A recommendation engine can lead to showing a targeted set of items, focusing the customer on the more desirable items. The engine driven by predictive analytics determines the items that the customer is more likely to enjoy. This improves the decision process by removing information overload.
For example, Google Search returns the most likely results first. Google’s predictive analytics removes the need to click through pages of insignificant results. Similarly, Amazon shows items that likely fit the need of the customer, allowing for ease of viewing and decision making.
Mike Shor, a professor at the Owen Graduate School of Management, wrote an article on the benefits of simplifying the decision-making process by decreasing the number of items offered.
Excerpt:
“...often-cited experiment in which people are given the chance to taste a sampling of jams, asked to pick their favorite, and given coupons to buy one. One group selects from 6 jams, another from 30. Researchers found that people who tasted 6 were more likely to buy something than those who tasted 30—the latter simply had too many choices.” - Link: Article
Monday, January 25, 2010
Monday, January 4, 2010
Drawing out Tastes
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.
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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