Queries · q076 · VP Product · week 1, night 1

Storefront-to-order funnel for top merchants, Q4 2025

“Where do shoppers drop off between landing on the storefront and placing a paid order, and does the shape differ across our biggest merchants?”

Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told

1. A business event lands on the VP Product's desk

   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.

   One of the 35 events we wrote for the role play; it shaped the manager's first note. How events arise · All the events

2. Jonas Feld writes to Emeka Obi

   The manager, at the start of week 1

   Subject: Shopper journey end-to-end — what I need for the 2026 roadmap

   Emeka,

   We're resetting the shopper journey for 2026, and I need a clear picture of the whole path — first visit through checkout, delivery, returns, and whether people come back — so we can decide where to invest design and engineering time first. This feeds the roadmap review I'm presenting in two weeks, so the Friday memo should leave me able to say "here's where shoppers are leaking out, and here's the one or two places a fix would move the most volume."

   I know you only get a handful of report slots this week, so please prioritize in the order below — stop earlier in the list if something forces a tradeoff, rather than spreading thin across all of it.

   1. Where do we lose people between landing on the storefront and placing an order? I want to see the shape of the funnel (visit → browse/search → cart → checkout → paid order) and which step is the biggest drop-off, ideally for a few of our bigger merchants so we're not averaging away real differences.
   2. Once someone reaches checkout, what's stopping them from finishing? Is it a one-step cliff or a slow bleed across the checkout flow, and does it differ by device or by how they arrived (e.g. ad vs. direct vs. returning)?
   3. After the order is placed, how is delivery experience affecting trust? I want to understand how often deliveries run late and whether that visibly connects to returns or to people not coming back.
   4. How painful are returns, and does a return kill the relationship? I'd like a sense of how often a return leads to the shopper buying again within a reasonable window versus going quiet.
   5. Who comes back, and how soon? A simple read on repeat purchase behavior — how many first-time buyers return, and roughly how long it takes — would help me know if we're building a roadmap for acquisition or for retention.

   If you can only land three of these this week, do 1, 2 and 5 — funnel, checkout, and repeat purchase are the ones I most need going into the roadmap conversation. Delivery and returns can follow next week if we run out of nights.

   Thanks — looking forward to Friday.

   Jonas

   Show the whole note

3. Emeka Obi plans the night

   The analyst · week 1, night 1, request 1 of 3

   Jonas asked for 5 things but explicitly said: if time-constrained, prioritize #1 (funnel), #2 (checkout drop-off), #5 (repeat purchase) — delivery/returns can wait. I'm spending all 3 slots on those tonight.

   Schema reality check before writing specs:

   • There's no FK linking carts/web_sessions directly (carts.session_token isn't a declared FK to web_sessions.anonymous_id), so "visit" and "cart" stages have to be joined approximately — I've asked the engineer to do a best-effort match by user_id and nearest time, and to flag if that join is too noisy rather than silently forcing it.
   • orders.cart_id IS a real FK, so cart→order→paid is solid.
   • For repeat purchase, I'm using orders.status progression as the "reached paid" signal (no need for payments table complexity at this grain) and defining first-time buyer / repeat purchase purely off orders.placed_at per user, which is clean and well-supported by the schema.
   • For device/channel on checkout, I'm leaning on web_sessions.user_agent (parsed for device class) and utm_source (channel), acknowledging this is an approximation since there's no hard session→cart link.

   I'm deliberately keeping report 1 to the top ~10 merchants by volume (not all 120) per Jonas's ask to see real differences rather than an averaged-away number, and keeping report 5 to cohorts with a full 90-day observation window given data ends 2025-12-31.

   Read the whole plan
   Storefront-to-order funnel for top merchants, Q4 2025

   Question

   Where do shoppers drop off between landing on the storefront and placing a paid order, and does the shape differ across our biggest merchants?

