Campaigns are a means of organizing and implementing sets of personalization behavior. A useful analogy is an advertising campaign, which targets specific audiences with high-priority information for a specified period of time. Campaigns achieve this by allowing you to preferentially display campaign-related content in the content spots of a Web site. To accomplish such a goal, a campaign contains a set of rule-to-content spot mappings, start dates, and stop dates.
Users can create and manage campaigns through the Personalization Authoring Portlet. Campaigns are live as soon as their start date is reached and they may be published to other servers together with rules.
When a campaign is active in the run-time environment, its rule mappings take precedence over those in the Normal View. For example, a seasonal campaign might contain certain rule mappings that result in the display of special offers to a Web site visitor. A campaign can contain rule mappings for some or all of the content spots on a site.
Personalization provides a complete logging framework for collecting data on how visitors are using your Web site. If Feedback is enabled, data is automatically collected about each Personalization rule that is fired. In addition, development tools enable Web sites to collect a variety of data related to visitors' actions and behavior. By default this data is logged to a standard database schema for later analysis and reporting. The framework is also extensible, allowing Web sites to customize and supplement the way data is collected and stored to more fully meet their needs.
Personalization contains a dynamic recommendation system based on LikeMinds. LikeMinds is software that is used with your e-commerce applications. LikeMinds analyzes user interactions that occur on your Web site and generates real time predictions and recommendations to your Web site users.
Real time predictions are generated by three LikeMinds engines using recommendation rules within Personalization. These rules, called recommend content, base their predictions on transactions logged through Personalization's rating and action beans.
When a user visits your Web site, rating and action beans log captured transactional data. If your e-commerce Web site is set up so that users can rate content (or products), you use Rating beans to capture rating data. Similarly, if you use shopping cart technology, you use action logging beans to capture content affinity behavior to capture shopping activity. Both rating and action data is stored in your database. For example, the following types of transactions may be recorded: