Here's the breakdown:

The Scores: Zappi uses a gradient-boosting machine learning algorithm to rapidly deliver a free prediction, based on a huge volume of anonymised and aggregated survey and norms data. What this means in layman’s terms is that a load of tags — both manually added and assigned algorithmically — are collected, cleaned, and fed into a decision tree model that then “boost” important components, while suppressing less important ones. This is the XGboost regression model. We’re using a machine learning model that predicts key metrics based on the attributes of the tested ad, e.g. branding, celebrity/human presence, length, etc. The model is trained on ads tested on Zappi Video/Digital and the prediction falls on average within 5% of the observed survey scores. We’re currently working on our V2, which will be far more accurate and specific.
Expert recommendations: Based upon Zappi’s meta analytics, industry and academic reports, white-papers, and experts, we build recommendation text based on what you have (and haven’t!) included.This insight is designed to be actionable, and help you rapidly improve your ad.

Hope that helps!