Intercom conversation tagging: Manual vs Tag Rules vs Prodsight
Intercom conversation leaves a trail of valuable data around the issues your customers have experienced, feature requests, questions and concerns. If your company has been using Intercom for any length of time, you have most likely accumulated a lot of this type of data.
However, it’s hard to draw meaningful, actionable insights from Intercom conversations unless you methodically analyze them regularly.
The three main methods for analyzing Intercom conversations are:
- Manual tagging of conversations in Intercom;
- Use Intercom’s Tag Rules to apply tags to incoming conversations automatically;
- Use an automated Intercom tagging app like Prodsight.
If you are evaluating options for setting up or improving your Intercom conversation tagging process, you will find this article helpful.
Tag application method
The first step is to turn raw data (Intercom conversations) into information (tagged conversations). Tags can be applied manually by a support agent as they are dealing with a support query based on the pre-defined tag taxonomy. Alternatively, this process can be automated by software which can apply tags based on rules or Machine Learning classification models.
Below I explore some key tag method considerations in more detail.
Automated Tagging is the ability to apply tags to conversations based on message contents automatically. The benefits of automated tagging include significantly reduced manual effort, increased accuracy and consistency, as well as better coverage than manual tagging methods.
Your tag taxonomy will continuously be evolving as new issues arise. The ability to automatically apply tags to historic conversations can reduce the time it takes to crystalize insights and help inform actions quicker.
It can be challenging to come up with tags to cover all customer issues and requests upfront. Tag discovery systems which automatically scan conversations for frequently mentioned issues can help you discover new tags and improve your tagging coverage.
The outputs of your tagging process are only as valuable as the insights you can generate and the actions you can inform based on those insights.
Reporting can turn your tagged conversations into insights which you can could then be used to inform decisions around your customer experience. The more flexibility you have in exploring your data from various angles, the deeper your understanding of that data will be.
Here are some key reports you should consider when analyzing the conversation tags:
Volume Report shows you how many mentions various tags have attracted in a given timeframe (week, month, quarter, etc.). It can help you identify your support volume drivers and understand the reasons your customers contact you.
Volume Change Report
Volume Change Report helps you compare tag mention volume on a period-over-period basis and identify growth patterns in your tags.
A report which allows for tracking the relative change in tag mention volume to help identify outliers.
Attribute Filtering allows you to filter tag reports by Intercom user attributes to help segment your tag data.
Tag Hierarchy enables you to report on tag trends based on different levels of abstraction.
Sometimes you might discover a strong connection between two tags and want to combine them into one. The ability to quickly merge tags help keep your tag reporting focused and accurate.
Cost and time investment
A key consideration when choosing a tagging method is assessing how much it costs to run.
Manual tagging is included in the base Intercom subscription and attracts no additional software cost. However, to get the benefits from this process, you will need to invest staff time in continuously applying tags to conversations, creating and maintaining a tag taxonomy and analyzing and preparing reports. You will incur this cost in the form of staff salaries.
Another consideration is the opportunity cost of your staff being distracted from higher-value activities such as solving complex user support issues.
Be sure to check out this post on some common challenges when running a manual tagging process.
Automated tagging methods such as Intercom Tag Rules or using a dedicated analytics tool like Prodsight require significantly less staff effort to run and maintain. While it will attract some cost in the form of monthly or annual subscription payments, in most cases, it will be significantly less expensive than running a manual tagging process.
Sentiment analysis can add a new dimension to your reporting, which allows you to gauge now just which issues are trending amongst your user base but also which experiences cause the most negative emotion and friction.
Additional Data Sources
Intercom conversation analysis will give you a good sense around the issues your customers are experiencing but might leave you with blind spots as not all users proactively get in touch. By bringing in other sources of customer feedback into the mix such as Typeform surveys, App Store reviews, Delighted NPS verbatims you can build a more rounded picture of your current and potential customer needs.
While you can often rely on a regular review of weekly or monthly tag reporting, it can be very impactful to be alerted of tag trends changes via email.
In the table below, you can see a side-by-side comparison of Intercom Tag Rules, manual tagging and Prodsight in terms of their capabilities and features.
We hope this comparison helped you evaluate your Intercom conversation analytics strategy. Whether you are just thinking about conversation tagging or have already tried manual tagging or Intercom Tag Rules, be sure to check out Prodsight free trial.