Comparison
For fifty years, testing an ad before launch meant putting people in a room. Now there's a faster screen. Here's an honest look at when each one earns its cost.
| Focus groups | AI pre-testing | |
|---|---|---|
| Cost | $4,000–$12,000+ per session | $49/mo for 20 analyses |
| Turnaround | 2–6 weeks | Minutes |
| Sample | 6–10 people in a room | Model trained on large-scale viewing data |
| Output | Opinions and discussion themes | Second-by-second engagement curve + 4 metrics |
| Bias risk | Groupthink, social desirability | Model error (directional, not a guarantee) |
| Best for | Concept & messaging exploration | Screening creative before spend |
Focus groups measure what people say, and people are unreliable narrators of their own attention. They rationalize ("I liked the message"), perform for the moderator, and anchor on the loudest voice in the room. Meanwhile the thing that actually decides your ad's fate — whether a distracted thumb keeps scrolling at second 2 — is precisely the kind of fast, unconscious behavior people can't accurately self-report.
Engagement prediction sidesteps self-reporting entirely: it estimates moment-by-moment attention from the creative itself, based on models trained on how people actually respond to video. See our methodology for exactly what the score does and doesn't claim.
Qualitative research answers questions a model can't: Does this concept make sense to my audience? Is the messaging offensive, confusing, or off-brand? What language do real customers use? If you're exploring positioning or testing a risky concept, human conversation is still the right tool.
The economical sequence: screen every cut with AI pre-testing first ($49/mo), and reserve qualitative spend for the one or two finalists that already hold attention. You stop paying $8,000 to learn that an ad nobody watches past second 3 also confuses people.
New to the category? Start with what ad pre-testing is, or compare against the other common alternative: pre-testing vs. A/B testing.
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