Conversation Analysis (CA) is a research method that has been increasingly used in UX research to understand social interaction and talk-in-interaction within the context of user experience. By analyzing naturalistic interactions between users and products or services, Conversation Analysis aims to describe the stable practices and underlying normative organizations of interaction, helping researchers gain insights into users' behavior, attitudes, and needs.

CA is particularly useful in UX research because it allows researchers to examine the ways in which users interact with digital products, including how they navigate interfaces, communicate their needs and preferences, and respond to system feedback. By analyzing audio or video recordings of user interactions with products, researchers can identify patterns and regularities in users' behavior, as well as the social norms and expectations that shape their interactions. In spite of the name, the Conversation Analysis research method includes nonverbal communication and the way people say things. Are they making a distinctive facial expression or speaking with strong emotion?

One of the key domains of research within CA is turn-taking, which refers to the way in which participants in a conversation take turns speaking and responding. By analyzing turn-taking in user interactions with products, researchers can identify patterns in users' behavior, such as interruptions or delays in response time, which can provide insights into the usability and effectiveness of the product.

Another important domain of research within CA is repair, which refers to the ways in which participants in a conversation repair misunderstandings or errors in communication. By analyzing repair in user interactions with products, researchers can gain insights into the challenges users face when using the product, such as confusing interface design or unclear instructions.

Finally, CA can also be used to analyze action formation and ascription, which refers to the ways in which participants in a conversation assign meaning to actions and behaviors. By analyzing how users interpret and respond to system feedback, researchers can gain insights into how users perceive the product and the value it provides.

Overall, CA is a powerful research method for UX researchers, as it allows them to gain deep insights into the ways in which users interact with digital products. By analyzing naturalistic interactions, researchers can identify patterns and regularities in users' behavior, helping them design products that are more usable, effective, and satisfying for users.