Merge commit '4cf6f91e954b770198578bcb8db5d98ac964bfed' as 'common'

2026-03-28 02:24:14 +00:00
parent 27fe957074 4cf6f91e95
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+-- =============================================================================
+-- Stat 2: Annual savings potential from cross-store price comparison
+-- Validates: "$336/year potential savings from buying the same items
+--             at the cheapest store" (launch announcement)
+--
+-- Methodology:
+--   1. For each (normalized_product_id, store_id), take the MOST RECENT
+--      regular_price within the past 90 days ("current" price).
+--   2. Keep only products observed at 2+ distinct stores.
+--   3. For each product: savings_per_purchase = avg_price - min_price across stores.
+--   4. Annualise: multiply by an assumed purchase frequency of 26x/year
+--      (~every 2 weeks for regularly purchased grocery items).
+--   5. Sum across all eligible products to get total annual savings potential.
+--
+-- Sensitivity:
+--   Change the frequency constant (26) and lookback interval (90 days) to
+--   explore how sensitive the $336 figure is to these assumptions.
+--
+-- Run against production Postgres once infrastructure is available.
+-- =============================================================================
+
+-- Step 1: most-recent price per (product, store) within the past 90 days
+WITH latest_prices AS (
+    SELECT DISTINCT ON (ph.normalized_product_id, ph.store_id)
+        ph.normalized_product_id,
+        ph.store_id,
+        s.slug                 AS store_slug,
+        ph.regular_price       AS current_price,
+        ph.observed_date
+    FROM price_history ph
+    JOIN stores s ON s.id = ph.store_id
+    WHERE ph.observed_date >= CURRENT_DATE - INTERVAL '90 days'
+      AND ph.regular_price   > 0
+    ORDER BY
+        ph.normalized_product_id,
+        ph.store_id,
+        ph.observed_date DESC
+),
+
+-- Step 2: aggregate per product — only keep products seen at 2+ stores
+product_price_spread AS (
+    SELECT
+        lp.normalized_product_id,
+        COUNT(DISTINCT lp.store_id)         AS store_count,
+        MIN(lp.current_price)               AS cheapest_price,
+        AVG(lp.current_price)               AS avg_price,
+        MAX(lp.current_price)               AS most_expensive_price,
+        MAX(lp.current_price) - MIN(lp.current_price) AS price_range
+    FROM latest_prices lp
+    GROUP BY lp.normalized_product_id
+    HAVING COUNT(DISTINCT lp.store_id) >= 2
+),
+
+-- Step 3: compute savings_per_purchase and annualise
+--   Purchase frequency assumption: 26 purchases/year per product (~every 2 weeks)
+--   This is a conservative estimate for regularly purchased grocery items.
+savings_per_product AS (
+    SELECT
+        pps.normalized_product_id,
+        np.canonical_name,
+        np.category,
+        pps.store_count,
+        pps.cheapest_price,
+        pps.avg_price,
+        pps.price_range,
+        ROUND(pps.avg_price - pps.cheapest_price, 2)             AS savings_per_purchase,
+        ROUND((pps.avg_price - pps.cheapest_price) * 26, 2)      AS annual_savings_at_26x
+    FROM product_price_spread pps
+    JOIN normalized_products np ON np.id = pps.normalized_product_id
+)
+
+-- Final summary: total annual savings potential
+SELECT
+    COUNT(*)                                         AS eligible_product_count,
+    ROUND(AVG(savings_per_purchase), 4)              AS avg_savings_per_purchase,
+    ROUND(SUM(annual_savings_at_26x), 2)             AS total_annual_savings_26x_freq,
+    -- Sensitivity: alternative frequencies
+    ROUND(SUM(savings_per_purchase) * 20, 2)         AS total_annual_savings_20x_freq,
+    ROUND(SUM(savings_per_purchase) * 52, 2)         AS total_annual_savings_52x_freq
+FROM savings_per_product;
+
+
+-- Per-product detail (top 50 by annual savings opportunity)
+WITH latest_prices AS (
+    SELECT DISTINCT ON (ph.normalized_product_id, ph.store_id)
+        ph.normalized_product_id,
+        ph.store_id,
+        s.slug   AS store_slug,
+        ph.regular_price AS current_price,
+        ph.observed_date
+    FROM price_history ph
+    JOIN stores s ON s.id = ph.store_id
+    WHERE ph.observed_date >= CURRENT_DATE - INTERVAL '90 days'
+      AND ph.regular_price > 0
+    ORDER BY ph.normalized_product_id, ph.store_id, ph.observed_date DESC
+),
+product_price_spread AS (
+    SELECT
+        lp.normalized_product_id,
+        COUNT(DISTINCT lp.store_id)         AS store_count,
+        MIN(lp.current_price)               AS cheapest_price,
+        AVG(lp.current_price)               AS avg_price
+    FROM latest_prices lp
+    GROUP BY lp.normalized_product_id
+    HAVING COUNT(DISTINCT lp.store_id) >= 2
+)
+SELECT
+    np.canonical_name,
+    np.category,
+    np.brand,
+    np.size,
+    np.size_unit,
+    pps.store_count,
+    pps.cheapest_price,
+    ROUND(pps.avg_price, 2)                              AS avg_price,
+    ROUND(pps.avg_price - pps.cheapest_price, 2)         AS savings_per_purchase,
+    ROUND((pps.avg_price - pps.cheapest_price) * 26, 2)  AS annual_savings_at_26x
+FROM product_price_spread pps
+JOIN normalized_products np ON np.id = pps.normalized_product_id
+ORDER BY annual_savings_at_26x DESC
+LIMIT 50;