Queries · q080 · VP Product · week 1, night 2

Where orders stall after checkout: status-transition funnel, Q4 2025

“Within the post-checkout order lifecycle, is the drop-off to a paid order a single cliff at one status or a slow bleed spread across several, and which status is it?”

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 2, request 2 of 3

   Reading the three reports critically:

   Report 1 (funnel): several tenants show visit→cart conversion ABOVE 100% (up to 191.5%), which is impossible for a real per-visitor funnel. The engineer's note explains why: stages are counted independently with no row-level join, so this isn't "% of visitors who added to cart" — it's just cart_count/visit_count. The merchants with the worst over-100% ratios (Brennquell Kaffee, Spice Route Box, Grind Collective, Honeycomb Larder) read like subscription/recurring-box businesses, while the merchants with ratios close to 100-150% and a big cart→order drop (Kibblewick, Studio Vrij, Petalcourt Beauty, Marrowby & Vane, Cragsley Outfitters, Ampfeld Elektronik) read like regular one-off retail. My hypothesis: subscription merchants generate many carts/orders from recurring billing that never involve a fresh tracked web session, which would make "visits" structurally incomparable across merchant types. I want to test that before telling Jonas where the "biggest leak" is, otherwise I'd be mixing a tracking-coverage artifact with real shopper drop-off.

   For the real retail merchants, the consistent, trustworthy signal across stages is: cart→order is the dominant drop (27-61% conversion) while order→paid is consistently high (94-99%). That's the leak worth roadmapping — but I want one more clean (schema-safe) cut at where in that drop it happens, since Report 2's device/channel attempt failed.

   Report 2 (checkout by device/channel): 16,987 of ~17,158 carts (99%) landed in device=unknown/channel=unknown — the engineer flagged exactly this risk. This means the user_id-based same-day join to web_sessions essentially didn't work (most carts are likely guest/anonymous, with no user_id to match on). I'm treating device/channel-by-cart as not reliably answerable with the current schema and pivoting to schema-clean alternatives (order_status_history for stall location; cart timestamps for cliff-vs-bleed shape) rather than re-trying the same fragile join.

   Report 3 (repeat purchase): looks internally consistent — no red flags. Repeat-within-90-days sits in a 40-62% band, median days-to-second-order 32-78 days, with the two most recent cohorts showing lower medians as expected from a shorter observation window. I consider priority 5 answered; no follow-up planned tonight.

   Tonight's 3 requests: (A) test the subscription-vs-session-tracking-gap hypothesis from Report 1, (B) find exactly which status the post-checkout order stalls at using the clean orders/order_status_history join (no fragile session join needed), (C) check whether cart abandonment is a quick cliff or a slow lingering bleed using carts' own timestamps, restricted to the real-retail tenants so we're not mixing in the subscription pattern.

   Read the whole plan
   Where orders stall after checkout: status-transition funnel, Q4 2025

   Question

   Within the post-checkout order lifecycle, is the drop-off to a paid order a single cliff at one status or a slow bleed spread across several, and which status is it?

   One row per

   order status

   Columns

   • status
   • orders reaching this status (count)
   • pct of total orders in window
   • median hours from order creation to reaching this status

