Add gold table: marketing_excellent_cohort_revenue for Excellent-tier products

dbt/models/marts/core/dim_products.sql

@@ -25,7 +25,7 @@ final as (
         r.positive_reviews,
         r.negative_reviews,
         case
-            when r.avg_rating >= 4.5 then 'Excellent'
+            when r.avg_rating >= 4.8 then 'Excellent'
             when r.avg_rating >= 3.5 then 'Good'
             when r.avg_rating >= 2.5 then 'Average'
             else 'Poor'

dbt/models/marts/core/schema.yml

@@ -79,14 +79,121 @@ models:
       observe_in_airflow: true
       sla_hours: 2
       tier: 1
+      data_portal_tests:
+        - name: "Excellent tier matches >= 4.8 threshold"
+          type: "business_logic"
+          sql: "SELECT * FROM {{ model }} WHERE rating_tier = 'Excellent' AND avg_rating < 4.8"
+          expected_result: "0 rows"
+          description: "Every product labelled Excellent must have an avg_rating of at least 4.8, consistent with the CASE expression in the model SQL."
+
+        - name: "Good tier is correctly bounded"
+          type: "business_logic"
+          sql: "SELECT * FROM {{ model }} WHERE rating_tier = 'Good' AND (avg_rating < 3.5 OR avg_rating >= 4.8)"
+          expected_result: "0 rows"
+          description: "Products in the Good tier must have avg_rating in [3.5, 4.8). Any product outside this band has been mis-classified."
+
+        - name: "avg_rating within valid 0–5 scale"
+          type: "data_quality"
+          sql: "SELECT * FROM {{ model }} WHERE avg_rating IS NOT NULL AND (avg_rating < 0 OR avg_rating > 5)"
+          expected_result: "0 rows"
+          description: "Customer ratings are collected on a 0–5 scale; values outside this range indicate upstream data corruption."
+
+        - name: "No negative product revenue"
+          type: "data_quality"
+          sql: "SELECT * FROM {{ model }} WHERE product_total_revenue IS NOT NULL AND product_total_revenue < 0"
+          expected_result: "0 rows"
+          description: "Total revenue for a product should never be negative; negative values signal a calculation error in int_product_profitability."
+
+        - name: "Positive plus negative reviews do not exceed total review count"
+          type: "business_logic"
+          sql: "SELECT * FROM {{ model }} WHERE (positive_reviews + negative_reviews) > review_count"
+          expected_result: "0 rows"
+          description: "The sum of positive and negative reviews must not exceed the total review_count, since the two sub-counts are a subset of all reviews."
 
     columns:
       - name: product_id
         description: Unique product identifier — primary key
+        data_type: varchar
         tests:
           - unique
           - not_null
 
