Personalization & Customization

Have you wondered what “Personalization and Customization is? These two words are used interchangeably many times, while both have different meaning. A brief description is given below, from the point of view of websites / internet.

Personalization (p13n)

Personalization is driven by the web server which tries to serve up individualized pages to the user based on some form of model of that user’s needs. Good personalization requires the system to know a lot about the user to guess the user’s needs.

p13n stands for the word “personalization”. The number 13, represents the number of letters found between the “p” and the “n” in the word “personalization”.

People are more motivated to start using things than to take the initial time to learn about them or to set up a lot of parameters. They don’t want to spend time setting up complex personalization features.

The website can be said to be personalized when the user gets an “aha!” experience. This occurs when the content adapts itself based on the person’s profile, and provides something new, different, and possibly unexpected.

A content site that tailors links to news stories based on individual profiles is personalized, because the headlines change each day. This is a great time-saver, because the audience is working with the editorial staff to display new and informative headlines within a familiar framework.

Customization

Customization is under direct user control. The user explicitly selects between certain options. For example, you can setup a start page in My yahoo with headlines from the New York Times or from USA Today or setup ticker symbols for the stocks you want to track.

Amazon.com has an amazing personalization engine that gives each customer individualized recommendations of books. Even though this feature is far from perfect, it usually succeeds in including some relevant titles.

Users do not need to do anything to set it up. The system learns their preferences by recording what books they buy. (This is not perfect, since people sometimes buy books for presents even if they don’t like the books themselves.)

By watching millions of buyers, the system learns which books are similar. If many people who buy Salman Rushdie books also buy Arundhati Roy’s books, then it is a good idea to recommend Arundhati Roy’s new book to somebody who has bought Salman Rushdie’s books in the past, even if they have never bought any of his books. This recommendation happens without imposing any extra work on the users.

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