Queries · q351 · Head of Merchandising · week 2, night 2

Image-Count Buckets: Product Age, Status, and Vertical Mix

“Are zero-image product listings systematically different from one-plus-image listings (older, more likely draft/archived, or concentrated in certain verticals), rather than the image count itself being the driver?”

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

1. Freya Lindgren writes to Zara Ahmed

   The manager, at the start of week 2

   Subject: Good start — let's dig into the risk areas

   Hi Zara,

   This is really useful, and a few things jumped out that I want to understand better before Friday.

   The Cragsley Outfitters finding worries me most — our #2 merchant by revenue with real quality concentration, and some of those poorly-rated items are still selling briskly. That combination (strong sales, weak ratings, sizable product count) feels like the kind of thing that erodes trust quietly until it doesn't. I'd like to understand it better: is this concentrated in certain kinds of products, has it been building over time, and does it look like it's about to hit sales or is demand just lagging behind reputation?

   Second, the December rating dip needs a clearer story. You flagged it as needing a second month to confirm — I agree, but I'd also like to understand whether there's something explainable behind it (seasonal returns, a particular product push, shipping delays around the holidays) rather than just watching and waiting. If Fennel & Flint and Wiesgrund Wohnen are worth a check-in, I want to go into that conversation knowing roughly what happened.

   Third, I was surprised that going from zero to one image makes such a big difference but more images after that don't. Before I turn that into a merchant talking point, I want to know if we're actually looking at a real presentation effect or just a stand-in for something else — are the zero-image listings systematically different in some way, like older, or a different kind of product?

   Finally, on bundles: you said it's white space but we can't test performance yet because usage is too rare. I'd like to understand it a bit more anecdotally before we design a pilot — among the handful of merchants that do use bundles, is there anything that distinguishes them, even descriptively, from the ones that don't?

   Thanks again — this is exactly the kind of clarity I needed.

   Freya

   Show the whole note

2. Zara Ahmed plans the night

   The analyst · week 2, night 2, request 2 of 3

   Night 1 findings on Cragsley: the poor ratings concentrate in soft-goods categories (Loungewear 66.7%/40.0% poor, Outerwear 50.0%/28.6%, Shirts 50.0%, Tops/Trousers 37-40%) while Boots, Knitwear, Scarves & Hats, Sandals are clean (0-17%) — looks like a fit/fabric-quality issue in specific garment types, not catalog-wide. Note: category names repeat (Tops, Loungewear, Outerwear, Trousers, Knitwear each appear twice) — likely duplicate category records, a data-quality wrinkle worth flagging separately but not urgent. The rating trend shows this is NOT new: pct_1_2_star spikes every December (25.0% in 2023, 22.1% in 2024, 25.7% in 2025) alongside every November/December order surge — a recurring seasonal pattern, not a 2025-specific crisis, which tempers how I should frame the Dec 2025 dip for Cragsley specifically. The sales-trajectory report shows the poor-tier's absolute revenue did grow (896→6,177 EUR/month) but its share of Cragsley's total shrank from ~23% to ~6% as the rest of the catalog scaled much faster — so it's 'stagnating while the business grows around it,' not yet a demand collapse. That closes out the Cragsley thread well enough for the memo. Tonight I'm moving to the other three asks: (1) test the shipping/carrier hypothesis for the Dec rating dip by looking at delivery performance alongside review sentiment for Fennel & Flint and Wiesgrund Wohnen specifically, since COO's team flagged an Albion Parcel carrier breakdown in exactly this window; (2) test whether zero-image products are systematically older/draft/different-vertical rather than just under-photographed, by looking at age, status and vertical mix by image bucket without restricting to active products only (the earlier report did, which could have hidden this); (3) get a raw, merchant-level profile of the handful of merchants that actually use bundles, to look for descriptive patterns before even thinking about a pilot design.

   Read the whole plan
   Image-Count Buckets: Product Age, Status, and Vertical Mix

   Question

   Are zero-image product listings systematically different from one-plus-image listings (older, more likely draft/archived, or concentrated in certain verticals), rather than the image count itself being the driver?

