AI ad performance pretesting within 24 hours with ACT Instant

Harnessing AI to predict ad performance in 24 hours

ACT Instant uses AI-based coding, machine learning and predictive modeling to forecast ad performance. There are no?time and cost intensive surveys or respondents.

Ads are coded and analyzed across 130+ dimensions
70% of coding is automated
The remaining 30% is handled by our specialists
Predictive modeling analyzes individual dimensions and themes: Brand, Copy, Message, Scene, Character, Audio, Response
Our advertising specialists provide written analysis of the results and recommendations

How it impacts your business

  • Improve creative at lightning speed to accelerate impact across more content
  • Develop meta-learnings more quickly to enable greater impact across your whole optimization process
  • Leverage AI-powered technology for actionable recommendations and insights

Pre-test ads in 24 hours or less*

Latest technology

Latest technology

Leverages the latest technology?in computer vision, speech to text and facial recognition algorithms

Coded dimensions

Coded dimensions

130+ coded dimensions yielding over 300 data points per ad



  • Harnesses cutting-edge AI technology
  • Results at speed to fit almost all development timelines – potentially same day turnaround*
  • Tiered pricing starting from: 2,500 USD/ad
  • Predictive modeling based on MetrixLab databases from proven/validated pre-testing methodology
  • More detailed diagnostics via coding dimensions than traditional survey-based approaches
  • Enables more efficient meta-analyses to drive best practices and large-scale learning
  • Advertising expertise consultancy
  • It is available in the US for mobile social video and will roll out globally for other ad formats

* Same day turnaround for 5 ads or less if with advance notice and ads received by 10am local time

Insights and deviverables

Insights and deliverables

You’ll receive a fully analyzed report that includes one scorecard per ad tested:

  • Overall performance across ads
  • Performance against best practices
  • Reasons for performance
  • Recommendations based on individual ad/campaign objectives
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