   One row per

   tenant (top 10 merchants by paid order volume in Q4 2025)

   Columns

   • tenant name
   • visits (distinct web_sessions started in period)
   • carts created in period
   • orders placed in period (any status)
   • orders paid in period (status not in pending, cancelled)
   • visit → cart %
   • cart → order %
   • order → paid %
   • overall visit → paid %

4. Grace writes the SQL

   The engineer · 1 dry run on the empty schema, then the submission

   Dry run 1 · STATUS: COMPILES (dry run on the empty schema, no data read)

   WITH paid_orders AS (
     SELECT tenant_id, COUNT(*) AS paid_order_count
     FROM orders
     WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'
       AND status NOT IN ('pending', 'cancelled')
     GROUP BY tenant_id
   ),
   top_tenants AS (
     SELECT tenant_id, paid_order_count
     FROM paid_orders
     ORDER BY paid_order_count DESC, tenant_id ASC
     LIMIT 10
   ),
   visits AS (
     SELECT tenant_id, COUNT(*) AS visit_count
     FROM web_sessions
     WHERE started_at >= DATE '2025-10-01' AND started_at < DATE '2026-01-01'
     GROUP BY tenant_id
   ),
   carts_created AS (
     SELECT tenant_id, COUNT(*) AS cart_count
     FROM carts
     WHERE created_at >= DATE '2025-10-01' AND created_at < DATE '2026-01-01'
     GROUP BY tenant_id
   ),
   orders_placed AS (
     SELECT tenant_id, COUNT(*) AS order_count
     FROM orders
     WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'
     GROUP BY tenant_id
   ),
   assembled AS (
     SELECT
       t.id AS tenant_id,
       t.name AS tenant_name,
       COALESCE(v.visit_count, 0) AS visits,
       COALESCE(c.cart_count, 0) AS carts_created,
       COALESCE(o.order_count, 0) AS orders_placed,
       tt.paid_order_count AS orders_paid
     FROM top_tenants tt
     JOIN tenants t ON t.id = tt.tenant_id
     LEFT JOIN visits v ON v.tenant_id = tt.tenant_id
     LEFT JOIN carts_created c ON c.tenant_id = tt.tenant_id
     LEFT JOIN orders_placed o ON o.tenant_id = tt.tenant_id
   )
   SELECT
     tenant_name,
     visits,
     carts_created,
     orders_placed,
     orders_paid,
     ROUND(CASE WHEN visits > 0 THEN 100.0 * carts_created / visits ELSE NULL END, 1) AS visit_to_cart_pct,
     ROUND(CASE WHEN carts_created > 0 THEN 100.0 * orders_placed / carts_created ELSE NULL END, 1) AS cart_to_order_pct,
     ROUND(CASE WHEN orders_placed > 0 THEN 100.0 * orders_paid / orders_placed ELSE NULL END, 1) AS order_to_paid_pct,
     ROUND(CASE WHEN visits > 0 THEN 100.0 * orders_paid / visits ELSE NULL END, 1) AS overall_visit_to_paid_pct
   FROM assembled
   ORDER BY overall_visit_to_paid_pct DESC NULLS LAST, tenant_name ASC

   “Query compiles cleanly with the right columns. Submitting.”

   Submitted · the SQL of the last dry run, unchanged · Submitted to tonight's batch. Your work on this request is done.

   “Submitted. The report returns one row per top-10 tenant (by Q4 2025 paid-order count) with visits, carts, orders placed, orders paid, and the four conversion percentages, ordered by overall visit→paid % descending.”

