Queries · q289 · VP Product · week 2, night 1
“Is the near-zero web-session count for subscription merchants a tracking blind spot, or does it reflect real behavior — customers browsing once to sign up and then never browsing again?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
Week 1
We are redesigning the shopper journey for 2026. We need to understand the whole path, from the first visit to checkout, delivery, returns and the next purchase, and where shoppers drop out and why.
The manager, at the start of week 2
Subject: Good start — let's dig into the cart leak
Emeka, this is exactly the kind of clarity I needed. The cart-stage bleed is the headline, and I agree we should lead with it — but a two-thirds drop-off that lingers for days, converting at under 20%, isn't something we can act on until we know why people stall. "Add cart recovery emails" is a guess until we understand the shape of the hesitation. That's my main ask for this week.
I also want to flag one thing before we build a roadmap slide on repeat purchase and returns: the return-vs-repeat gap and the delivery/returns pattern are both flagged as not-yet-causal, confounded by vertical. I don't want to present an 8-point "returns hurt loyalty" number to the exec team if it's really just "apparel behaves differently." Let's firm that up before it goes in a deck, but it's not urgent this week.
Questions for this week:
Keep the returns/delivery vertical-control work and the causal retention question on the list for whenever a slot frees up, but the cart story is what I want to walk into the roadmap review with real conviction on.
Thanks, Emeka — strong week.
Jonas
The analyst · week 2, night 1, request 3 of 3
Jonas's email has an implicit priority: Q1 (what's in a stalled cart) and Q2 (is the bleed trending worse) are "my main ask for this week"; Q3 (organic recovery) is also wanted but reads as a natural follow-up once we understand the shape; Q4 is explicitly "a quick gut-check... no need for deep investigation." So tonight I'm doing Q1, Q2, and a lightweight version of Q4, saving Q3 for night 2 since it benefits from what we learn tonight (e.g., whether stalled carts later convert with a discount attached is itself a clue about organic recovery).
Design notes:
All three use the 6 retail tenants (Q1, Q2) or 4 subscription tenants (Q4) established in last week's work, so the engineer has a concrete, named population to filter on rather than re-deriving "top merchants."
The engineer · 1 dry run on the empty schema, then the submission
SELECT t.name AS tenant_name, COUNT(*) AS customers, ROUND(100.0 * SUM(CASE WHEN cust.before_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_with_session_before, ROUND(100.0 * SUM(CASE WHEN cust.after_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_with_session_after, ROUND(AVG(cust.after_cnt), 2) AS avg_sessions_after_first_order_per_customer FROM ( SELECT fo.tenant_id, fo.user_id, fo.first_order_at, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at <= fo.first_order_at) AS before_cnt, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at > fo.first_order_at) AS after_cnt FROM ( SELECT tenant_id, user_id, MIN(placed_at) AS first_order_at FROM orders WHERE tenant_id IN (SELECT id FROM tenants WHERE name IN ('Brennquell Kaffee','Spice Route Box','Grind Collective','Honeycomb Larder')) GROUP BY tenant_id, user_id ) fo ) cust JOIN tenants t ON t.id = cust.tenant_id GROUP BY t.name ORDER BY t.name
“Compiles cleanly and matches the requested grain and columns.”
“Submitted. One note for the analyst: since web_sessions only link to a customer once user_id is set, any anonymous pre-signup browsing (common for a "browse once to sign up" pattern) won't show up in pct_with_session_before — that's a real limitation of the session-to-user linkage, not a query choice, and worth flagging if the before-percentage comes back low.”
As Emeka Obi saw it, from the small dataset the agents worked on
tenant_name | customers | pct_with_session_before | pct_with_session_after | avg_sessions_after_first_order_per_custo ------------------+-----------+-------------------------+------------------------+----------------------------------------- Brennquell Kaffee | 659 | 20.2 | 50.7 | 3.13 Grind Collective | 145 | 15.9 | 49.0 | 2.86 Honeycomb Larder | 115 | 30.4 | 69.6 | 4.13 Spice Route Box | 158 | 22.8 | 42.4 | 2.44 (4 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 t.name AS tenant_name, COUNT(*) AS customers, ROUND(100.0 * SUM(CASE WHEN cust.before_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_with_session_before, ROUND(100.0 * SUM(CASE WHEN cust.after_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_with_session_after, ROUND(AVG(cust.after_cnt), 2) AS avg_sessions_after_first_order_per_customer 100.0 * SUM(CASE WHEN cust.before_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*) AS pct_with_session_before, 100.0 * SUM(CASE WHEN cust.after_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*) AS pct_with_session_after, AVG(CAST(cust.after_cnt AS NUMERIC(18,6))) AS avg_sessions_after_first_order_per_customerFROM ( SELECT fo.tenant_id, fo.user_id, fo.first_order_at, SELECT fo.tenant_id AS tenant_id, fo.user_id AS user_id, fo.first_order_at AS first_order_at, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at <= fo.first_order_at) AS before_cnt, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at > fo.first_order_at) AS after_cnt FROM ( SELECT tenant_id, user_id, MIN(placed_at) AS first_order_at FROM orders WHERE tenant_id IN (SELECT id FROM tenants WHERE name IN ('Brennquell Kaffee','Spice Route Box','Grind Collective','Honeycomb Larder')) GROUP BY tenant_id, user_id ) fo) custJOIN tenants t ON t.id = cust.tenant_idGROUP BY t.nameORDER BY t.nameORDER BY t.name NULLS LAST