4. Bea 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 base_orders AS (
     SELECT o.id AS order_id, o.tenant_id, o.created_at AS order_created_at
     FROM orders o
     JOIN (
       SELECT o2.tenant_id, COUNT(*) AS order_count
       FROM orders o2
       WHERE o2.created_at >= DATE '2025-10-01' AND o2.created_at < DATE '2026-01-01'
       GROUP BY o2.tenant_id
       ORDER BY order_count DESC
       LIMIT 10
     ) top_tenants ON top_tenants.tenant_id = o.tenant_id
     WHERE o.created_at >= DATE '2025-10-01' AND o.created_at < DATE '2026-01-01'
   ),
   total_orders AS (
     SELECT COUNT(DISTINCT order_id) AS total_orders FROM base_orders
   ),
   status_list AS (
     SELECT status_name FROM (VALUES ('pending'),('confirmed'),('paid'),('partially_fulfilled'),('fulfilled'),('cancelled'),('refunded')) AS s(status_name)
   ),
   first_reach AS (
     SELECT bo.order_id, osh.to_status AS status_name, MIN(osh.created_at) AS first_reached_at
     FROM base_orders bo
     JOIN order_status_history osh ON osh.order_id = bo.order_id
     WHERE osh.to_status IN ('pending','confirmed','paid','partially_fulfilled','fulfilled','cancelled','refunded')
     GROUP BY bo.order_id, osh.to_status
   ),
   status_metrics AS (
     SELECT
       sl.status_name,
       COUNT(DISTINCT fr.order_id) AS orders_reaching_status,
       PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY (EXTRACT(EPOCH FROM (fr.first_reached_at - bo.order_created_at)) / 3600.0)) AS median_hours
     FROM status_list sl
     LEFT JOIN first_reach fr ON fr.status_name = sl.status_name
     LEFT JOIN base_orders bo ON bo.order_id = fr.order_id
     GROUP BY sl.status_name
   )
   SELECT
     sm.status_name AS status,
     COALESCE(sm.orders_reaching_status, 0) AS orders_reaching_status,
     ROUND(COALESCE(sm.orders_reaching_status, 0) * 100.0 / t.total_orders, 1) AS pct_of_total_orders,
     ROUND(sm.median_hours, 1) AS median_hours_to_status
   FROM status_metrics sm
   CROSS JOIN total_orders t
   ORDER BY pct_of_total_orders DESC, sm.status_name ASC

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

   “Submitted. Summary of what I did and the one deviation: - Base population: orders created 2025-10-01 to 2025-12-31 (exclusive upper bound 2026-01-01), restricted to tenants. - Tenant selection caveat: the request referenced "the same 10 tenants as the Q4 funnel report," but no tenant names were provided to me and I have no access to that companion request. I substituted the 10 tenants with the highest order counts in the Q4 2025 window as a defensible proxy, and flagged this clearly in the submit note so the analyst can correct it if the actual list differs. - Grain: one row per status (pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded). - Measures: orders reaching each status (count of distinct orders with at least one order_status_history row with that to_status), pct of total base-population orders, and median hours from orders.created_at to the first time that status was reached (via PERCENTILE_CONT). - Ordering: pct descending, tie-break by status name, per spec.”

5. The report comes back overnight

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

   status              | orders_reaching_status | pct_of_total_orders | median_hours_to_status
   --------------------+------------------------+---------------------+-----------------------
   pending             |                   4909 |               100.0 |                    0.0
   paid                |                   4810 |                98.0 |                    0.0
   fulfilled           |                   4697 |                95.7 |                   22.3
   refunded            |                    179 |                 3.6 |                  523.6
   cancelled           |                    162 |                 3.3 |                    0.1
   confirmed           |                      0 |                 0.0 |                   NULL
   partially_fulfilled |                      0 |                 0.0 |                   NULL
   (7 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:

   • q082Where orders stall after checkout, Q4 2025 — retail merchants only (corrected)

   It builds on:

