cartsnitch-fork-test/common/scripts/stats/README.md

Launch Announcement Validation Queries

Scripts to validate the two statistics cited in the CartSnitch launch announcement:

1. 847 products that shrank in the past 12 months
2. $336/year potential savings from buying the same items at the cheapest store

Status

These queries are written against the production data model but cannot be run yet — production infrastructure (CAR-99, CAR-104) is still being deployed. Once production data is available, run these scripts to confirm the cited numbers.

Queries

Stat 1: Shrinkflation count (shrinkflation_count.sql)

Counts distinct normalized_product_id values with at least one ShrinkflationEvent where detected_date falls within the past 12 months.

Key assumptions:

• "Past 12 months" is relative to query execution date (CURRENT_DATE - INTERVAL '12 months').
• A product counts once even if it has multiple shrinkflation events in the window.
• The 847 figure was generated from a specific date — re-running will drift as the window slides.

Stat 2: Annual savings potential (savings_potential.sql)

Methodology:

For each normalized_product_id with price observations from two or more distinct stores in the past 90 days:

1. Take the most recent regular_price per (normalized_product_id, store_id) pair.
2. Compute cheapest_price = MIN across stores, avg_price = AVG across stores.
3. savings_per_purchase = avg_price - cheapest_price.

To arrive at annual savings per family:

• Assume a typical family purchases each product ~N times per year (see PURCHASE_FREQUENCY_PER_YEAR constant in validate_launch_stats.py).
• Default assumption: products purchased on average 26×/year (~every 2 weeks for regularly bought items).
• Sum across all eligible products: Σ(savings_per_purchase × frequency).

Sensitivity knobs:

• PURCHASE_FREQUENCY_PER_YEAR — adjust purchase cadence assumption
• LOOKBACK_DAYS — how recent a price observation must be to be "current" (default: 90 days)
• MIN_STORES_FOR_COMPARISON — minimum number of stores a product must appear at (default: 2)

The $336 figure assumes the defaults above. If actual purchase frequencies differ significantly, rerun validate_launch_stats.py --freq <N>.

Running

# Requires DATABASE_URL env var pointing at production Postgres
python scripts/stats/validate_launch_stats.py

# Adjust purchase frequency assumption (default: 26 times/year)
python scripts/stats/validate_launch_stats.py --freq 20

# Run just stat 1 or stat 2
python scripts/stats/validate_launch_stats.py --stat 1
python scripts/stats/validate_launch_stats.py --stat 2

Raw SQL files (shrinkflation_count.sql, savings_potential.sql) can also be run directly with psql.