   One row per

   product image-count bucket (0, 1, 2-3, 4-6, 7+), across ALL products regardless of status (not just active ones, to test the status hypothesis)

   Columns

   • image_count_bucket
   • product_count
   • avg_days_since_created
   • pct_status_draft
   • pct_status_archived
   • pct_status_active
   • pct_apparel_footwear_industry
   • pct_b2b_industry (Office Supplies + Industrial Supplies combined)
   • avg_variants_per_product

3. 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 product_base AS (
     SELECT
       t0.id AS product_id,
       t0.tenant_id AS tenant_id,
       t0.status AS status,
       t0.created_at AS created_at,
       CASE
         WHEN COALESCE(t1.image_count, 0) = 0 THEN '0'
         WHEN COALESCE(t1.image_count, 0) = 1 THEN '1'
         WHEN COALESCE(t1.image_count, 0) BETWEEN 2 AND 3 THEN '2-3'
         WHEN COALESCE(t1.image_count, 0) BETWEEN 4 AND 6 THEN '4-6'
         ELSE '7+'
       END AS image_count_bucket
     FROM products t0
     LEFT JOIN (
       SELECT
         t1a.product_id AS product_id,
         COUNT(*) AS image_count
       FROM product_media t1a
       WHERE t1a.kind = 'image'
       GROUP BY t1a.product_id
     ) t1 ON t1.product_id = t0.id
   ),
   dim_bucket AS (
     SELECT DISTINCT t2.image_count_bucket AS image_count_bucket
     FROM product_base t2
   ),
   m_count AS (
     SELECT
       t3.image_count_bucket AS image_count_bucket,
       COUNT(*) AS product_count
     FROM product_base t3
     GROUP BY t3.image_count_bucket
   ),
   m_age AS (
     SELECT
       t4.image_count_bucket AS image_count_bucket,
       AVG(CAST(DATE '2025-12-31' - CAST(t4.created_at AS date) AS numeric)) AS avg_days_since_created
     FROM product_base t4
     GROUP BY t4.image_count_bucket
   ),
   m_status AS (
     SELECT
       t5.image_count_bucket AS image_count_bucket,
       100.0 * SUM(CASE WHEN t5.status = 'draft' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_draft,
       100.0 * SUM(CASE WHEN t5.status = 'archived' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_archived,
       100.0 * SUM(CASE WHEN t5.status = 'active' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_active
     FROM product_base t5
     GROUP BY t5.image_count_bucket
   ),
   m_industry AS (
     SELECT
       t6.image_count_bucket AS image_count_bucket,
       100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%apparel%' OR LOWER(t7.industry) LIKE '%footwear%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_apparel_footwear_industry,
       100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%office%' OR LOWER(t7.industry) LIKE '%industrial%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_b2b_industry
     FROM product_base t6
     JOIN tenants t7 ON t7.id = t6.tenant_id
     GROUP BY t6.image_count_bucket
   ),
   m_variants AS (
     SELECT
       t8.image_count_bucket AS image_count_bucket,
       AVG(COALESCE(t9.variant_count, 0)) AS avg_variants_per_product
     FROM product_base t8
     LEFT JOIN (
       SELECT
         t9a.product_id AS product_id,
         COUNT(*) AS variant_count
       FROM product_variants t9a
       GROUP BY t9a.product_id
     ) t9 ON t9.product_id = t8.product_id
     GROUP BY t8.image_count_bucket
   )
   SELECT
     t10.image_count_bucket AS image_count_bucket,
     COALESCE(t11.product_count, 0) AS product_count,
     ROUND(COALESCE(t12.avg_days_since_created, 0), 1) AS avg_days_since_created,
     ROUND(COALESCE(t13.pct_status_draft, 0), 1) AS pct_status_draft,
     ROUND(COALESCE(t13.pct_status_archived, 0), 1) AS pct_status_archived,
     ROUND(COALESCE(t13.pct_status_active, 0), 1) AS pct_status_active,
     ROUND(COALESCE(t14.pct_apparel_footwear_industry, 0), 1) AS pct_apparel_footwear_industry,
     ROUND(COALESCE(t14.pct_b2b_industry, 0), 1) AS pct_b2b_industry,
     ROUND(COALESCE(t15.avg_variants_per_product, 0), 1) AS avg_variants_per_product
   FROM dim_bucket t10
   LEFT JOIN m_count t11 ON t11.image_count_bucket = t10.image_count_bucket
   LEFT JOIN m_age t12 ON t12.image_count_bucket = t10.image_count_bucket
   LEFT JOIN m_status t13 ON t13.image_count_bucket = t10.image_count_bucket
   LEFT JOIN m_industry t14 ON t14.image_count_bucket = t10.image_count_bucket
   LEFT JOIN m_variants t15 ON t15.image_count_bucket = t10.image_count_bucket
   ORDER BY
     CASE t10.image_count_bucket
       WHEN '0' THEN 1
       WHEN '1' THEN 2
       WHEN '2-3' THEN 3
       WHEN '4-6' THEN 4
       WHEN '7+' THEN 5
     END

