Part 3/9:
The first hurdle was making sense of the vast amounts of data collected through Dataf Fast. By analyzing various websites, the creator developed a unique conversion profile for each one—essentially a fingerprint of what a converting customer looks like. The profile includes factors like the visitor's geographic location, device used, and the number of visits prior to making a purchase.
Next came the ambitious task of writing an algorithm that could assess the likelihood of conversion for individual visitors. Machine learning capabilities enabled the AI to compare incoming visitor data against the established conversion profiles, generating a conversion score ranging from 0 to 100. A score of 0 indicated a cold visitor, while a score of 100 indicated a hot prospect ready to make a purchase.