E-commerce marketers cannot ignore the tremendous innovation in the AI sector that has the potential to deliver more refined user experiences.
Machine-learning working beneath the algorithms is becoming more and more pervasive and intelligent in synthesizing data in real time to help personalization engines to present individualized experiences.
In the world of e-commerce as the self-gained or third party data gets accumulated, machine learning revolution can enable stores to present super-personalized experiences.
Machine learning helps Recommendation Engines:
-To learn from businesses and customers across external and internal touchpoints,
-To create customized algorithms that constantly polish themselves to serve the business goals more successfully,
From the stream of information containing user feedback occurrences and recurrences the predictive model can learn how the visitor/customer reacts or makes no reaction to different recommendations. Machine learning technology helps in identifying algorithms performing better or worse for a certain customer segment/scenario, and able to fine-tune itself to serve more seamlessly.
As we saw enormous computing potential needed to serve recommendations for numerous customers who have to be understood first on their individual level - within milliseconds. Content optimization on a large scale could not exist without the help of machine learning - or it could exist on the expense of draining the resources of human workforce, and it still would be so slow that it would take days to react the customers’ actions.