5. The report comes back overnight

   As Emeka Obi saw it, from the small dataset the agents worked on

   tenant_name         | visits | carts_created | orders_placed | orders_paid | visit_to_cart_pct | cart_to_order_pct | order_to_paid_pct | overall_visit_to_paid_pct
   --------------------+--------+---------------+---------------+-------------+-------------------+-------------------+-------------------+--------------------------
   Brennquell Kaffee   |    672 |          1287 |          1172 |        1147 |             191.5 |              91.1 |              97.9 |                     170.7
   Spice Route Box     |    138 |           239 |           201 |         198 |             173.2 |              84.1 |              98.5 |                     143.5
   Grind Collective    |    245 |           365 |           351 |         346 |             149.0 |              96.2 |              98.6 |                     141.2
   Honeycomb Larder    |    148 |           192 |           188 |         186 |             129.7 |              97.9 |              98.9 |                     125.7
   Kibblewick          |    222 |           330 |           200 |         196 |             148.6 |              60.6 |              98.0 |                      88.3
   Studio Vrij         |    432 |           651 |           241 |         227 |             150.7 |              37.0 |              94.2 |                      52.5
   Petalcourt Beauty   |    485 |           669 |           256 |         243 |             137.9 |              38.3 |              94.9 |                      50.1
   Marrowby & Vane     |   1316 |          1818 |           662 |         632 |             138.1 |              36.4 |              95.5 |                      48.0
   Cragsley Outfitters |   2978 |          3922 |          1443 |        1385 |             131.7 |              36.8 |              96.0 |                      46.5
   Ampfeld Elektronik  |    691 |           720 |           195 |         187 |             104.2 |              27.1 |              95.9 |                      27.1
   (10 rows)

6. What came of it

   The memo and the queries that followed; links found by title, so inferred

   Later queries that build on this report:

   • q079Session-tracking coverage vs order volume for top merchants, 2025
   • q080Where orders stall after checkout: status-transition funnel, Q4 2025
   • q081Cart abandonment: cliff or slow bleed, Q4 2025 (non-subscription merchants)

7. It becomes benchmark query q076

   Changed only where the benchmark needs the same result on every system

   • spellingfetch first
   • determinismtie-breaker: tenant names are not unique; assembled has one row per tenant_id
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • determinismthe standard leaves the place of NULLs in a sort to the implementation: NULLS LAST ascending, NULLS FIRST descending, as in the reference results (4×)
   • placeholderfetch first
   The benchmark text, against the submitted SQL

   WITH paid_orders AS (  SELECT tenant_id, COUNT(*) AS paid_order_count  FROM orders  WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'    AND status NOT IN ('pending', 'cancelled')  GROUP BY tenant_id),top_tenants AS (  SELECT tenant_id, paid_order_count  FROM paid_orders  ORDER BY paid_order_count DESC, tenant_id ASC  LIMIT 10  ORDER BY paid_order_count DESC NULLS FIRST, tenant_id ASC NULLS LAST  {{fetch_first(10)}}),visits AS (  SELECT tenant_id, COUNT(*) AS visit_count  FROM web_sessions  WHERE started_at >= DATE '2025-10-01' AND started_at < DATE '2026-01-01'  GROUP BY tenant_id),carts_created AS (  SELECT tenant_id, COUNT(*) AS cart_count  FROM carts  WHERE created_at >= DATE '2025-10-01' AND created_at < DATE '2026-01-01'  GROUP BY tenant_id),orders_placed AS (  SELECT tenant_id, COUNT(*) AS order_count  FROM orders  WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'  GROUP BY tenant_id),assembled AS (  SELECT    t.id AS tenant_id,    t.name AS tenant_name,    COALESCE(v.visit_count, 0) AS visits,    COALESCE(c.cart_count, 0) AS carts_created,    COALESCE(o.order_count, 0) AS orders_placed,    tt.paid_order_count AS orders_paid  FROM top_tenants tt  JOIN tenants t ON t.id = tt.tenant_id  LEFT JOIN visits v ON v.tenant_id = tt.tenant_id  LEFT JOIN carts_created c ON c.tenant_id = tt.tenant_id  LEFT JOIN orders_placed o ON o.tenant_id = tt.tenant_id)SELECT  tenant_name,  visits,  carts_created,  orders_placed,  orders_paid,  ROUND(CASE WHEN visits > 0 THEN 100.0 * carts_created / visits ELSE NULL END, 1) AS visit_to_cart_pct,  ROUND(CASE WHEN carts_created > 0 THEN 100.0 * orders_placed / carts_created ELSE NULL END, 1) AS cart_to_order_pct,  ROUND(CASE WHEN orders_placed > 0 THEN 100.0 * orders_paid / orders_placed ELSE NULL END, 1) AS order_to_paid_pct,  ROUND(CASE WHEN visits > 0 THEN 100.0 * orders_paid / visits ELSE NULL END, 1) AS overall_visit_to_paid_pct  CASE WHEN visits > 0 THEN 100.0 * carts_created / visits ELSE NULL END AS visit_to_cart_pct,  CASE WHEN carts_created > 0 THEN 100.0 * orders_placed / carts_created ELSE NULL END AS cart_to_order_pct,  CASE WHEN orders_placed > 0 THEN 100.0 * orders_paid / orders_placed ELSE NULL END AS order_to_paid_pct,  CASE WHEN visits > 0 THEN 100.0 * orders_paid / visits ELSE NULL END AS overall_visit_to_paid_pctFROM assembledORDER BY overall_visit_to_paid_pct DESC NULLS LAST, tenant_name ASCORDER BY overall_visit_to_paid_pct DESC NULLS LAST, tenant_name ASC NULLS LAST, tenant_id NULLS LAST

