Days live, within a single brand's account
Foreplay -- same video, two ads4 -> 52
Ace Blend -- testimonial video x26 -> 216
Qurist -- one product, three hooks4 -> 96
Plix Kids -- bundle vs single product13 -> 236
Each row is one brand, one product line, one account. The spread inside a single account is larger than the spread a scoring model is trying to detect between accounts.
A model has to beat that. It has to separate ads whose measurable attributes are identical, in accounts where same-creative outcomes already differ by an order of magnitude. That's a hard problem, and "82/100" is not a solution to it -- it's a confident restatement of the category average.
## How to audit a scoring vendor in one meeting
You don't need a data team. Five questions, asked in the demo:
1. **"What was the model trained on, and does it include losers?"** If the training set is only ads that ran long enough to be noticed, the model learnt survivorship, not performance.
2. **"Show me the holdout."** Ask for performance on ads the model never saw, in *my* category. Not a case study -- a confusion matrix, or at least a hit rate against a coin flip.
3. **"Does the score change if I swap the brand logo?"** If a known brand's logo moves the number, you're being sold a brand-equity lookup.
4. **"What's the score's accuracy on ads from one account?"** Cross-account accuracy is mostly base rates. Within-account is the only number that matters, and it's the one nobody reports.
5. **"What would make this score wrong?"** A vendor who can't answer this hasn't tested it. A score that can't be wrong isn't a measurement.
Ask these politely. The answers are usually honest and usually revealing -- most product teams know exactly where the model is weak, and almost nobody asks.
## What this category is actually good at
Rejecting prediction isn't rejecting the tools. The useful half is real, and it's the half that doesn't require a forecast:
- **Duplication detection.** Decode five variants and see that four share a hook type and an emotion. That's a measurable fact, available before launch, and it's the most common way a test budget gets wasted.
- **Account memory.** "This hook ran here in March and lasted nine days." Not a prediction -- a lookup. Teams forget; accounts don't.
- **Category saturation.** Counting how many competitors are already running your argument is arithmetic, not AI, and it kills more bad ads than any score.
- **Fatigue detection.** A decaying creative has a measurable shape. CTR, CPM, ROAS and frequency, weighted, moving over time. This is the one place where modelling genuinely earns its keep -- because you're describing a curve that already exists rather than one that doesn't yet.
- **Coverage gaps.** What the account has never tested is a fact about history, not a forecast.
Note what every item has in common: it's checkable. Someone can come back in six weeks and tell you it was wrong. A predicted score mostly can't be falsified, which is why it survives.
If you want the full pre-launch version of this -- the six checks and the scorecard -- it's in [pre-launch creative scoring](/blog/pre-launch-creative-scoring).
## Our position, including the inconvenient part
Marxx tested predictive scoring. Creative embeddings against outcomes, the standard approach. Once brand and objective base rates were controlled for, the signal did not hold. We didn't ship it.
That cost us a demo slide. It's the right call, and the exhibits above are why: in a world where the same video runs 52 days and 4 days in the same account, a number predicting which creative wins is theatre.
What Marxx does instead is report what is actually knowable -- what ran, what is decaying, what competitors are doing, and what the account has never tested -- decoding creative across hook type, offer type, ad type, emotion, social proof type, authenticity type and intended audience, over the full creative history of the connected account rather than the last thirty days.
**[Book a demo](/book-a-demo)** and we'll decode your last quarter live -- including the duplicates you didn't know you'd shipped.
## FAQs
### Do AI ad prediction tools actually work?
Not for ranking creative by future performance. The public record is full of near-identical ads with wildly different outcomes inside the same account -- which is the condition any predictive score would have to overcome, and generally doesn't.
### Why do scoring tools seem accurate in demos?
Because demos use ads that already ran. Scored retrospectively, a model that encodes brand size and category will look sharp. Ask for a holdout on unseen ads in your own category and the picture changes.
### Is there any creative signal at all?
Some. It's just thin relative to everything else driving the outcome -- audience, placement, auction conditions, budget decisions. Thin enough that acting on the score costs more than ignoring it.
### What should I use instead of a predicted score?
Duplication checks, account history lookups, category saturation counts and fatigue detection. All of them are facts rather than forecasts, and all of them can be shown to be wrong.
### Is longevity a good proxy for performance?
It's the best public one, with limits. An ad that has run 200 days is almost certainly working. A 4-day ad might have been killed for reasons unrelated to the creative. Treat long runs as strong evidence and short runs as weak evidence.
### Should I stop testing creative then?
The opposite. If you can't know in advance, the return comes from making tests cheap, fast and genuinely different from each other -- and from writing the kill rule down before you launch.
---
- [More Creative strategy articles](https://marxx.ai/posts/category/creative-strategy)
- [All articles](https://marxx.ai/posts)
