Predictive Content vs. Full Experiments | Persado Enterprise Help Center

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Predictive Content vs. Full Experiments

Predictive Content vs. Full Experiments

Compare the major differences between Predictive Content and Full Experiments to understand when to use both.

Written by Persado 1st Level Support

Updated over 2 years ago

	Predictive Content	Full Experiments
Definition	Predictive Content produces 1 to 4 Variants designed to deliver performance. Our machine picks concepts and phrases that are predicted to drive impact based on best performing language, emotions, and benefits according to your brand, industry, and channel. Predictive Content is a fast and easy way to use machine-generated content and still achieve a performance uplift.	Full Experiments produce 16 Variants to power our machine learning and achieve greater engagement. Full Experiments have two phases: Exploration and Broadcast. Exploration, the ‘learning’ phase, allows the platform to identify which emotions, narratives, descriptive elements, calls to action (CTAs), and formatting and positioning choices resonate with an audience at any given time. Broadcast, the ‘earning’ phase, allows marketers to take those learnings and amplify the success metric of each campaign.
Pros	Offers the fastest time to market and lowest level of effort. Can serve low-volume, low-impact campaigns. Is ideal for when technical or schedule limitations do not allow for testing of numerous Variants.	Deliver deep insights so you can understand the particular parts of your message that contributed most to success. Include an earning phase to optimize conversions. Feed Persado’s machine learning so we can further deepen our understanding of your brand.
Cons	Due to the small number of Variants tested, insights gained are limited. Typically, Full Experiments produce a higher lift for KPIs.	2-phased approach may not be ideal for rapid-time-to-market campaigns. Typically requires significant volume to reach statistical significance in both phases.
Average Number of Variants	1+ Variants and control (optional).	16 Variants and control.
Popular Use Cases	Audience size and/or response rates do not allow for Exploration (i.e., the sample size isn’t enough to reach statistical significance, a key component of Experiments). Time does not allow for Exploration. Technology limitations do not allow for testing of multiple creatives/Variants. Insights gained from the experimental design structure aren’t a priority.	The campaign is projected to be sent to a medium to large audience size. The campaign planning timeline allows for experimentation. The campaign is considered high impact and has high ROI potential. Gathering insights about your audience, in addition to performance, is a priority.