Most of the technologies and tools that companies use to manage their relationship with their customers usually fall under the banner of Customer Relationship Management (CRM) System. Even though personalization is just one piece of the CRM pie, it is a very crucial piece as effective personalization significantly enhances the ability of the organization to initiate a discourse with its customers to the point where any and all of these dialogues are seamlessly integrated with the database’s historical and transactional information. Based on the data stored in these databases and recent history (the pages customers viewed in the last session), Web sites automatically attempt to improve their organization and presentation of content. These Web sites, armed with a host of appropriate tools — including intelligent agents, recommendation engines and the like — attempt to anticipate the context of the interaction with their customers and personalize each customer’s shopping experience (Andre & Rist, 2002; Billsus, Brunk, Evans, Gladish, & Pazzani, 2002).
Personalization is a process of providing special treatment to a repeat visitor to a Web site by providing relevant information and services based on the visitor’s interests and the context of the interaction (Chiu, 2000; Cingil, Dogac, & Azgin, 2000). Personalization is needed to successfully manage customer relationships, promote the right product the customer is interested in, and manage content. Most of the advanced personalization might require sophisticated data mining techniques and the ability to display dynamic content without seriously compromising system resources (dynamic display of content will usually mean increased download time).
There are a few well-known techniques for personalization. Rules-based personalization modifies the content of a page based on specific set of business rules. Cross-selling is a classic example of this type of personalization. The key limitation of this technique is that these rules must be specified in advance. Personalization that uses simple filtering techniques determines the content that would be displayed based on predefined groups or classes of visitors and is very similar to personalization based on rules-based techniques. Personalization based on content-based filtering analyzes the “contents of the objects to form a representation of the visitor’s interest” (Chiu, 2000). This would work well for products with a set of key attributes. For example, a Web site can identify the key attributes of movies (VHS, DVD) such as drama, humor, violence, etc., and can recommend movies to its visitors based on similar content. Personalization based on collaborative filtering offers recommendations to a user based on the preferences of like-minded peers. To determine the set of users who have similar tastes, this method collects users’ opinion on a set of products using either explicit or implicit ratings (Chiu, 2000). Please see Figure 8-1 for an illustration of how a Web site could use all three personalization methods to best serve the customer.
Figure 8-1: Overview of personalization techniques