   As each system received it; highlighted, the parts spelled for that system

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · Firebolt · ClickHouse · chDB · clickhouse_brewDataFusion

   WITH paid_orders AS (
     SELECT tenant_id, COUNT(*) AS paid_order_count
     FROM orders
     WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'
       AND status NOT IN ('pending', 'cancelled')
     GROUP BY tenant_id
   ),
   top_tenants AS (
     SELECT tenant_id, paid_order_count
     FROM paid_orders
     ORDER BY paid_order_count DESC NULLS FIRST, tenant_id ASC NULLS LAST
     FETCH FIRST 10 ROWS ONLY
   ),
   visits AS (
     SELECT tenant_id, COUNT(*) AS visit_count
     FROM web_sessions
     WHERE started_at >= DATE '2025-10-01' AND started_at < DATE '2026-01-01'
     GROUP BY tenant_id
   ),
   carts_created AS (
     SELECT tenant_id, COUNT(*) AS cart_count
     FROM carts
     WHERE created_at >= DATE '2025-10-01' AND created_at < DATE '2026-01-01'
     GROUP BY tenant_id
   ),
   orders_placed AS (
     SELECT tenant_id, COUNT(*) AS order_count
     FROM orders
     WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'
     GROUP BY tenant_id
   ),
   assembled AS (
     SELECT
       t.id AS tenant_id,
       t.name AS tenant_name,
       COALESCE(v.visit_count, 0) AS visits,
       COALESCE(c.cart_count, 0) AS carts_created,
       COALESCE(o.order_count, 0) AS orders_placed,
       tt.paid_order_count AS orders_paid
     FROM top_tenants tt
     JOIN tenants t ON t.id = tt.tenant_id
     LEFT JOIN visits v ON v.tenant_id = tt.tenant_id
     LEFT JOIN carts_created c ON c.tenant_id = tt.tenant_id
     LEFT JOIN orders_placed o ON o.tenant_id = tt.tenant_id
   )
   SELECT
     tenant_name,
     visits,
     carts_created,
     orders_placed,
     orders_paid,
     CASE WHEN visits > 0 THEN 100.0 * carts_created / visits ELSE NULL END AS visit_to_cart_pct,
     CASE WHEN carts_created > 0 THEN 100.0 * orders_placed / carts_created ELSE NULL END AS cart_to_order_pct,
     CASE WHEN orders_placed > 0 THEN 100.0 * orders_paid / orders_placed ELSE NULL END AS order_to_paid_pct,
     CASE WHEN visits > 0 THEN 100.0 * orders_paid / visits ELSE NULL END AS overall_visit_to_paid_pct
   FROM assembled
   ORDER BY overall_visit_to_paid_pct DESC NULLS LAST, tenant_name ASC NULLS LAST, tenant_id NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q076 on the start page, with every configuration.