   “Query compiles cleanly with the expected columns. Submitting now.”

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

   “Submitted. The report builds five image-count buckets (0, 1, 2-3, 4-6, 7+) across all products regardless of status/deletion, with product age, status mix, industry mix, and average variants per product computed per bucket. One caveat noted to the analyst: since tenants.industry is free-text rather than a coded field, the apparel/footwear and B2B industry shares are derived via keyword matching, which should be verified against actual values if results look off.”

4. The report comes back overnight

   As Zara Ahmed saw it, from the small dataset the agents worked on

   image_count_bucket | product_count | avg_days_since_created | pct_status_draft | pct_status_archived | pct_status_active | pct_apparel_footwear_industry | pct_b2b_industry | avg_variants_per_product
   -------------------+---------------+------------------------+------------------+---------------------+-------------------+-------------------------------+------------------+-------------------------
   0                  |            85 |                 1136.1 |              0.0 |                 0.0 |             100.0 |                          34.1 |              0.0 |                      3.4
   1                  |          1951 |                 1039.9 |              3.0 |                56.0 |              41.0 |                          26.7 |              7.8 |                      5.5
   2-3                |         10226 |                 1048.1 |              2.8 |                58.2 |              39.0 |                          26.5 |              7.7 |                      5.4
   4-6                |          8118 |                 1042.6 |              3.0 |                57.8 |              39.2 |                          26.9 |              7.7 |                      5.5
   7+                 |           669 |                 1032.0 |              3.3 |                58.0 |              38.7 |                          27.5 |              8.5 |                      5.5
   (5 rows)

5. What came of it

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

   It builds on:

   • q137Catalog Richness vs Sales Performance by Image Count Bucket
   • q139Image-Richness vs Sales Within Apparel & Footwear

6. It becomes benchmark query q351

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

   • numericsdocs/NUMERICS.md: bare NUMERIC cast that only served the rounding removed; AVG over an integer computed as a decimal: AVG(CAST(x AS NUMERIC(18,6)))
   • numericsdocs/NUMERICS.md: AVG over an integer computed as a decimal: AVG(CAST(x AS NUMERIC(18,6)))
   • 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
   The benchmark text, against the submitted SQL

