Based on the analysis of data on visits and transactions, SALESmanago calculates the probability of other products occurring after a visit or purchase of a specific product and recommends those giving the greatest chance for customer interest.
SALESmanago Copernicus operates on the basis of five types of recommendations:
- collaborative filtering - offering products based on the similarity of users and concurrence of various products
- most frequently bought after visit other
- most frequently visited together
- most frequently bought together
- mixed statistics with weight
Generated recommendations can be used in communication channels: e-mail, website, web push, social media, advertising networks.
Machine Learning eliminates expert restrictions such as custom user behavior and unique preferences, matching recommendations to changing customer behavior
in real time or price sensitivity.