8. Everything, as recorded

   The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted

   Grace's whole session, 7 turns

   • The request, as Grace received it

     Report request from Emeka Obi (analyst for the VP Product):
     
     Title: Storefront-to-order funnel for top merchants, Q4 2025
     Question: Where do shoppers drop off between landing on the storefront and placing a paid order, and does the shape differ across our biggest merchants?
     Grain: one row per tenant (top 10 merchants by paid order volume in Q4 2025)
     Columns:
     - tenant name
     - visits (distinct web_sessions started in period)
     - carts created in period
     - orders placed in period (any status)
     - orders paid in period (status not in pending, cancelled)
     - visit → cart %
     - cart → order %
     - order → paid %
     - overall visit → paid %
     Details: Time window: 2025-10-01 to 2025-12-31 (orders.placed_at, carts.created_at, web_sessions.started_at all within this range). Tenant selection: rank tenants by count of orders with status not in ('pending','cancelled') and placed_at in this window; keep the top 10, ordered descending by that paid-order count. Tables: tenants (name), web_sessions (visits, started_at, tenant_id), carts (carts created, created_at, tenant_id), orders (orders placed and orders paid, placed_at, status, tenant_id). No FK links web_sessions to carts or carts to orders' session — just count each stage independently per tenant per period (don't try to force a row-level join between visits and carts). Round percentages to 1 decimal. Order final report by overall visit→paid % descending, tie-break by tenant name.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Grace calls dry_run

     WITH paid_orders AS (
       SELECT tenant_id, COUNT(*) AS paid_order_count
       FROM orders
       WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'
         AND status NOT IN ('pending', 'cancelled')
       GROUP BY tenant_id
     ),
     top_tenants AS (
       SELECT tenant_id, paid_order_count
       FROM paid_orders
       ORDER BY paid_order_count DESC, tenant_id ASC
       LIMIT 10
     ),
     visits AS (
       SELECT tenant_id, COUNT(*) AS visit_count
       FROM web_sessions
       WHERE started_at >= DATE '2025-10-01' AND started_at < DATE '2026-01-01'
       GROUP BY tenant_id
     ),
     carts_created AS (
       SELECT tenant_id, COUNT(*) AS cart_count
       FROM carts
       WHERE created_at >= DATE '2025-10-01' AND created_at < DATE '2026-01-01'
       GROUP BY tenant_id
     ),
     orders_placed AS (
       SELECT tenant_id, COUNT(*) AS order_count
       FROM orders
       WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'
       GROUP BY tenant_id
     ),
     assembled AS (
       SELECT
         t.id AS tenant_id,
         t.name AS tenant_name,
         COALESCE(v.visit_count, 0) AS visits,
         COALESCE(c.cart_count, 0) AS carts_created,
         COALESCE(o.order_count, 0) AS orders_placed,
         tt.paid_order_count AS orders_paid
       FROM top_tenants tt
       JOIN tenants t ON t.id = tt.tenant_id
       LEFT JOIN visits v ON v.tenant_id = tt.tenant_id
       LEFT JOIN carts_created c ON c.tenant_id = tt.tenant_id
       LEFT JOIN orders_placed o ON o.tenant_id = tt.tenant_id
     )
     SELECT
       tenant_name,
       visits,
       carts_created,
       orders_placed,
       orders_paid,
       ROUND(CASE WHEN visits > 0 THEN 100.0 * carts_created / visits ELSE NULL END, 1) AS visit_to_cart_pct,
       ROUND(CASE WHEN carts_created > 0 THEN 100.0 * orders_placed / carts_created ELSE NULL END, 1) AS cart_to_order_pct,
       ROUND(CASE WHEN orders_placed > 0 THEN 100.0 * orders_paid / orders_placed ELSE NULL END, 1) AS order_to_paid_pct,
       ROUND(CASE WHEN visits > 0 THEN 100.0 * orders_paid / visits ELSE NULL END, 1) AS overall_visit_to_paid_pct
     FROM assembled
     ORDER BY overall_visit_to_paid_pct DESC NULLS LAST, tenant_name ASC