   WITH product_base AS (  SELECT    t0.id AS product_id,    t0.tenant_id AS tenant_id,    t0.status AS status,    t0.created_at AS created_at,    CASE      WHEN COALESCE(t1.image_count, 0) = 0 THEN '0'      WHEN COALESCE(t1.image_count, 0) = 1 THEN '1'      WHEN COALESCE(t1.image_count, 0) BETWEEN 2 AND 3 THEN '2-3'      WHEN COALESCE(t1.image_count, 0) BETWEEN 4 AND 6 THEN '4-6'      ELSE '7+'    END AS image_count_bucket  FROM products t0  LEFT JOIN (    SELECT      t1a.product_id AS product_id,      COUNT(*) AS image_count    FROM product_media t1a    WHERE t1a.kind = 'image'    GROUP BY t1a.product_id  ) t1 ON t1.product_id = t0.id),dim_bucket AS (  SELECT DISTINCT t2.image_count_bucket AS image_count_bucket  FROM product_base t2),m_count AS (  SELECT    t3.image_count_bucket AS image_count_bucket,    COUNT(*) AS product_count  FROM product_base t3  GROUP BY t3.image_count_bucket),m_age AS (  SELECT    t4.image_count_bucket AS image_count_bucket,    AVG(CAST(DATE '2025-12-31' - CAST(t4.created_at AS date) AS numeric)) AS avg_days_since_created    AVG(CAST(DATE '2025-12-31' - CAST(t4.created_at AS date) AS numeric(18,6))) AS avg_days_since_created  FROM product_base t4  GROUP BY t4.image_count_bucket),m_status AS (  SELECT    t5.image_count_bucket AS image_count_bucket,    100.0 * SUM(CASE WHEN t5.status = 'draft' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_draft,    100.0 * SUM(CASE WHEN t5.status = 'archived' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_archived,    100.0 * SUM(CASE WHEN t5.status = 'active' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_active  FROM product_base t5  GROUP BY t5.image_count_bucket),m_industry AS (  SELECT    t6.image_count_bucket AS image_count_bucket,    100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%apparel%' OR LOWER(t7.industry) LIKE '%footwear%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_apparel_footwear_industry,    100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%office%' OR LOWER(t7.industry) LIKE '%industrial%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_b2b_industry  FROM product_base t6  JOIN tenants t7 ON t7.id = t6.tenant_id  GROUP BY t6.image_count_bucket),m_variants AS (  SELECT    t8.image_count_bucket AS image_count_bucket,    AVG(COALESCE(t9.variant_count, 0)) AS avg_variants_per_product    AVG(CAST(COALESCE(t9.variant_count, 0) AS NUMERIC(18,6))) AS avg_variants_per_product  FROM product_base t8  LEFT JOIN (    SELECT      t9a.product_id AS product_id,      COUNT(*) AS variant_count    FROM product_variants t9a    GROUP BY t9a.product_id  ) t9 ON t9.product_id = t8.product_id  GROUP BY t8.image_count_bucket)SELECT  t10.image_count_bucket AS image_count_bucket,  COALESCE(t11.product_count, 0) AS product_count,  ROUND(COALESCE(t12.avg_days_since_created, 0), 1) AS avg_days_since_created,  ROUND(COALESCE(t13.pct_status_draft, 0), 1) AS pct_status_draft,  ROUND(COALESCE(t13.pct_status_archived, 0), 1) AS pct_status_archived,  ROUND(COALESCE(t13.pct_status_active, 0), 1) AS pct_status_active,  ROUND(COALESCE(t14.pct_apparel_footwear_industry, 0), 1) AS pct_apparel_footwear_industry,  ROUND(COALESCE(t14.pct_b2b_industry, 0), 1) AS pct_b2b_industry,  ROUND(COALESCE(t15.avg_variants_per_product, 0), 1) AS avg_variants_per_product  COALESCE(t12.avg_days_since_created, 0) AS avg_days_since_created,  COALESCE(t13.pct_status_draft, 0) AS pct_status_draft,  COALESCE(t13.pct_status_archived, 0) AS pct_status_archived,  COALESCE(t13.pct_status_active, 0) AS pct_status_active,  COALESCE(t14.pct_apparel_footwear_industry, 0) AS pct_apparel_footwear_industry,  COALESCE(t14.pct_b2b_industry, 0) AS pct_b2b_industry,  COALESCE(t15.avg_variants_per_product, 0) AS avg_variants_per_productFROM dim_bucket t10LEFT JOIN m_count t11 ON t11.image_count_bucket = t10.image_count_bucketLEFT JOIN m_age t12 ON t12.image_count_bucket = t10.image_count_bucketLEFT JOIN m_status t13 ON t13.image_count_bucket = t10.image_count_bucketLEFT JOIN m_industry t14 ON t14.image_count_bucket = t10.image_count_bucketLEFT JOIN m_variants t15 ON t15.image_count_bucket = t10.image_count_bucketORDER BY  CASE t10.image_count_bucket    WHEN '0' THEN 1    WHEN '1' THEN 2    WHEN '2-3' THEN 3    WHEN '4-6' THEN 4    WHEN '7+' THEN 5  END  END NULLS LAST

