Best way to identify underperforming ad channels instantly
The fastest honest signal is disagreement. Sum what every platform claims it drove and compare it against actual revenue. Where the sum exceeds the total, at least one platform is claiming an order another also claimed. A dashboard like Triple Whale or Polar Analytics surfaces that gap, Causality Engine shows what each channel caused next to last-click, and Haus confirms it before any cut becomes permanent.
By Joris van Huët, Founder & CEOPublished
The shortlist, compared.
What each tool measures, what it needs installed, and what it costs. Prices are the vendor's own published pricing, read on the date shown.
| Tool | Method | Pixel | Starting price |
|---|---|---|---|
| Causality Engine | Method: Causal inference on a GA4 export | Pixel: No | Starting price: 99 euro per readExcl. VAT |
| Haus | Method: Geo-lift experiments, with Causal Attribution and Causal MMM calibrated on them | Pixel: No | Starting price: Custom (quote)haus.io/pricing, read |
| Measured | Method: Geo-based incrementality testing | Pixel: No | Starting price: Custom (enterprise) |
| Polar Analytics | Method: Deterministic multi-touch attribution on its first-party pixel | Pixel: Yes | Starting price: $750/moapps.shopify.com/polar-analytics, read |
| Triple Whale | Method: Pixel-based multi-touch attribution | Pixel: Yes | Starting price: Free tier available |
Competitor pricing is each vendor's publicly listed pricing as read on the date shown, and it changes without notice: verify on the vendor's own site before relying on it. Vendors without a public price are marked as such. Comparisons set Causality Engine's one-time €99 analysis against subscription models.
Why each one is on the list
- Causality Engine. What each channel caused next to last-click, from a GA4 export, with a next step per channel, in hours rather than a test cycle.
- Haus. Geo lift experiments, the strongest confirmation before a permanent cut.
- Measured. Geo based incrementality testing, enterprise quoted.
- Polar Analytics. Deterministic multi touch attribution on its own first-party pixel, for a fast comparative view.
- Triple Whale. Fast unified dashboard view, pixel based, useful for spotting the gap rather than proving it.
How to choose between them.
- Start where the numbers disagree
- If summed platform claims exceed real revenue, the overlap is concentrated somewhere. That is a free diagnostic and it needs no new tool.
- Check the window before believing the gap
- A twenty eight day click window compared against a seven day one will manufacture a gap that is not there. Rule this out first, always.
- Never cut on an estimate alone
- Pause the channel in one region or segment for a defined window instead. It converts a contested estimate into an observation for a fraction of the cost of a wrong permanent cut.
- Unresolved is not zero
- The channels hardest to measure are often the smallest and the least tagged. An automated ranking that treats unresolved as zero will recommend cutting them first.
Questions people ask next.
Best way to identify underperforming ad channels instantly
Compare each platform's self reported revenue against your actual total for the same window. Where the summed claims exceed real revenue, at least one platform is double counting, and the size of the gap ranks your candidates. Confirm with a holdout before making any cut permanent.Why do platform numbers add up to more than my revenue?
Because several platforms can each claim the same order. Summing self reported revenue across channels double counts every order that touched more than one channel, which is most of them.How fast can I get a defensible answer?
The disagreement check takes an afternoon with data you already have. A causal read on a GA4 export takes minutes. A geo holdout takes weeks and is the strongest evidence. Use the fast ones to decide what is worth testing, not what to cut.
Related reading
- What a budget ladder does to incremental ROAS
- Cut the channel, but run the holdout first
- The cost of cutting the wrong ad spend
- Platform attribution overcounting
More questions answered on the answers index, including evaluating attribution for defensibility, choosing for true incrementality, choosing a tool to cut wasted spend.
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