Hi Stephen, Sorry for the slow response. I was out of the country for most of the last 3 weeks and largely offline during that time. Interest topic to kick around.
It seems that monitoring our behaviors and extracting predictible patterns, which in turn can be converted into actionable insights that will drive future (buying) decisions is the holy grail of using big-data for marketing purposes. The discussion on explicit and implicit ratings touches on this. But where I think AI/ML/Big Data will largely fall a bit short is at the individual level. People are fickle. Statistically, what I did yesterday may have a strong influence on what I did today, but no AI or Big Data process will be able to monitor all of my behaviors and determine that I might wake up tomorrow and decide "today is the day that I am going to start doing X" or "ok... I'm kind of tired of binge watching netflix" or whatever. Maybe recommendation engines work well for many people, but I have yet to find one that works for me. Because I like one musical artist, does not mean I like some other musical artist that the recommendation engine thinks is closely related, because for me it may not be about "genre" but maybe about something else hidden deeper in the music. But I may just be an oddball.
Where I see Big Data being really powerful is in sorting out problems that are ridiculously mult-variate. Better understanding how individuals in different populations will respond to different medical treatments, for example. Climate. Or for industrial applications where 2nd or 3rd order effects on individual variables interact to produce significant, but rare, ocurrances which can influence production efficiency, product quality, reliability, and safety. Marketing can fit into this category as well, across large demographics, predicting future trends based on current events, etc.. But I think it may be a fools errand to try to predict the behavior of Jane Smith as if she is nothing other than some algorithm that can be decoded given enough data, the right data scientists, and sufficient server farms.