Queries · q137 · Head of Merchandising · week 1, night 1

Catalog Richness vs Sales Performance by Image Count Bucket

“Do products with more images, a proxy for richer catalog content, see higher engagement and sales than sparser listings?”

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 1

   Subject: My first-week priorities — catalog questions

   Hi Zara,

   Great to have you helping me get oriented this week. I know slots are tight, so here's what I most want to understand first. Pick the ones you think will give us the clearest picture given the report budget — happy to defer the rest to next week.

   1. How healthy is our catalog overall, and where is it thin? I'd like a sense of how many products and variants each merchant is actually running, which ones feel well-built out (good imagery, descriptions, variants) versus sparse, and whether that varies a lot across merchants or verticals. I need this to know where to focus my attention first.
   2. Does catalog quality actually move sales? I'm curious whether products with richer content — more images, more reviews, better ratings — sell noticeably better than ones without. I want to build the case (to myself and to merchants) for why investing in catalog presentation is worth it.
   3. What do customers think of the products, and where are the trouble spots? I'd like to see how reviews and ratings are trending, which products or categories are getting poor feedback, and whether there are products with surprisingly few or no reviews despite decent sales. This tells me where to nudge merchants on quality or listing improvements.
   4. Are we using collections and bundles well? I want to know how much merchants are leaning on curated collections and bundles versus just flat product listings, and whether those grouped presentations correlate with better performance. This will shape whether I push merchandising tools harder in my conversations with merchants.
   5. How are our top merchants' catalogs structured compared to the long tail? Since a handful of merchants drive most of the volume, I'd like to understand if their catalogs look meaningfully different (size, categorization, media richness) from smaller merchants — it'll help me figure out what "good" looks like and what advice to bring to the smaller ones.

   Let me know what's realistic to cover by Friday, and flag if any of these need to be split into smaller questions on your end.

   Thanks, Freya

   Show the whole note

2. Zara Ahmed plans the night

   The analyst · week 1, night 1, request 2 of 3

   Freya has 5 broad asks; I am splitting them into clean one-grain reports rather than covering everything at once. Tonight I want foundational views that tell me where to dig deeper on nights 2 to 4: (1) a per-merchant catalog profile that speaks to Q1 catalog health and size, and seeds Q5 top vs long tail since it is ordered by revenue; (2) a richness vs sales bucket report that gives a first, non survivorship biased read on Q2 using image count as the richness proxy; (3) a monthly review and rating trend to start on Q3. I am deliberately leaving collections and bundles (Q4) and the detailed top vs tail structural comparison (Q5) for later nights once I see what these three reports reveal. Each report is a single grain and stays under 12 columns.

   Read the whole plan
   Catalog Richness vs Sales Performance by Image Count Bucket

   Question

   Do products with more images, a proxy for richer catalog content, see higher engagement and sales than sparser listings?

   One row per

   product image-count bucket: 0, 1, 2 to 3, 4 to 6, 7 plus

   Columns

   • image_count_bucket
   • product_count
   • pct_with_description
   • avg_reviews_per_product
   • avg_rating
   • avg_units_sold_per_product_last_12mo
   • avg_revenue_per_product_eur_last_12mo