   • q076Storefront-to-order funnel for top merchants, Q4 2025

7. It becomes benchmark query q080

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

   • spellingfetch first
   • determinismtie-breaker: tenant_id is the grouping key
   • numericsdocs/NUMERICS.md: rounding removed; results keep full precision
   • numericsdocs/NUMERICS.md: divisor guarded with NULLIF(..., 0); it is zero when only part of the data is loaded (an empty period), where engines raise an error or return inf/nan
   • numericsdocs/NUMERICS.md: EXTRACT(EPOCH FROM (b - a)) cast to NUMERIC(18,6)
   • made explicita selected t.col gets AS col, its standard name made explicit (4×)
   • placeholderpercentile cont
   • placeholderseconds between
   • 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 base_orders AS (  SELECT o.id AS order_id, o.tenant_id, o.created_at AS order_created_at  SELECT o.id AS order_id, o.tenant_id AS tenant_id, o.created_at AS order_created_at  FROM orders o  JOIN (    SELECT o2.tenant_id, COUNT(*) AS order_count    SELECT o2.tenant_id AS tenant_id, COUNT(*) AS order_count    FROM orders o2    WHERE o2.created_at >= DATE '2025-10-01' AND o2.created_at < DATE '2026-01-01'    GROUP BY o2.tenant_id    ORDER BY order_count DESC    LIMIT 10    ORDER BY order_count DESC NULLS FIRST, o2.tenant_id NULLS LAST    {{fetch_first(10)}}  ) top_tenants ON top_tenants.tenant_id = o.tenant_id  WHERE o.created_at >= DATE '2025-10-01' AND o.created_at < DATE '2026-01-01'),total_orders AS (  SELECT COUNT(DISTINCT order_id) AS total_orders FROM base_orders),status_list AS (  SELECT status_name FROM (VALUES ('pending'),('confirmed'),('paid'),('partially_fulfilled'),('fulfilled'),('cancelled'),('refunded')) AS s(status_name)),first_reach AS (  SELECT bo.order_id, osh.to_status AS status_name, MIN(osh.created_at) AS first_reached_at  SELECT bo.order_id AS order_id, osh.to_status AS status_name, MIN(osh.created_at) AS first_reached_at  FROM base_orders bo  JOIN order_status_history osh ON osh.order_id = bo.order_id  WHERE osh.to_status IN ('pending','confirmed','paid','partially_fulfilled','fulfilled','cancelled','refunded')  GROUP BY bo.order_id, osh.to_status),status_metrics AS (  SELECT    sl.status_name,    sl.status_name AS status_name,    COUNT(DISTINCT fr.order_id) AS orders_reaching_status,    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY (EXTRACT(EPOCH FROM (fr.first_reached_at - bo.order_created_at)) / 3600.0)) AS median_hours    {{percentile_cont(0.5, ({{seconds_between(bo.order_created_at, fr.first_reached_at)}} / 3600.0))}} AS median_hours  FROM status_list sl  LEFT JOIN first_reach fr ON fr.status_name = sl.status_name  LEFT JOIN base_orders bo ON bo.order_id = fr.order_id  GROUP BY sl.status_name)SELECT  sm.status_name AS status,  COALESCE(sm.orders_reaching_status, 0) AS orders_reaching_status,  ROUND(COALESCE(sm.orders_reaching_status, 0) * 100.0 / t.total_orders, 1) AS pct_of_total_orders,  ROUND(sm.median_hours, 1) AS median_hours_to_status  COALESCE(sm.orders_reaching_status, 0) * 100.0 / NULLIF(t.total_orders, 0) AS pct_of_total_orders,  sm.median_hours AS median_hours_to_statusFROM status_metrics smCROSS JOIN total_orders tORDER BY pct_of_total_orders DESC, sm.status_name ASCORDER BY pct_of_total_orders DESC NULLS FIRST, sm.status_name ASC NULLS LAST

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

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · clickhouse_brewDataFusionFireboltClickHouse · chDB

   WITH base_orders AS (
     SELECT o.id AS order_id, o.tenant_id AS tenant_id, o.created_at AS order_created_at
     FROM orders o
     JOIN (
       SELECT o2.tenant_id AS tenant_id, COUNT(*) AS order_count
       FROM orders o2
       WHERE o2.created_at >= DATE '2025-10-01' AND o2.created_at < DATE '2026-01-01'
       GROUP BY o2.tenant_id
       ORDER BY order_count DESC NULLS FIRST, o2.tenant_id NULLS LAST
       FETCH FIRST 10 ROWS ONLY
     ) top_tenants ON top_tenants.tenant_id = o.tenant_id
     WHERE o.created_at >= DATE '2025-10-01' AND o.created_at < DATE '2026-01-01'
   ),
   total_orders AS (
     SELECT COUNT(DISTINCT order_id) AS total_orders FROM base_orders
   ),
   status_list AS (
     SELECT status_name FROM (VALUES ('pending'),('confirmed'),('paid'),('partially_fulfilled'),('fulfilled'),('cancelled'),('refunded')) AS s(status_name)
   ),
   first_reach AS (
     SELECT bo.order_id AS order_id, osh.to_status AS status_name, MIN(osh.created_at) AS first_reached_at
     FROM base_orders bo
     JOIN order_status_history osh ON osh.order_id = bo.order_id
     WHERE osh.to_status IN ('pending','confirmed','paid','partially_fulfilled','fulfilled','cancelled','refunded')
     GROUP BY bo.order_id, osh.to_status
   ),
   status_metrics AS (
     SELECT
       sl.status_name AS status_name,
       COUNT(DISTINCT fr.order_id) AS orders_reaching_status,
       PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY (CAST(EXTRACT(EPOCH FROM (fr.first_reached_at - bo.order_created_at)) AS NUMERIC(18,6)) / 3600.0)) AS median_hours
     FROM status_list sl
     LEFT JOIN first_reach fr ON fr.status_name = sl.status_name
     LEFT JOIN base_orders bo ON bo.order_id = fr.order_id
     GROUP BY sl.status_name
   )
   SELECT
     sm.status_name AS status,
     COALESCE(sm.orders_reaching_status, 0) AS orders_reaching_status,
     COALESCE(sm.orders_reaching_status, 0) * 100.0 / NULLIF(t.total_orders, 0) AS pct_of_total_orders,
     sm.median_hours AS median_hours_to_status
   FROM status_metrics sm
   CROSS JOIN total_orders t
   ORDER BY pct_of_total_orders DESC NULLS FIRST, sm.status_name ASC NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q080 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

