Consumers buy based on their tastes. Their tastes have patterns that predictive analytics can model. As a customer gives feedback on items through a rating system such as Netflix's the patterns describe the customer’s buying preference.
The patterns may be logical, such as enjoying a particular director or agreeing with a certain critic. However, the patterns may be more difficult to decipher by human effort. Predictive analytics proves these patterns, both the logical and the not obvious, removing the need for human intervention.
The Netflix recommendation system through the rating patterns determines the user’s preferences for movies. This system introduces movies that meet the user’s tastes, increasing the satisfaction of the Netflix service. As well, the system can recommend lesser known movies that match the user’s tastes, increasing capacity use of all movies.
As well, the Netflix system removes items that I am less likely to enjoy. Netflix removes the noise leaving items that have greater satisfaction.
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