Digital body language refers to the online signals a visitor sends to a brand through various interactions with a site or app which conveys their interests, preferences, and intent. Such behavior is implicit and includes viewing a specific product, bookmarking an article, adding an item to the cart, reading an article, and so on. 

Capturing digital body language information is not only critical to understanding the wants and needs of each visitor, but also predicting the next best action. However, as engagement deepens, it becomes more and more difficult for marketers to assign meaning to a particular interaction, ultimately stunting their ability to act on it.

To make sense of the complex web of data surrounding a visitor’s digital language, machine learning algorithms can be leveraged to uncover important correlations between the product or piece of content a user is interacting with and its many attributes. This process, known as affinity profiling, calculates a score based on the inferred value of each engagement as well as its recency, creating a hierarchy of preferred product colors, categories, brands, topics, and so forth. With every new page view, action, and event, the affinity profile is updated in real time to reflect changes in user behavior and preferences over the course of the customer experience. 

Brands can then use these automatically generated user affinity profiles to easily build and target sophisticated audiences according to their distinct actions, or make recommendations based on signaled product or content affinities. 

Learn more about Dynamic Yield’s Affinity-Based Personalization capabilities.

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