How to find which products earn their ad spend
SKU-level truth across Shopping, Meta catalogue ads and Pinterest: which products convert the spend they take, and which quietly burn it.
IntentRank products by advertising efficiency with names, not item IDs, so the merchandising decision is readable.
- 5
- Steps
- 6
- Tools
- 3
- Rules
- 3
- Failure modes
When to use it
Use it when
- Monthly on any catalogue-driven account.
- Before excluding or boosting product groups.
- When blended ROAS is fine but someone suspects it hides losers.
Do not use it when
- Catalogues under ~50 products: read the campaigns directly.
- Windows shorter than the purchase cycle: slow considered purchases need longer reads.
What it needs first
Required
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
In order. Every tool named is one the gateway ships, and links to its reference.
Pull product-level Shopping performance with spend and conversions.
On Meta, join product-level insights to the catalogue so rows carry names and brands.
On Pinterest, run the conversion product report.
Merge by item ID, then bucket: earners, burners above the spend floor, and unjudged.
If GA4 ecommerce is healthy, sanity-check the top rows against site revenue.
Decision rules
The observable condition, what it lets you conclude, and what takes the conclusion back.
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 every conclusion must carry
- Every row carries product name, item ID, platform, spend, conversions, window.
- Exclusion proposals quote total spend saved over the window.
Where the agent stops
- 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.
How it goes wrong quietly
The cases where the analysis is wrong and still looks right. Read them before trusting a number.
- 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.
What the answer contains
- 01Earners
- Top products by efficiency with volumes.
- 02Burners
- Spend, zero-or-poor conversion products above the floor.
- 03Platform fit
- Products whose efficiency differs sharply by platform.