There are 3 options available to you (based on your subscription) if you wish to categorize and label conversations. These will allow you to track common themes or problems:

Each method has its benefits depending on what’s important to you, but in general we recommend using a combination of all three. Read on to find out about the differences between these methods.

Our Product Education team has created a short video comparing conversation tags and attributes 👇

Conversation topics are the most consistent, automated way for you to categorize every conversation in your workspace.

You define a a set of keywords and phrases to include (or exclude) and any matching conversation will be automatically added to a topic:

Topics can be applied retroactively to your existing conversations, and require no manual input from your customers or team.

Intercom’s machine learning engine will also suggest new topics for you based on trends in your conversations, opening up potentially missed insights and opportunities.

Conversation topics are purpose built for in-depth reporting too, so you can really dive deep into the subjects your customers chat about:

Conversation data attributes are the most structured way to group your conversations because you can define a specific type like: Text, Numbers, True/false or even a list of predefined values.

They're excellent for categorizing all of your conversations for reporting purposes. For example, by type, urgency, product area etc.

Conversation data attributes are perfect for managing your Help Desk. For example, if you have a “Priority” attribute it could be set to "high, medium, or low". You can then use these attributes as filters for Help Desk views, and conversations will move in and out of views in real time.

For comprehensive reporting, conversation attributes can be set as required, which ensures that every conversation is correctly identified or categorized before it's closed.

If your teams use tickets, you can create ticket attributes to store this information. Same as conversation data attributes, you can make these required and use them in your Help Desk views.

Conversation tags and attributes can be applied automatically using Workflows, but only conversation attributes can be collected from your customers directly.

This lets you group conversations by values that can’t be discerned from keywords. For example, a customer could choose between “bug report” or “feature request”, or let you know how urgent their request is:

Conversation tags are the most flexible, and specific as they’re applied to individual replies in a conversation.

They’re useful for marking a particular part of the conversation so you can find it later. For example if you’re highlighting beta feedback for your engineers, or need to bookmark conversations affected by a bug or outage.

Tags can also be applied automatically by Workflows, but not defined by your customers.

Tags are particularly useful for an individual’s needs, like highlighting a list of conversations to learn from, or collecting a showcase of your team’s top GIFs. 😉

To demonstrate how all these methods can be used in conjunction with one another, let’s take an example conversation: