(*) FILTER (WHERE delivered_at IS NOT NULL AND clicked_at IS NOT NULL) AS clicked_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND bounced_at IS NOT NULL) AS bounced_count, COUNT(*) FILTER (WHERE sent_at IS NOT NULL AND unsubscribed_at IS NOT NULL) AS unsubscribed_count FROM messages GROUP BY channel ) SELECT channel, messages_sent, ROUND(100.0 * delivered_count / NULLIF(messages_sent, 0), 1) AS delivered_rate_pct, ROUND(100.0 * opened_count / NULLIF(delivered_count, 0), 1) AS open_rate_pct, ROUND(100.0 * clicked_count / NULLIF(delivered_count, 0), 1) AS click_rate_pct, ROUND(100.0 * bounced_count / NULLIF(messages_sent, 0), 1) AS bounce_rate_pct, ROUND(100.0 * unsubscribed_count / NULLIF(messages_sent, 0), 1) AS unsubscribe_rate_pct FROM agg ORDER BY messages_sent DESC, channel ASC
Submitted to tonight's batch. Your work on this request is done.