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

   DuckDB · Hyper · GizmoSQL · CedarDB · pg_duckdb · PostgreSQL · DataFusion · Firebolt · ClickHouse · chDB · clickhouse_brew

   WITH product_base AS (
     SELECT
       t0.id AS product_id,
       t0.tenant_id AS tenant_id,
       t0.status AS status,
       t0.created_at AS created_at,
       CASE
         WHEN COALESCE(t1.image_count, 0) = 0 THEN '0'
         WHEN COALESCE(t1.image_count, 0) = 1 THEN '1'
         WHEN COALESCE(t1.image_count, 0) BETWEEN 2 AND 3 THEN '2-3'
         WHEN COALESCE(t1.image_count, 0) BETWEEN 4 AND 6 THEN '4-6'
         ELSE '7+'
       END AS image_count_bucket
     FROM products t0
     LEFT JOIN (
       SELECT
         t1a.product_id AS product_id,
         COUNT(*) AS image_count
       FROM product_media t1a
       WHERE t1a.kind = 'image'
       GROUP BY t1a.product_id
     ) t1 ON t1.product_id = t0.id
   ),
   dim_bucket AS (
     SELECT DISTINCT t2.image_count_bucket AS image_count_bucket
     FROM product_base t2
   ),
   m_count AS (
     SELECT
       t3.image_count_bucket AS image_count_bucket,
       COUNT(*) AS product_count
     FROM product_base t3
     GROUP BY t3.image_count_bucket
   ),
   m_age AS (
     SELECT
       t4.image_count_bucket AS image_count_bucket,
       AVG(CAST(DATE '2025-12-31' - CAST(t4.created_at AS date) AS numeric(18,6))) AS avg_days_since_created
     FROM product_base t4
     GROUP BY t4.image_count_bucket
   ),
   m_status AS (
     SELECT
       t5.image_count_bucket AS image_count_bucket,
       100.0 * SUM(CASE WHEN t5.status = 'draft' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_draft,
       100.0 * SUM(CASE WHEN t5.status = 'archived' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_archived,
       100.0 * SUM(CASE WHEN t5.status = 'active' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_active
     FROM product_base t5
     GROUP BY t5.image_count_bucket
   ),
   m_industry AS (
     SELECT
       t6.image_count_bucket AS image_count_bucket,
       100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%apparel%' OR LOWER(t7.industry) LIKE '%footwear%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_apparel_footwear_industry,
       100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%office%' OR LOWER(t7.industry) LIKE '%industrial%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_b2b_industry
     FROM product_base t6
     JOIN tenants t7 ON t7.id = t6.tenant_id
     GROUP BY t6.image_count_bucket
   ),
   m_variants AS (
     SELECT
       t8.image_count_bucket AS image_count_bucket,
       AVG(CAST(COALESCE(t9.variant_count, 0) AS NUMERIC(18,6))) AS avg_variants_per_product
     FROM product_base t8
     LEFT JOIN (
       SELECT
         t9a.product_id AS product_id,
         COUNT(*) AS variant_count
       FROM product_variants t9a
       GROUP BY t9a.product_id
     ) t9 ON t9.product_id = t8.product_id
     GROUP BY t8.image_count_bucket
   )
   SELECT
     t10.image_count_bucket AS image_count_bucket,
     COALESCE(t11.product_count, 0) AS product_count,
     COALESCE(t12.avg_days_since_created, 0) AS avg_days_since_created,
     COALESCE(t13.pct_status_draft, 0) AS pct_status_draft,
     COALESCE(t13.pct_status_archived, 0) AS pct_status_archived,
     COALESCE(t13.pct_status_active, 0) AS pct_status_active,
     COALESCE(t14.pct_apparel_footwear_industry, 0) AS pct_apparel_footwear_industry,
     COALESCE(t14.pct_b2b_industry, 0) AS pct_b2b_industry,
     COALESCE(t15.avg_variants_per_product, 0) AS avg_variants_per_product
   FROM dim_bucket t10
   LEFT JOIN m_count t11 ON t11.image_count_bucket = t10.image_count_bucket
   LEFT JOIN m_age t12 ON t12.image_count_bucket = t10.image_count_bucket
   LEFT JOIN m_status t13 ON t13.image_count_bucket = t10.image_count_bucket
   LEFT JOIN m_industry t14 ON t14.image_count_bucket = t10.image_count_bucket
   LEFT JOIN m_variants t15 ON t15.image_count_bucket = t10.image_count_bucket
   ORDER BY
     CASE t10.image_count_bucket
       WHEN '0' THEN 1
       WHEN '1' THEN 2
       WHEN '2-3' THEN 3
       WHEN '4-6' THEN 4
       WHEN '7+' THEN 5
     END NULLS LAST

   Scale factor 1000, c8a.24xlarge, after the last load. q351 on the start page, with every configuration.