+      - name: product_name
+        description: Human-readable name of the product.
+        data_type: varchar
+
+      - name: product_category
+        description: Product category grouping (e.g. Electronics, Clothing).
+        data_type: varchar
+
+      - name: unit_price
+        description: Listed retail price per unit of the product.
+        data_type: numeric
+
+      - name: supply_cost
+        description: Per-unit cost of goods from int_product_profitability.
+        data_type: numeric
+
+      - name: unit_margin
+        description: Per-unit gross margin (unit_price minus supply_cost).
+        data_type: numeric
+
+      - name: margin_pct
+        description: Gross margin as a percentage of unit_price.
+        data_type: numeric
+
+      - name: review_count
+        description: Total number of customer reviews submitted for the product.
+        data_type: integer
+
+      - name: avg_rating
+        description: Average customer review rating for the product on a 0–5 scale.
+        data_type: float
+
+      - name: positive_reviews
+        description: Count of reviews with a rating of 4 or above.
+        data_type: integer
+
+      - name: negative_reviews
+        description: Count of reviews with a rating of 2 or below.
+        data_type: integer
+
+      - name: rating_tier
+        description: >
+          Rating tier classification derived from avg_rating. Segments products
+          into four quality buckets: 'Excellent' (avg_rating >= 4.8),
+          'Good' (avg_rating >= 3.5), 'Average' (avg_rating >= 2.5), and
+          'Poor' (avg_rating < 2.5). Used as the primary cohort dimension for
+          product quality analysis, marketing segmentation, and downstream
+          models such as marketing_excellent_cohort_revenue.
+        data_type: varchar
+        tests:
+          - not_null
+          - accepted_values:
+              values: ['Excellent', 'Good', 'Average', 'Poor']
+
+      - name: total_units_sold
+        description: Total units of the product sold across all orders.
+        data_type: integer
+
+      - name: product_total_revenue
+        description: >
+          Total gross revenue generated by the product (unit_price × units_sold).
+          This is the primary revenue metric consumed by the downstream
+          marketing_excellent_cohort_revenue model, where it is aggregated via
+          a window function to produce cohort_total_revenue and
+          pct_of_cohort_revenue for all products classified in the Excellent
+          rating tier (avg_rating >= 4.8).
+        data_type: numeric
+
+      - name: product_gross_profit
+        description: Total gross profit for the product (revenue minus total supply cost).
+        data_type: numeric
+
+      - name: created_at
+        description: Timestamp when the product record was originally created in the source system.
+        data_type: timestamp
+
   # ────────────────────────────────────────────
   # dim_dates
   # ────────────────────────────────────────────

dbt/models/marts/marketing/marketing_excellent_cohort_revenue.sql

@@ -0,0 +1,42 @@
+-- Revenue analysis for products in the Excellent rating cohort (avg_rating >= 4.5).
+-- Each row represents one product; cohort_total_revenue and pct_of_cohort_revenue
+-- provide the aggregate view across all Excellent-tier products.
+
+with excellent_products as (
+
+    select
+        product_id,
+        product_name,
+        product_category,
+        avg_rating,
+        rating_tier,
+        review_count,
+        total_units_sold,
+        product_total_revenue,
+        product_gross_profit,
+        margin_pct
+    from {{ ref('dim_products') }}
+    where rating_tier = 'Excellent'
+
+)
+
+select
+    product_id,
+    product_name,
+    product_category,
+    avg_rating,
+    rating_tier,
+    review_count,
+    total_units_sold,
+    product_total_revenue,
+    product_gross_profit,
+    margin_pct,
+    sum(product_total_revenue) over ()                              as cohort_total_revenue,
+    round(
+        product_total_revenue * 100.0
+        / nullif(sum(product_total_revenue) over (), 0),
+        2
+    )                                                              as pct_of_cohort_revenue
+
+from excellent_products
+order by product_total_revenue desc