   Bea's whole session, 6 turns

   • The request, as Bea received it

     Report request from Emeka Obi (analyst for the VP Product):
     
     Title: Where orders stall after checkout: status-transition funnel, Q4 2025
     Question: Within the post-checkout order lifecycle, is the drop-off to a paid order a single cliff at one status or a slow bleed spread across several, and which status is it?
     Grain: one row per order status
     Columns:
     - status
     - orders reaching this status (count)
     - pct of total orders in window
     - median hours from order creation to reaching this status
     Details: Base population: orders with created_at in 2025-10-01 to 2025-12-31, restricted to the same 10 tenants as the Q4 funnel report (tenant_id lookup by name as in the companion request). total_orders = count of distinct such orders. For each status in (pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded), 'orders reaching this status' = count of distinct orders that have at least one row in order_status_history with to_status = that status (occurred within the same order's lifetime, no extra date filter beyond the order being in the base population). Median hours = median of (first order_status_history.created_at with that to_status for the order) minus orders.created_at, in hours, rounded to 1 decimal. pct of total orders = orders_reaching_status / total_orders * 100, rounded to 1 decimal. Order rows by pct of total orders descending, tie-break by status name.
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Bea calls dry_run

     WITH base_orders AS (
       SELECT o.id AS order_id, o.tenant_id, o.created_at AS order_created_at
       FROM orders o
       JOIN (
         SELECT o2.tenant_id, COUNT(*) AS order_count
         FROM orders o2
         WHERE o2.created_at >= DATE '2025-10-01' AND o2.created_at < DATE '2026-01-01'
         GROUP BY o2.tenant_id
         ORDER BY order_count DESC
         LIMIT 10
       ) top_tenants ON top_tenants.tenant_id = o.tenant_id
       WHERE o.created_at >= DATE '2025-10-01' AND o.created_at < DATE '2026-01-01'
     ),
     total_orders AS (
       SELECT COUNT(DISTINCT order_id) AS total_orders FROM base_orders
     ),
     status_list AS (
       SELECT status_name FROM (VALUES ('pending'),('confirmed'),('paid'),('partially_fulfilled'),('fulfilled'),('cancelled'),('refunded')) AS s(status_name)
     ),
     first_reach AS (
       SELECT bo.order_id, osh.to_status AS status_name, MIN(osh.created_at) AS first_reached_at
       FROM base_orders bo
       JOIN order_status_history osh ON osh.order_id = bo.order_id
       WHERE osh.to_status IN ('pending','confirmed','paid','partially_fulfilled','fulfilled','cancelled','refunded')
       GROUP BY bo.order_id, osh.to_status
     ),
     status_metrics AS (
       SELECT
         sl.status_name,
         COUNT(DISTINCT fr.order_id) AS orders_reaching_status,
         PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY (EXTRACT(EPOCH FROM (fr.first_reached_at - bo.order_created_at)) / 3600.0)) AS median_hours
       FROM status_list sl
       LEFT JOIN first_reach fr ON fr.status_name = sl.status_name
       LEFT JOIN base_orders bo ON bo.order_id = fr.order_id
       GROUP BY sl.status_name
     )
     SELECT
       sm.status_name AS status,
       COALESCE(sm.orders_reaching_status, 0) AS orders_reaching_status,
       ROUND(COALESCE(sm.orders_reaching_status, 0) * 100.0 / t.total_orders, 1) AS pct_of_total_orders,
       ROUND(sm.median_hours, 1) AS median_hours_to_status
     FROM status_metrics sm
     CROSS JOIN total_orders t
     ORDER BY pct_of_total_orders DESC, sm.status_name ASC