7. 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, 7 turns

   • The request, as Bea received it

     Report request from Zara Ahmed (analyst for the Head of Merchandising):
     
     Title: Image-Count Buckets: Product Age, Status, and Vertical Mix
     Question: Are zero-image product listings systematically different from one-plus-image listings (older, more likely draft/archived, or concentrated in certain verticals), rather than the image count itself being the driver?
     Grain: one row per product image-count bucket (0, 1, 2-3, 4-6, 7+), across ALL products regardless of status (not just active ones, to test the status hypothesis)
     Columns:
     - image_count_bucket
     - product_count
     - avg_days_since_created
     - pct_status_draft
     - pct_status_archived
     - pct_status_active
     - pct_apparel_footwear_industry
     - pct_b2b_industry (Office Supplies + Industrial Supplies combined)
     - avg_variants_per_product
     Details: Tables: products (all statuses, all deleted_at values included this time), product_media (kind='image', counted per product to form the bucket), tenants (for industry), product_variants. avg_days_since_created = average of (2025-12-31 minus products.created_at) in days. pct_status_draft/archived/active = share of products in that bucket with that products.status value. pct_apparel_footwear_industry and pct_b2b_industry = share of products in the bucket belonging to tenants with that industry. avg_variants_per_product = average count of product_variants (any status) per product in the bucket. Round averages to 1 decimal, percentages to 1 decimal. Order by image_count_bucket in natural ascending order (0, 1, 2-3, 4-6, 7+).
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Bea calls dry_run

