With automatic personalization, people's interests are inferred on the basis of the actions they take, such as pages they visit, links they click on, and products they buy. Such inferences can lead to erroneous assumptions about visitors' interests, however, because people's intentions may be different from the actions they perform. For example, if a visitor looking for recipes mistakenly navigates to cooking classes, hoping to find links to recipes, her profile will indicate an interest in cooking classes, which may not be true.

Devise a system that automatically matches the needs of the audience to the content you have available. The basic idea here is to divide customers into groups, on the basis of a shared characteristic, and to look for trends within those groups. The assumption is that because customers within a group are similar in one way, they may be similar in other ways. For example, if Victoria is placed in a group with 20 other people, 15 of whom really like the novel The Scarlet Letter, the odds are that she will like it too.

Tables have had bad press of late. Originally designed to display tabular data correctly in HTML documents, they were soon misappropriated as a way to lay out Web pages. Understanding how to create complex layouts using nested tables has become part of the standard skillset of the Web designer. However, using tables in this way requires large amounts of markup and can cause real problems for users who are trying to access your content using screen readers or other text only devices. Thus, the Web Standards movement has pushed for the replacement of tables with CSS layout, which is designed for the job and is, ultimately, far more flexible.

Tables are becoming less popular as a layout tool, as designers take on the newer CSS techniques for which browser support is becoming widespread. However, tables can be used for their real purpose—that of displaying tabular data.

Here's how a scoring system would work. Say you offer 500 products on your site. Instead of making visitors sort through all 500 products to determine which one is best for them, you want to present only those that best fit their needs. If you have initially rated each product for speed, power, ease of use, and price, you can now have visitors score or rate these same criteria through a quick online interview. Store each customer's scores in a database. To determine which products to present to visitors, reference each customer profile by looking up the person's account using the account ID that you stored in that customer's cookie (cookies will be discussed shortly), and show only those products that meet the guest profile threshold scores.

You can create personalized pages by matching a customer to the highest-rated content in an area and by pushing the content to CONTENT MODULES. Include a list of all the related content by publishing links to CONSISTENT SIDEBARS OF RELATED CONTENT so that visitors can browse and make their own selections.

Here are four techniques for gathering information: edit, interview, deduce, and filter. The first two are forms of intentional personalization, and the last two are forms of automatic personalization.

Build a list of customer preferences and display the recommended items to visitors. Amazon.com does this when it tracks the books that all its readers buy most and displays them as FEATURED PRODUCTS. Provide CONTENT MODULES based on similar customer profiles. To determine areas of interest, analyze the correlation of all the guest profiles. This is also known as collaborative filtering.

Employ these techniques singly or in combination. By providing initial personalization from filtering, customers do not need to enter any special information up front. Over time, implicit and explicit information voluntarily offered by a customer can be added, allowing you to target customer needs more directly.

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