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: status, orders_reaching_status, pct_of_total_orders, median_hours_to_status

   • Bea calls submit

     WITH base_orders AS (
       SELECT o.id AS order_id, o.tenant_id, o.created_at AS order_created_at
       FROM orders o
       JOIN (
         SELECT o2.tenant_id, COUNT(*) AS order_count
         FROM orders o2
         WHERE o2.created_at >= DATE '2025-10-01' AND o2.created_at < DATE '2026-01-01'
         GROUP BY o2.tenant_id
         ORDER BY order_count DESC
         LIMIT 10
       ) top_tenants ON top_tenants.tenant_id = o.tenant_id
       WHERE o.created_at >= DATE '2025-10-01' AND o.created_at < DATE '2026-01-01'
     ),
     total_orders AS (
       SELECT COUNT(DISTINCT order_id) AS total_orders FROM base_orders
     ),
     status_list AS (
       SELECT status_name FROM (VALUES ('pending'),('confirmed'),('paid'),('partially_fulfilled'),('fulfilled'),('cancelled'),('refunded')) AS s(status_name)
     ),
     first_reach AS (
       SELECT bo.order_id, osh.to_status AS status_name, MIN(osh.created_at) AS first_reached_at
       FROM base_orders bo
       JOIN order_status_history osh ON osh.order_id = bo.order_id
       WHERE osh.to_status IN ('pending','confirmed','paid','partially_fulfilled','fulfilled','cancelled','refunded')
       GROUP BY bo.order_id, osh.to_status
     ),
     status_metrics AS (
       SELECT
         sl.status_name,
         COUNT(DISTINCT fr.order_id) AS orders_reaching_status,
         PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY (EXTRACT(EPOCH FROM (fr.first_reached_at - bo.order_created_at)) / 3600.0)) AS median_hours
       FROM status_list sl
       LEFT JOIN first_reach fr ON fr.status_name = sl.status_name
       LEFT JOIN base_orders bo ON bo.order_id = fr.order_id
       GROUP BY sl.status_name
     )
     SELECT
       sm.status_name AS status,
       COALESCE(sm.orders_reaching_status, 0) AS orders_reaching_status,
       ROUND(COALESCE(sm.orders_reaching_status, 0) * 100.0 / t.total_orders, 1) AS pct_of_total_orders,
       ROUND(sm.median_hours, 1) AS median_hours_to_status
     FROM status_metrics sm
     CROSS JOIN total_orders t
     ORDER BY pct_of_total_orders DESC, sm.status_name ASC

   • The warehouse answers

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

   • BeaSubmitted. Summary of what I did and the one deviation: - **Base population**: orders created 2025-10-01 to 2025-12-31 (exclusive upper bound 2026-01-01), restricted to tenants. - **Tenant selection caveat**: the request referenced "the same 10 tenants as the Q4 funnel report," but no tenant names were provided to me and I have no access to that companion request. I substituted the 10 tenants with the highest order counts in the Q4 2025 window as a defensible proxy, and flagged this clearly in the submit note so the analyst can correct it if the actual list differs. - **Grain**: one row per status (pending, confirmed, paid, partially_fulfilled, fulfilled, cancelled, refunded). - **Measures**: orders reaching each status (count of distinct orders with at least one `order_status_history` row with that `to_status`), pct of total base-population orders, and median hours from `orders.created_at` to the first time that status was reached (via `PERCENTILE_CONT`). - **Ordering**: pct descending, tie-break by status name, per spec.

   Not shown here: 240 KB of context the agent was given, its instructions and the schema documentation; it is in the raw transcript.

   Bea's transcript (272 KB) · Emeka Obi's transcript (864 KB) · Jonas Feld's transcript (263 KB) · the night's plan (7 KB) · the night's reports (4 KB) · the week's memo (3 KB)