# How to find which products earn their ad spend

> Tier: deep. Full contract: quantified decision rules and evidence requirements.

SKU-level truth across Shopping, Meta catalogue ads and Pinterest: which products convert the spend they take, and which quietly burn it.

**Intent.** Rank products by advertising efficiency with names, not item IDs, so the merchandising decision is readable.

## When to use

- Monthly on any catalogue-driven account.
- Before excluding or boosting product groups.
- When blended ROAS is fine but someone suspects it hides losers.

## When not to use

- Catalogues under ~50 products: read the campaigns directly.
- Windows shorter than the purchase cycle: slow considered purchases need longer reads.

## Sources

Required: Google Ads
Optional: Meta Ads, Pinterest Ads, Google Analytics 4

## Preconditions

- Catalogue campaigns with product-level reporting live.
- One currency, or explicit conversion notes.

## Inputs

- `window`: Analysis window. Default: last 30 days.
- `spend_floor`: Spend below which a product is not judged. Default: 10x target CPA equivalent.

## Procedure

1. Pull product-level Shopping performance with spend and conversions. — tools: `google_ads_get_shopping_performance`, `google_ads_get_shopping_products`
2. On Meta, join product-level insights to the catalogue so rows carry names and brands. — tools: `meta_join_product_insights`, `meta_get_catalog_products`
3. On Pinterest, run the conversion product report. — tools: `pinterest_run_conversion_product_report`
4. Merge by item ID, then bucket: earners, burners above the spend floor, and unjudged.
5. If GA4 ecommerce is healthy, sanity-check the top rows against site revenue. — tools: `ga4_run_report`

## Decision rules

- **When** A product spent above the floor with zero conversions on every platform → **conclude** Burner confirmed; propose exclusion with the exact spend as evidence. **Unless** It is a new launch inside its ramp window; date it instead.
- **When** A product earns on one platform and burns on another → **conclude** Platform fit finding, not a product finding; propose moving budget, not delisting.
- **When** The top 20% of products carry over 80% of catalogue spend → **conclude** Report concentration; the long tail's problems are secondary by construction.

## Evidence requirements

- Every row carries product name, item ID, platform, spend, conversions, window.
- Exclusion proposals quote total spend saved over the window.

## Human approval boundary

- Exclusions and product-group changes are merchandising decisions applied by humans in platform UIs.
- The gateway writes none of this; the deliverable is the ranked list.

## Failure modes

- Item IDs differ across platforms for the same SKU; merge failures silently double-count. State the join rate.
- Returns are invisible to ad platforms; high-return products look like earners. Flag categories with known return rates.
- Price changes inside the window shift ROAS without any media cause; note repricing dates when known.

## Output

- **Earners**: Top products by efficiency with volumes.
- **Burners**: Spend, zero-or-poor conversion products above the floor.
- **Platform fit**: Products whose efficiency differs sharply by platform.

## Related

- Skill: [shopping-feed-health](https://www.getmcpads.com/skills/shopping-feed-health.md)
- Skill: [budget-reallocation](https://www.getmcpads.com/skills/budget-reallocation.md)
- Guide: [cross-platform-report](https://www.getmcpads.com/guides/cross-platform-report.md)