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Hi everyone,

I”m Darshan Hiranandani, We’re a startup using Intercom for customer support, and we handle about 4K-5K conversations per month. One of our main challenges is effectively tracking and understanding the reasons behind users reaching out to our support team.

I’ve looked into conversation tagging, topics, and attributes, but they don’t seem to fully solve the problem, or perhaps I’m just not sure how to best apply them to our business. Should we be using all of these tools together, or is there a better approach? Would integrating another tool or system help us better categorize and analyze these conversations?

Ultimately, our goal is to capture user sentiment and clearly communicate this data to our product team to help drive improvements.

Has anyone faced something similar or have suggestions on how to approach this? Any advice on how to better track the reasons for support inquiries would be greatly appreciated!

Thanks in advance!

Regards

Darshan Hiranandani

Hey there ​@darshanhiranandani24, Emily here from Support Engineering at Intercom 👋🏼
 

For effectively tracking and understanding the reasons behind user inquiries, tagging conversations is a key strategy. By consistently tagging messages with labels such as "Bug", "Feature Request”, you can amass valuable data over time. This data can reveal common requests or prevalent bugs, helping your team prioritize fixes and feature development.

Additionally, you can generate reports such as a "Customer Voice Report" to list top feature requests, which can inform your product roadmap. This process becomes more manageable once you have a history of tagged messages.

Remember, tagging not only helps in categorizing conversations but also ensures that the right feedback reaches the appropriate teams within your company, such as engineers and product teams.

You can learn more about tags from this article 👈🏼

Hello Darshan, to address the challenge of effectively tracking and understanding the reasons behind customer support conversations, you could benefit from integrating multiple tools and strategies. Using conversation tagging, topics, and attributes together is a good approach, but ensuring they are set up properly to capture specific data points is key. Additionally, you could consider:

Refining tagging systems: Make sure the tags are comprehensive and aligned with common user issues or inquiries.
Sentiment analysis tools: Integrating sentiment analysis could provide clearer insights into user emotions and intent during interactions.
Automated categorization: Tools like machine learning-based systems can categorize and analyze conversations more effectively over time.
Data visualization and reporting tools: Using dashboards and analytics tools to present clear, actionable insights from these conversations can help communicate trends to the product team.


By combining these tools and ensuring proper implementation, you'll be able to better categorize user conversations, capture sentiment, and improve communication with your product team.

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