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Want to use conversation data attributes but you’re not sure how they can help stay organized and keep track of important information in your conversations?

We got you covered! 🤝 In this video, we’ll walk you through how to create and use them using real examples and use cases.

You’ll learn how to:

1. Set up conversation data attributes from scratch.
2. Apply them inside a conversation manually.
3. Automate their use with Macros, Basics, and Workflows to capture key data points from customers upfront and route conversations based on your criteria.
5. Organize these attributes with Views in your inbox.
6. Report on them to gain valuable insights.
 


If you found this helpful, let us know how you intend to use them to inspire us and others! 🚀

In what situations do you find attributes more advantageous than tags?

Hey @Graziela 👋

That’s a great question! Ultimately this will depend on what kind of data you want to capture the most and how you plan to use it, but below are some of the top situations where Conversation Data Attributes (CvDAs) can be more advantageous than tags.

Feel free to follow along this video too if you’re more of a visual learner 👀

 

  1. CvDAs offer more consistent tracking and organization
    Tags can be applied in all kinds of ways by folks who have permissions to manage them, so two teammates might use slightly different tags like “billing query” vs. “billing issue.” This can make it hard to see patterns when you’re looking at reports. CvDAs make this easier because they offer teammates a set of predefined options––like a list, dates, boolean and more–– to pick from and apply to the entire conversation every time, so everything stays organized and you can see trends and common issues clearly.
     
  2. CvDAs offer more flexible and accurate reporting
    Because CvDAs allow specific options like “priority: high, medium, low” or “customer tier: VIP, new, repeat” you can easily filter and break down conversations by these details. For example, if you want to know how many high priority refund requests came from VIP customers, CvDAs let you do that quickly and with more flexibility. This kind of filtering is not possible with tags, which are like single-layer labels and don’t allow the same level of sorting and insights.
     
  3. CvDAs are better at routing conversations to the right teams or teammates––with the right conditions
    CvDAs offer more flexibility when setting up routing rules in workflows because as they are more like structured and layered, while tags are more surface level and don’t give the same level of context. For instance, you might get multiple refund requests tagged with “refund,” but only the ones marked “high priority” and “VIP” should go directly to a specialist team with a faster response time. With CvDAs, you can can set up automatic routing rules to send the “high pri” refund conversation from VIP customers to the right team with a shorter SLA, making sure urgent cases get the attention they need.

In short, CvDAs provide structure and flexibility that make managing, reporting, and routing conversations easier and more efficient, especially when using an AI Agent like Fin that can benefit from having deeper context to automatically resolve issues in half the time!

I hope this answers your question. Anything else you’re curious about? Let us know! 👇

This video is incredibly helpful for anyone looking to better utilize conversation data attributes in Intercom! It covers everything from setting up attributes to automating processes and organizing them with Views. The step-by-step examples and practical use cases make it easy to understand how these tools can enhance workflow and keep customer interactions organized. Great resource for boosting efficiency and tracking key info!

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