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