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: tenant_name, visits, carts_created, orders_placed, orders_paid, visit_to_cart_pct, cart_to_order_pct, order_to_paid_pct, overall_visit_to_paid_pct

   • GraceQuery compiles cleanly with the right columns. Submitting.
   • Grace calls submit

     WITH paid_orders AS (
       SELECT tenant_id, COUNT(*) AS paid_order_count
       FROM orders
       WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'
         AND status NOT IN ('pending', 'cancelled')
       GROUP BY tenant_id
     ),
     top_tenants AS (
       SELECT tenant_id, paid_order_count
       FROM paid_orders
       ORDER BY paid_order_count DESC, tenant_id ASC
       LIMIT 10
     ),
     visits AS (
       SELECT tenant_id, COUNT(*) AS visit_count
       FROM web_sessions
       WHERE started_at >= DATE '2025-10-01' AND started_at < DATE '2026-01-01'
       GROUP BY tenant_id
     ),
     carts_created AS (
       SELECT tenant_id, COUNT(*) AS cart_count
       FROM carts
       WHERE created_at >= DATE '2025-10-01' AND created_at < DATE '2026-01-01'
       GROUP BY tenant_id
     ),
     orders_placed AS (
       SELECT tenant_id, COUNT(*) AS order_count
       FROM orders
       WHERE placed_at >= DATE '2025-10-01' AND placed_at < DATE '2026-01-01'
       GROUP BY tenant_id
     ),
     assembled AS (
       SELECT
         t.id AS tenant_id,
         t.name AS tenant_name,
         COALESCE(v.visit_count, 0) AS visits,
         COALESCE(c.cart_count, 0) AS carts_created,
         COALESCE(o.order_count, 0) AS orders_placed,
         tt.paid_order_count AS orders_paid
       FROM top_tenants tt
       JOIN tenants t ON t.id = tt.tenant_id
       LEFT JOIN visits v ON v.tenant_id = tt.tenant_id
       LEFT JOIN carts_created c ON c.tenant_id = tt.tenant_id
       LEFT JOIN orders_placed o ON o.tenant_id = tt.tenant_id
     )
     SELECT
       tenant_name,
       visits,
       carts_created,
       orders_placed,
       orders_paid,
       ROUND(CASE WHEN visits > 0 THEN 100.0 * carts_created / visits ELSE NULL END, 1) AS visit_to_cart_pct,
       ROUND(CASE WHEN carts_created > 0 THEN 100.0 * orders_placed / carts_created ELSE NULL END, 1) AS cart_to_order_pct,
       ROUND(CASE WHEN orders_placed > 0 THEN 100.0 * orders_paid / orders_placed ELSE NULL END, 1) AS order_to_paid_pct,
       ROUND(CASE WHEN visits > 0 THEN 100.0 * orders_paid / visits ELSE NULL END, 1) AS overall_visit_to_paid_pct
     FROM assembled
     ORDER BY overall_visit_to_paid_pct DESC NULLS LAST, tenant_name ASC

   • The warehouse answers

     Submitted to tonight's batch. Your work on this request is done.

   • GraceSubmitted. The report returns one row per top-10 tenant (by Q4 2025 paid-order count) with visits, carts, orders placed, orders paid, and the four conversion percentages, ordered by overall visit→paid % descending.

   Not shown here: 239 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Grace's transcript (264 KB) · Emeka Obi's transcript (864 KB) · Jonas Feld's transcript (263 KB) · the night's plan (5 KB) · the night's reports (12 KB) · the week's memo (3 KB)