3. Omar 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_images AS (
     SELECT product_id, COUNT(*) AS image_count
     FROM product_media
     WHERE kind = 'image'
     GROUP BY product_id
   ),
   base AS (
     SELECT p.id AS product_id,
            COALESCE(pi.image_count,0) AS image_count,
            CASE WHEN p.description IS NOT NULL AND p.description <> '' THEN 1 ELSE 0 END AS has_description
     FROM products p
     LEFT JOIN product_images pi ON pi.product_id = p.id
     WHERE p.status = 'active' AND p.deleted_at IS NULL
   ),
   bucketed AS (
     SELECT product_id, has_description,
       CASE
         WHEN image_count = 0 THEN '0'
         WHEN image_count = 1 THEN '1'
         WHEN image_count BETWEEN 2 AND 3 THEN '2-3'
         WHEN image_count BETWEEN 4 AND 6 THEN '4-6'
         ELSE '7+'
       END AS image_count_bucket,
       CASE
         WHEN image_count = 0 THEN 0
         WHEN image_count = 1 THEN 1
         WHEN image_count BETWEEN 2 AND 3 THEN 2
         WHEN image_count BETWEEN 4 AND 6 THEN 3
         ELSE 4
       END AS bucket_order
     FROM base
   ),
   reviews AS (
     SELECT product_id, COUNT(*) AS review_count, AVG(rating) AS avg_rating
     FROM product_reviews
     GROUP BY product_id
   ),
   sales AS (
     SELECT pv.product_id,
            SUM(oi.quantity) AS units_sold,
            SUM(oi.line_total / er.rate) AS revenue_eur
     FROM order_items oi
     JOIN orders o ON o.id = oi.order_id
     JOIN product_variants pv ON pv.id = oi.product_variant_id
     JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = oi.currency_code AND er.as_of = DATE(o.placed_at)
     WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'
       AND o.status <> 'cancelled'
     GROUP BY pv.product_id
   )
   SELECT
     b.image_count_bucket,
     COUNT(*) AS product_count,
     ROUND(100.0 * SUM(b.has_description) / COUNT(*), 2) AS pct_with_description,
     ROUND(AVG(COALESCE(r.review_count,0)), 2) AS avg_reviews_per_product,
     ROUND(AVG(r.avg_rating), 2) AS avg_rating,
     ROUND(AVG(COALESCE(s.units_sold,0)), 2) AS avg_units_sold_per_product_last_12mo,
     ROUND(AVG(COALESCE(s.revenue_eur,0))) AS avg_revenue_per_product_eur_last_12mo
   FROM bucketed b
   LEFT JOIN reviews r ON r.product_id = b.product_id
   LEFT JOIN sales s ON s.product_id = b.product_id
   GROUP BY b.image_count_bucket, b.bucket_order
   ORDER BY b.bucket_order

   “Query compiles and returns the expected columns. Submitting.”

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

   “Submitted. Summary of the approach: - Grain: one row per image-count bucket (0, 1, 2‑3, 4‑6, 7+), built from product_media (kind='image') counts joined to active, non-deleted products. - Content richness: pct_with_description from non-null/non-empty products.description. - Engagement: avg_reviews_per_product and avg_rating from product_reviews (rating averaged per product first, then across products in the bucket). - Sales: units and EUR revenue from order_items joined through product_variants to products, restricted to orders.placed_at in 2025 and status ≠ cancelled, converted to EUR via exchange_rates (EUR→line currency rate on the order's placed date), averaged per product in the bucket (zero for products with no sales). - Noted in the submit note that products/ratings with no reviews or no 2025 sales are included as 0 in the per-product averages, which is the standard way to show the full richness-vs-performance comparison across all active products.”

4. The report comes back overnight

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

   image_count_bucket | product_count | pct_with_description | avg_reviews_per_product | avg_rating | avg_units_sold_per_product_last_12mo | avg_revenue_per_product_eur_last_12mo
   -------------------+---------------+----------------------+-------------------------+------------+--------------------------------------+--------------------------------------
   0                  |            85 |                97.65 |                     0.0 |       NULL |                                 1.14 |                                  62.0
   1                  |           800 |                70.75 |                    0.45 |       3.83 |                                 3.77 |                                 265.0
   2-3                |          3987 |                 70.3 |                     0.4 |       3.82 |                                 3.32 |                                 183.0
   4-6                |          3183 |                70.81 |                    0.42 |       3.92 |                                  3.5 |                                 192.0
   7+                 |           259 |                71.04 |                    0.31 |       3.65 |                                 3.32 |                                 521.0
   (5 rows)

5. What came of it

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

   Later queries that build on this report:

   • q139Image-Richness vs Sales Within Apparel & Footwear
   • q351Image-Count Buckets: Product Age, Status, and Vertical Mix

6. It becomes benchmark query q137

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

   • spellingdate cast
   • 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; AVG over an integer computed as a decimal: AVG(CAST(x AS NUMERIC(18,6)))
   • numericsdocs/NUMERICS.md: x / exchange rate cast to NUMERIC(18,6)
   • made explicita selected t.col gets AS col, its standard name made explicit (2×)
   • 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_images AS (  SELECT product_id, COUNT(*) AS image_count  FROM product_media  WHERE kind = 'image'  GROUP BY product_id),base AS (  SELECT p.id AS product_id,         COALESCE(pi.image_count,0) AS image_count,         CASE WHEN p.description IS NOT NULL AND p.description <> '' THEN 1 ELSE 0 END AS has_description  FROM products p  LEFT JOIN product_images pi ON pi.product_id = p.id  WHERE p.status = 'active' AND p.deleted_at IS NULL),bucketed AS (  SELECT product_id, has_description,    CASE      WHEN image_count = 0 THEN '0'      WHEN image_count = 1 THEN '1'      WHEN image_count BETWEEN 2 AND 3 THEN '2-3'      WHEN image_count BETWEEN 4 AND 6 THEN '4-6'      ELSE '7+'    END AS image_count_bucket,    CASE      WHEN image_count = 0 THEN 0      WHEN image_count = 1 THEN 1      WHEN image_count BETWEEN 2 AND 3 THEN 2      WHEN image_count BETWEEN 4 AND 6 THEN 3      ELSE 4    END AS bucket_order  FROM base),reviews AS (  SELECT product_id, COUNT(*) AS review_count, AVG(rating) AS avg_rating  SELECT product_id, COUNT(*) AS review_count, AVG(CAST(rating AS NUMERIC(18,6))) AS avg_rating  FROM product_reviews  GROUP BY product_id),sales AS (  SELECT pv.product_id,  SELECT pv.product_id AS product_id,         SUM(oi.quantity) AS units_sold,         SUM(oi.line_total / er.rate) AS revenue_eur         SUM(CAST(oi.line_total / er.rate AS NUMERIC(18,6))) AS revenue_eur  FROM order_items oi  JOIN orders o ON o.id = oi.order_id  JOIN product_variants pv ON pv.id = oi.product_variant_id  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = oi.currency_code AND er.as_of = DATE(o.placed_at)  JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = oi.currency_code AND er.as_of = CAST(o.placed_at AS DATE)  WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'    AND o.status <> 'cancelled'  GROUP BY pv.product_id)SELECT  b.image_count_bucket,  b.image_count_bucket AS image_count_bucket,  COUNT(*) AS product_count,  ROUND(100.0 * SUM(b.has_description) / COUNT(*), 2) AS pct_with_description,  ROUND(AVG(COALESCE(r.review_count,0)), 2) AS avg_reviews_per_product,  ROUND(AVG(r.avg_rating), 2) AS avg_rating,  ROUND(AVG(COALESCE(s.units_sold,0)), 2) AS avg_units_sold_per_product_last_12mo,  ROUND(AVG(COALESCE(s.revenue_eur,0))) AS avg_revenue_per_product_eur_last_12mo  100.0 * SUM(b.has_description) / COUNT(*) AS pct_with_description,  AVG(CAST(COALESCE(r.review_count,0) AS NUMERIC(18,6))) AS avg_reviews_per_product,  