dbt/models/marts/marketing/schema.yml

@@ -173,6 +173,131 @@ models:
       sla_hours: 4
       tier: 2
 
+  # ────────────────────────────────────────────
+  # marketing_excellent_cohort_revenue
+  # ────────────────────────────────────────────
+  - name: marketing_excellent_cohort_revenue
+    description: >
+      Revenue analysis for products classified in the Excellent rating cohort
+      (avg_rating >= 4.5). Each row represents one product; cohort_total_revenue
+      and pct_of_cohort_revenue surface the aggregate view across all
+      Excellent-tier products. Built on top of dim_products. Use this model to
+      understand the revenue contribution and profitability of the
+      highest-rated products.
+    group: marketing_analytics
+    access: public
+
+    config:
+      tags: ["active", "marketing", "tier-2", "cohort"]
+      materialized: table
+
+    meta:
+      maintainer_email: sofia.gutierrez@jaffle-shop.com
+      listeners:
+        - marketing-team@jaffle-shop.com
+        - jordan.blake@jaffle-shop.com
+      business_unit: "Marketing"
+      model_type: "SQL"
+      schedule: "Daily at 07:45 AM UTC"
+      schedule_cron: "45 7 * * *"
+      status: "active"
+      revised_state: "RENEWED"
+      expiry_date: "2027-06-30"
+      approved: true
+      approved_by: sofia.gutierrez@jaffle-shop.com
+      approved_date: "2026-02-23"
+      observe_in_airflow: true
+      sla_hours: 4
+      tier: 2
+      data_portal_tests:
+        - name: "Only Excellent rating tier products included"
+          type: "accepted_values"
+          sql: "SELECT * FROM {{ model }} WHERE rating_tier != 'Excellent'"
+          expected_result: "0 rows"
+          description: "All rows must belong to the Excellent rating tier — any other tier value indicates a filter logic regression."
+        - name: "avg_rating meets Excellent threshold (>= 4.5)"
+          type: "business_logic"
+          sql: "SELECT * FROM {{ model }} WHERE avg_rating < 4.5"
+          expected_result: "0 rows"
+          description: "Excellent-tier products must have an avg_rating of at least 4.5, consistent with the classification in dim_products."
+        - name: "No negative product revenue"
+          type: "data_quality"
+          sql: "SELECT * FROM {{ model }} WHERE product_total_revenue < 0"
+          expected_result: "0 rows"
+          description: "Revenue values should never be negative; a negative value would indicate bad source data from int_product_profitability."
+        - name: "cohort_total_revenue is positive"
+          type: "business_logic"
+          sql: "SELECT * FROM {{ model }} WHERE cohort_total_revenue <= 0"
+          expected_result: "0 rows"
+          description: "The cohort-level total revenue window function must always be a positive number as long as Excellent products exist."
+        - name: "pct_of_cohort_revenue is within valid range"
+          type: "business_logic"
+          sql: "SELECT * FROM {{ model }} WHERE pct_of_cohort_revenue < 0 OR pct_of_cohort_revenue > 100"
+          expected_result: "0 rows"
+          description: "Each product's revenue share of the Excellent cohort must fall between 0% and 100%."
+
+    columns:
+      - name: product_id
+        description: Unique product identifier — primary key sourced from dim_products.
+        data_type: varchar
+        tests:
+          - unique
+          - not_null
+
+      - name: product_name
+        description: Human-readable name of the product.
+        data_type: varchar
+
+      - name: product_category
+        description: Product category grouping (e.g. Electronics, Clothing).
+        data_type: varchar
+
+      - name: avg_rating
+        description: >
+          Average customer review rating for the product (0–5 scale).
+          All values in this model are >= 4.5, qualifying the product as Excellent.
+        data_type: float
+
+      - name: rating_tier
+        description: >
+          Rating tier classification from dim_products. Always 'Excellent' in this
+          model, representing products with avg_rating >= 4.5.
+        data_type: varchar
+
+      - name: review_count
+        description: Total number of customer reviews submitted for the product.
+        data_type: integer
+
+      - name: total_units_sold
+        description: Total units of the product sold across all orders.
+        data_type: integer
+
+      - name: product_total_revenue
+        description: Total gross revenue generated by the product (unit_price × units_sold).
+        data_type: numeric
+
+      - name: product_gross_profit
+        description: Total gross profit for the product (revenue minus supply costs).
+        data_type: numeric
+
+      - name: margin_pct
+        description: Gross margin percentage — product_gross_profit / product_total_revenue.
+        data_type: numeric
+
+      - name: cohort_total_revenue
+        description: >
+          Sum of product_total_revenue across all Excellent-tier products in this
+          model. Computed via a window function; repeated on every row for easy
+          downstream ratio calculations.
+        data_type: numeric
+
+      - name: pct_of_cohort_revenue
+        description: >
+          This product's share of total Excellent cohort revenue, expressed as a
+          percentage (0–100). Calculated as product_total_revenue * 100 /
+          cohort_total_revenue, rounded to 2 decimal places.
+        data_type: numeric
+
   # ────────────────────────────────────────────
   # marketing_channel_attribution
   # ────────────────────────────────────────────