     WITH product_base AS (
       SELECT
         t0.id AS product_id,
         t0.tenant_id AS tenant_id,
         t0.status AS status,
         t0.created_at AS created_at,
         CASE
           WHEN COALESCE(t1.image_count, 0) = 0 THEN '0'
           WHEN COALESCE(t1.image_count, 0) = 1 THEN '1'
           WHEN COALESCE(t1.image_count, 0) BETWEEN 2 AND 3 THEN '2-3'
           WHEN COALESCE(t1.image_count, 0) BETWEEN 4 AND 6 THEN '4-6'
           ELSE '7+'
         END AS image_count_bucket
       FROM products t0
       LEFT JOIN (
         SELECT
           t1a.product_id AS product_id,
           COUNT(*) AS image_count
         FROM product_media t1a
         WHERE t1a.kind = 'image'
         GROUP BY t1a.product_id
       ) t1 ON t1.product_id = t0.id
     ),
     dim_bucket AS (
       SELECT DISTINCT t2.image_count_bucket AS image_count_bucket
       FROM product_base t2
     ),
     m_count AS (
       SELECT
         t3.image_count_bucket AS image_count_bucket,
         COUNT(*) AS product_count
       FROM product_base t3
       GROUP BY t3.image_count_bucket
     ),
     m_age AS (
       SELECT
         t4.image_count_bucket AS image_count_bucket,
         AVG(CAST(DATE '2025-12-31' - CAST(t4.created_at AS date) AS numeric)) AS avg_days_since_created
       FROM product_base t4
       GROUP BY t4.image_count_bucket
     ),
     m_status AS (
       SELECT
         t5.image_count_bucket AS image_count_bucket,
         100.0 * SUM(CASE WHEN t5.status = 'draft' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_draft,
         100.0 * SUM(CASE WHEN t5.status = 'archived' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_archived,
         100.0 * SUM(CASE WHEN t5.status = 'active' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_active
       FROM product_base t5
       GROUP BY t5.image_count_bucket
     ),
     m_industry AS (
       SELECT
         t6.image_count_bucket AS image_count_bucket,
         100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%apparel%' OR LOWER(t7.industry) LIKE '%footwear%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_apparel_footwear_industry,
         100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%office%' OR LOWER(t7.industry) LIKE '%industrial%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_b2b_industry
       FROM product_base t6
       JOIN tenants t7 ON t7.id = t6.tenant_id
       GROUP BY t6.image_count_bucket
     ),
     m_variants AS (
       SELECT
         t8.image_count_bucket AS image_count_bucket,
         AVG(COALESCE(t9.variant_count, 0)) AS avg_variants_per_product
       FROM product_base t8
       LEFT JOIN (
         SELECT
           t9a.product_id AS product_id,
           COUNT(*) AS variant_count
         FROM product_variants t9a
         GROUP BY t9a.product_id
       ) t9 ON t9.product_id = t8.product_id
       GROUP BY t8.image_count_bucket
     )
     SELECT
       t10.image_count_bucket AS image_count_bucket,
       COALESCE(t11.product_count, 0) AS product_count,
       ROUND(COALESCE(t12.avg_days_since_created, 0), 1) AS avg_days_since_created,
       ROUND(COALESCE(t13.pct_status_draft, 0), 1) AS pct_status_draft,
       ROUND(COALESCE(t13.pct_status_archived, 0), 1) AS pct_status_archived,
       ROUND(COALESCE(t13.pct_status_active, 0), 1) AS pct_status_active,
       ROUND(COALESCE(t14.pct_apparel_footwear_industry, 0), 1) AS pct_apparel_footwear_industry,
       ROUND(COALESCE(t14.pct_b2b_industry, 0), 1) AS pct_b2b_industry,
       ROUND(COALESCE(t15.avg_variants_per_product, 0), 1) AS avg_variants_per_product
     FROM dim_bucket t10
     LEFT JOIN m_count t11 ON t11.image_count_bucket = t10.image_count_bucket
     LEFT JOIN m_age t12 ON t12.image_count_bucket = t10.image_count_bucket
     LEFT JOIN m_status t13 ON t13.image_count_bucket = t10.image_count_bucket
     LEFT JOIN m_industry t14 ON t14.image_count_bucket = t10.image_count_bucket
     LEFT JOIN m_variants t15 ON t15.image_count_bucket = t10.image_count_bucket
     ORDER BY
       CASE t10.image_count_bucket
         WHEN '0' THEN 1
         WHEN '1' THEN 2
         WHEN '2-3' THEN 3
         WHEN '4-6' THEN 4
         WHEN '7+' THEN 5
       END

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: image_count_bucket, product_count, avg_days_since_created, pct_status_draft, pct_status_archived, pct_status_active, pct_apparel_footwear_industry, pct_b2b_industry, avg_variants_per_product

   • BeaQuery compiles cleanly with the expected columns. Submitting now.
   • Bea calls submit