AVG(r.avg_rating) AS avg_rating,  AVG(CAST(COALESCE(s.units_sold,0) AS NUMERIC(18,6))) AS avg_units_sold_per_product_last_12mo,  AVG(COALESCE(s.revenue_eur,0)) AS avg_revenue_per_product_eur_last_12moFROM bucketed bLEFT JOIN reviews r ON r.product_id = b.product_idLEFT JOIN sales s ON s.product_id = b.product_idGROUP BY b.image_count_bucket, b.bucket_orderORDER BY b.bucket_orderORDER BY b.bucket_order 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_images AS (
     SELECT product_id, COUNT(*) AS image_count
     FROM product_media
     WHERE kind = 'image'
     GROUP BY product_id
   ),
   base AS (
     SELECT p.id AS product_id,
            COALESCE(pi.image_count,0) AS image_count,
            CASE WHEN p.description IS NOT NULL AND p.description <> '' THEN 1 ELSE 0 END AS has_description
     FROM products p
     LEFT JOIN product_images pi ON pi.product_id = p.id
     WHERE p.status = 'active' AND p.deleted_at IS NULL
   ),
   bucketed AS (
     SELECT product_id, has_description,
       CASE
         WHEN image_count = 0 THEN '0'
         WHEN image_count = 1 THEN '1'
         WHEN image_count BETWEEN 2 AND 3 THEN '2-3'
         WHEN image_count BETWEEN 4 AND 6 THEN '4-6'
         ELSE '7+'
       END AS image_count_bucket,
       CASE
         WHEN image_count = 0 THEN 0
         WHEN image_count = 1 THEN 1
         WHEN image_count BETWEEN 2 AND 3 THEN 2
         WHEN image_count BETWEEN 4 AND 6 THEN 3
         ELSE 4
       END AS bucket_order
     FROM base
   ),
   reviews AS (
     SELECT product_id, COUNT(*) AS review_count, AVG(CAST(rating AS NUMERIC(18,6))) AS avg_rating
     FROM product_reviews
     GROUP BY product_id
   ),
   sales AS (
     SELECT pv.product_id AS product_id,
            SUM(oi.quantity) AS units_sold,
            SUM(CAST(oi.line_total / er.rate AS NUMERIC(18,6))) AS revenue_eur
     FROM order_items oi
     JOIN orders o ON o.id = oi.order_id
     JOIN product_variants pv ON pv.id = oi.product_variant_id
     JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = oi.currency_code AND er.as_of = CAST(o.placed_at AS DATE)
     WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'
       AND o.status <> 'cancelled'
     GROUP BY pv.product_id
   )
   SELECT
     b.image_count_bucket AS image_count_bucket,
     COUNT(*) AS product_count,
     100.0 * SUM(b.has_description) / COUNT(*) AS pct_with_description,
     AVG(CAST(COALESCE(r.review_count,0) AS NUMERIC(18,6))) AS avg_reviews_per_product,
     AVG(r.avg_rating) AS avg_rating,
     AVG(CAST(COALESCE(s.units_sold,0) AS NUMERIC(18,6))) AS avg_units_sold_per_product_last_12mo,
     AVG(COALESCE(s.revenue_eur,0)) AS avg_revenue_per_product_eur_last_12mo
   FROM bucketed b
   LEFT JOIN reviews r ON r.product_id = b.product_id
   LEFT JOIN sales s ON s.product_id = b.product_id
   GROUP BY b.image_count_bucket, b.bucket_order
   ORDER BY b.bucket_order NULLS LAST

