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This topic introduces personalization in Optimizely. There are different ways of applying personalization to content on your website. You can either use the simple built-in visitor group functionality, or you can add the sophisticated machine-learning recommendation capabilities of Optimizely Content Recommendations, part of the Optimizely Personalization product suite.
Options
Through personalization you can individualize content displayed to online visitors, instead of showing the same message to everyone. Personalization can be applied for an individual visitor, or for a segment of visitors. Personalization can be manually configured from the user interface, or it can be automatic using intelligent algorithms.
Visitor groups - manual personalization
Using visitor group-based criteria, you can manually target content. You can, for example, design a product banner, a landing page, or a registration form specifically for first-time visitors, or for visitors from a geographic region or market.
Visitor group personalization (rule-based personalization) uses incoming HTTP request data from visitors to identify, for example, device, location, and number of page visits. You create visitor groups based on desired criteria, and use these to target content for visitor segments. You can also use visitor groups with Marketing Automation systems.
There are many predefined visitor group criteria available, and you can also develop your own criteria. See Visitor groups in the Optimizely User Guide, and Developing custom visitor group criteria.
Optimizely Content Recommendations - automated personalization
For large and complex websites, visitor group-based personalization may become difficult to manage. Here you can use Optimizely Content Recommendations to apply automatic content recommendations, based on individual or group website behavior. Content Recommendations uses Natural Language Processing (NLP) to understand the meaning of each piece of content at a granular level and builds a real-time interest profile for each visitor based on their interactions with the NLP-generated topics.
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Last updated: Jul 02, 2021