     WITH product_base AS (
       SELECT
         t0.id AS product_id,
         t0.tenant_id AS tenant_id,
         t0.status AS status,
         t0.created_at AS created_at,
         CASE
           WHEN COALESCE(t1.image_count, 0) = 0 THEN '0'
           WHEN COALESCE(t1.image_count, 0) = 1 THEN '1'
           WHEN COALESCE(t1.image_count, 0) BETWEEN 2 AND 3 THEN '2-3'
           WHEN COALESCE(t1.image_count, 0) BETWEEN 4 AND 6 THEN '4-6'
           ELSE '7+'
         END AS image_count_bucket
       FROM products t0
       LEFT JOIN (
         SELECT
           t1a.product_id AS product_id,
           COUNT(*) AS image_count
         FROM product_media t1a
         WHERE t1a.kind = 'image'
         GROUP BY t1a.product_id
       ) t1 ON t1.product_id = t0.id
     ),
     dim_bucket AS (
       SELECT DISTINCT t2.image_count_bucket AS image_count_bucket
       FROM product_base t2
     ),
     m_count AS (
       SELECT
         t3.image_count_bucket AS image_count_bucket,
         COUNT(*) AS product_count
       FROM product_base t3
       GROUP BY t3.image_count_bucket
     ),
     m_age AS (
       SELECT
         t4.image_count_bucket AS image_count_bucket,
         AVG(CAST(DATE '2025-12-31' - CAST(t4.created_at AS date) AS numeric)) AS avg_days_since_created
       FROM product_base t4
       GROUP BY t4.image_count_bucket
     ),
     m_status AS (
       SELECT
         t5.image_count_bucket AS image_count_bucket,
         100.0 * SUM(CASE WHEN t5.status = 'draft' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_draft,
         100.0 * SUM(CASE WHEN t5.status = 'archived' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_archived,
         100.0 * SUM(CASE WHEN t5.status = 'active' THEN 1 ELSE 0 END) / COUNT(*) AS pct_status_active
       FROM product_base t5
       GROUP BY t5.image_count_bucket
     ),
     m_industry AS (
       SELECT
         t6.image_count_bucket AS image_count_bucket,
         100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%apparel%' OR LOWER(t7.industry) LIKE '%footwear%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_apparel_footwear_industry,
         100.0 * SUM(CASE WHEN LOWER(t7.industry) LIKE '%office%' OR LOWER(t7.industry) LIKE '%industrial%' THEN 1 ELSE 0 END) / COUNT(*) AS pct_b2b_industry
       FROM product_base t6
       JOIN tenants t7 ON t7.id = t6.tenant_id
       GROUP BY t6.image_count_bucket
     ),
     m_variants AS (
       SELECT
         t8.image_count_bucket AS image_count_bucket,
         AVG(COALESCE(t9.variant_count, 0)) AS avg_variants_per_product
       FROM product_base t8
       LEFT JOIN (
         SELECT
           t9a.product_id AS product_id,
           COUNT(*) AS variant_count
         FROM product_variants t9a
         GROUP BY t9a.product_id
       ) t9 ON t9.product_id = t8.product_id
       GROUP BY t8.image_count_bucket
     )
     SELECT
       t10.image_count_bucket AS image_count_bucket,
       COALESCE(t11.product_count, 0) AS product_count,
       ROUND(COALESCE(t12.avg_days_since_created, 0), 1) AS avg_days_since_created,
       ROUND(COALESCE(t13.pct_status_draft, 0), 1) AS pct_status_draft,
       ROUND(COALESCE(t13.pct_status_archived, 0), 1) AS pct_status_archived,
       ROUND(COALESCE(t13.pct_status_active, 0), 1) AS pct_status_active,
       ROUND(COALESCE(t14.pct_apparel_footwear_industry, 0), 1) AS pct_apparel_footwear_industry,
       ROUND(COALESCE(t14.pct_b2b_industry, 0), 1) AS pct_b2b_industry,
       ROUND(COALESCE(t15.avg_variants_per_product, 0), 1) AS avg_variants_per_product
     FROM dim_bucket t10
     LEFT JOIN m_count t11 ON t11.image_count_bucket = t10.image_count_bucket
     LEFT JOIN m_age t12 ON t12.image_count_bucket = t10.image_count_bucket
     LEFT JOIN m_status t13 ON t13.image_count_bucket = t10.image_count_bucket
     LEFT JOIN m_industry t14 ON t14.image_count_bucket = t10.image_count_bucket
     LEFT JOIN m_variants t15 ON t15.image_count_bucket = t10.image_count_bucket
     ORDER BY
       CASE t10.image_count_bucket
         WHEN '0' THEN 1
         WHEN '1' THEN 2
         WHEN '2-3' THEN 3
         WHEN '4-6' THEN 4
         WHEN '7+' THEN 5
       END

   • The warehouse answers

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

   • BeaSubmitted. The report builds five image-count buckets (0, 1, 2-3, 4-6, 7+) across all products regardless of status/deletion, with product age, status mix, industry mix, and average variants per product computed per bucket. One caveat noted to the analyst: since `tenants.industry` is free-text rather than a coded field, the apparel/footwear and B2B industry shares are derived via keyword matching, which should be verified against actual values if results look off.

   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 (280 KB) · Zara Ahmed's transcript (894 KB) · Freya Lindgren's transcript (260 KB) · the night's plan (6 KB) · the night's reports (16 KB) · the week's memo (4 KB)