   • Firebolt{ "errors": [ { "description": "Line 41, Column 33: Operations between decimals with different precision and scale is not supported at the moment. Explicitly cast one of the decimals to other decimal precision and scale." } ], "query": { "query_id": "d1c9135e-3469-40d7-a8e2-814c5f719bfd", "query_lab
   • ClickHouseCode: 69. DB::Exception: Decimal result's scale is less than argument's one: while executing function divide on arguments __table9.line_total Decimal(14, 4) Decimal64(size = 0), __table12.rate Decimal(12, 6) Decimal64(size = 0). (ARGUMENT_OUT_OF_BOUND)

   Scale factor 1000, c8a.24xlarge, after the last load. q137 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

   Omar's whole session, 7 turns

   • The request, as Omar received it

     Report request from Zara Ahmed (analyst for the Head of Merchandising):
     
     Title: Catalog Richness vs Sales Performance by Image Count Bucket
     Question: Do products with more images, a proxy for richer catalog content, see higher engagement and sales than sparser listings?
     Grain: one row per product image-count bucket: 0, 1, 2 to 3, 4 to 6, 7 plus
     Columns:
     - image_count_bucket
     - product_count
     - pct_with_description
     - avg_reviews_per_product
     - avg_rating
     - avg_units_sold_per_product_last_12mo
     - avg_revenue_per_product_eur_last_12mo
     Details: Tables: products, product_media (kind = image, counted per product_id to form the bucket), product_reviews, order_items, product_variants, orders, exchange_rates. Scope to products with status = active and deleted_at is null across all tenants. Sales measures from order_items joined to orders where placed_at is between 2025-01-01 and 2025-12-31 and orders.status is not cancelled; units_sold = sum of order_items.quantity; revenue converted to EUR per order line using exchange_rates (base_code EUR, quote_code = currency_code, as_of = order placed_at date), summed then averaged per product within the bucket. Round rates and averages to 2 decimals, revenue to whole EUR. Order by image_count_bucket in the natural ascending order listed (not alphabetical).
     
     Check your query with the warehouse's dry_run tool, then hand it in with submit.

   • Omar calls dry_run

     WITH product_images AS (
       SELECT product_id, COUNT(*) AS image_count
       FROM product_media
       WHERE kind = 'image'
       GROUP BY product_id
     ),
     base AS (
       SELECT p.id AS product_id,
              COALESCE(pi.image_count,0) AS image_count,
              CASE WHEN p.description IS NOT NULL AND p.description <> '' THEN 1 ELSE 0 END AS has_description
       FROM products p
       LEFT JOIN product_images pi ON pi.product_id = p.id
       WHERE p.status = 'active' AND p.deleted_at IS NULL
     ),
     bucketed AS (
       SELECT product_id, has_description,
         CASE
           WHEN image_count = 0 THEN '0'
           WHEN image_count = 1 THEN '1'
           WHEN image_count BETWEEN 2 AND 3 THEN '2-3'
           WHEN image_count BETWEEN 4 AND 6 THEN '4-6'
           ELSE '7+'
         END AS image_count_bucket,
         CASE
           WHEN image_count = 0 THEN 0
           WHEN image_count = 1 THEN 1
           WHEN image_count BETWEEN 2 AND 3 THEN 2
           WHEN image_count BETWEEN 4 AND 6 THEN 3
           ELSE 4
         END AS bucket_order
       FROM base
     ),
     reviews AS (
       SELECT product_id, COUNT(*) AS review_count, AVG(rating) AS avg_rating
       FROM product_reviews
       GROUP BY product_id
     ),
     sales AS (
       SELECT pv.product_id,
              SUM(oi.quantity) AS units_sold,
              SUM(oi.line_total / er.rate) AS revenue_eur
       FROM order_items oi
       JOIN orders o ON o.id = oi.order_id
       JOIN product_variants pv ON pv.id = oi.product_variant_id
       JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = oi.currency_code AND er.as_of = DATE(o.placed_at)
       WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'
         AND o.status <> 'cancelled'
       GROUP BY pv.product_id
     )
     SELECT
       b.image_count_bucket,
       COUNT(*) AS product_count,
       ROUND(100.0 * SUM(b.has_description) / COUNT(*), 2) AS pct_with_description,
       ROUND(AVG(COALESCE(r.review_count,0)), 2) AS avg_reviews_per_product,
       ROUND(AVG(r.avg_rating), 2) AS avg_rating,
       ROUND(AVG(COALESCE(s.units_sold,0)), 2) AS avg_units_sold_per_product_last_12mo,
       ROUND(AVG(COALESCE(s.revenue_eur,0))) AS avg_revenue_per_product_eur_last_12mo
     FROM bucketed b
     LEFT JOIN reviews r ON r.product_id = b.product_id
     LEFT JOIN sales s ON s.product_id = b.product_id
     GROUP BY b.image_count_bucket, b.bucket_order
     ORDER BY b.bucket_order

   • The warehouse answers

     STATUS: COMPILES (dry run on the empty schema, no data read)
     COLUMNS: image_count_bucket, product_count, pct_with_description, avg_reviews_per_product, avg_rating, avg_units_sold_per_product_last_12mo, avg_revenue_per_product_eur_last_12mo

   • OmarQuery compiles and returns the expected columns. Submitting.
   • Omar calls submit