SELECT t.name AS tenant_name, COUNT(*) AS customers, 100.0 * SUM(CASE WHEN cust.before_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*) AS pct_with_session_before, 100.0 * SUM(CASE WHEN cust.after_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*) AS pct_with_session_after, AVG(CAST(cust.after_cnt AS NUMERIC(18,6))) AS avg_sessions_after_first_order_per_customer FROM ( SELECT fo.tenant_id AS tenant_id, fo.user_id AS user_id, fo.first_order_at AS first_order_at, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at <= fo.first_order_at) AS before_cnt, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at > fo.first_order_at) AS after_cnt FROM ( SELECT tenant_id, user_id, MIN(placed_at) AS first_order_at FROM orders WHERE tenant_id IN (SELECT id FROM tenants WHERE name IN ('Brennquell Kaffee','Spice Route Box','Grind Collective','Honeycomb Larder')) GROUP BY tenant_id, user_id ) fo ) cust JOIN tenants t ON t.id = cust.tenant_id GROUP BY t.name ORDER BY t.name 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 Emeka Obi (analyst for the VP Product): Title: Subscription merchants: do customers browse before vs. after their first order? Question: Is the near-zero web-session count for subscription merchants a tracking blind spot, or does it reflect real behavior — customers browsing once to sign up and then never browsing again? Grain: one row per subscription-merchant tenant Columns: - tenant name - customers (distinct users with at least one order) - pct of customers with any session before their first order - pct of customers with any session after their first order - avg sessions after first order, per customer Details: Tenants: the 4 Food & Coffee subscription merchants (Brennquell Kaffee, Spice Route Box, Grind Collective, Honeycomb Larder). For each tenant, customers = distinct users (tenant_id, user_id) with at least one order (any status) in orders. For each customer, first_order_at = min(orders.placed_at). pct_with_session_before = % of customers with at least one web_sessions row where started_at <= first_order_at. pct_with_session_after = % of customers with at least one web_sessions row where started_at > first_order_at (any time after, through 2025-12-31). avg_sessions_after_first_order_per_customer = average count of web_sessions rows with started_at > first_order_at, across all customers (including those with zero). Round percentages to 1 decimal and the average to 2 decimals. Order rows by tenant name. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runSELECT t.name AS tenant_name, COUNT(*) AS customers, ROUND(100.0 * SUM(CASE WHEN cust.before_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_with_session_before, ROUND(100.0 * SUM(CASE WHEN cust.after_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_with_session_after, ROUND(AVG(cust.after_cnt), 2) AS avg_sessions_after_first_order_per_customer FROM ( SELECT fo.tenant_id, fo.user_id, fo.first_order_at, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at <= fo.first_order_at) AS before_cnt, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at > fo.first_order_at) AS after_cnt FROM ( SELECT tenant_id, user_id, MIN(placed_at) AS first_order_at FROM orders WHERE tenant_id IN (SELECT id FROM tenants WHERE name IN ('Brennquell Kaffee','Spice Route Box','Grind Collective','Honeycomb Larder')) GROUP BY tenant_id, user_id ) fo ) cust JOIN tenants t ON t.id = cust.tenant_id GROUP BY t.name ORDER BY t.name
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: tenant_name, customers, pct_with_session_before, pct_with_session_after, avg_sessions_after_first_order_per_customer
submitSELECT t.name AS tenant_name, COUNT(*) AS customers, ROUND(100.0 * SUM(CASE WHEN cust.before_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_with_session_before, ROUND(100.0 * SUM(CASE WHEN cust.after_cnt > 0 THEN 1 ELSE 0 END) / COUNT(*), 1) AS pct_with_session_after, ROUND(AVG(cust.after_cnt), 2) AS avg_sessions_after_first_order_per_customer FROM ( SELECT fo.tenant_id, fo.user_id, fo.first_order_at, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at <= fo.first_order_at) AS before_cnt, (SELECT COUNT(*) FROM web_sessions ws WHERE ws.tenant_id = fo.tenant_id AND ws.user_id = fo.user_id AND ws.started_at > fo.first_order_at) AS after_cnt FROM ( SELECT tenant_id, user_id, MIN(placed_at) AS first_order_at FROM orders WHERE tenant_id IN (SELECT id FROM tenants WHERE name IN ('Brennquell Kaffee','Spice Route Box','Grind Collective','Honeycomb Larder')) GROUP BY tenant_id, user_id ) fo ) cust JOIN tenants t ON t.id = cust.tenant_id GROUP BY t.name ORDER BY t.name
Submitted to tonight's batch. Your work on this request is done.