     WITH product_images AS (
       SELECT product_id, COUNT(*) AS image_count
       FROM product_media
       WHERE kind = 'image'
       GROUP BY product_id
     ),
     base AS (
       SELECT p.id AS product_id,
              COALESCE(pi.image_count,0) AS image_count,
              CASE WHEN p.description IS NOT NULL AND p.description <> '' THEN 1 ELSE 0 END AS has_description
       FROM products p
       LEFT JOIN product_images pi ON pi.product_id = p.id
       WHERE p.status = 'active' AND p.deleted_at IS NULL
     ),
     bucketed AS (
       SELECT product_id, has_description,
         CASE
           WHEN image_count = 0 THEN '0'
           WHEN image_count = 1 THEN '1'
           WHEN image_count BETWEEN 2 AND 3 THEN '2-3'
           WHEN image_count BETWEEN 4 AND 6 THEN '4-6'
           ELSE '7+'
         END AS image_count_bucket,
         CASE
           WHEN image_count = 0 THEN 0
           WHEN image_count = 1 THEN 1
           WHEN image_count BETWEEN 2 AND 3 THEN 2
           WHEN image_count BETWEEN 4 AND 6 THEN 3
           ELSE 4
         END AS bucket_order
       FROM base
     ),
     reviews AS (
       SELECT product_id, COUNT(*) AS review_count, AVG(rating) AS avg_rating
       FROM product_reviews
       GROUP BY product_id
     ),
     sales AS (
       SELECT pv.product_id,
              SUM(oi.quantity) AS units_sold,
              SUM(oi.line_total / er.rate) AS revenue_eur
       FROM order_items oi
       JOIN orders o ON o.id = oi.order_id
       JOIN product_variants pv ON pv.id = oi.product_variant_id
       JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = oi.currency_code AND er.as_of = DATE(o.placed_at)
       WHERE o.placed_at >= DATE '2025-01-01' AND o.placed_at < DATE '2026-01-01'
         AND o.status <> 'cancelled'
       GROUP BY pv.product_id
     )
     SELECT
       b.image_count_bucket,
       COUNT(*) AS product_count,
       ROUND(100.0 * SUM(b.has_description) / COUNT(*), 2) AS pct_with_description,
       ROUND(AVG(COALESCE(r.review_count,0)), 2) AS avg_reviews_per_product,
       ROUND(AVG(r.avg_rating), 2) AS avg_rating,
       ROUND(AVG(COALESCE(s.units_sold,0)), 2) AS avg_units_sold_per_product_last_12mo,
       ROUND(AVG(COALESCE(s.revenue_eur,0))) AS avg_revenue_per_product_eur_last_12mo
     FROM bucketed b
     LEFT JOIN reviews r ON r.product_id = b.product_id
     LEFT JOIN sales s ON s.product_id = b.product_id
     GROUP BY b.image_count_bucket, b.bucket_order
     ORDER BY b.bucket_order

   • The warehouse answers

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

   • OmarSubmitted. Summary of the approach: - **Grain**: one row per image-count bucket (0, 1, 2‑3, 4‑6, 7+), built from `product_media` (kind='image') counts joined to active, non-deleted `products`. - **Content richness**: `pct_with_description` from non-null/non-empty `products.description`. - **Engagement**: `avg_reviews_per_product` and `avg_rating` from `product_reviews` (rating averaged per product first, then across products in the bucket). - **Sales**: units and EUR revenue from `order_items` joined through `product_variants` to `products`, restricted to `orders.placed_at` in 2025 and status ≠ cancelled, converted to EUR via `exchange_rates` (EUR→line currency rate on the order's placed date), averaged per product in the bucket (zero for products with no sales). - Noted in the submit note that products/ratings with no reviews or no 2025 sales are included as 0 in the per-product averages, which is the standard way to show the full richness-vs-performance comparison across all active products.

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

   Omar's transcript (273 KB) · Zara Ahmed's transcript (894 KB) · Freya Lindgren's transcript (260 KB) · the night's plan (4 KB) · the night's reports (20 KB) · the week's memo (3 KB)