Every chat conversation your customer service team has leaves a trail of valuable data around the issues your customers have experienced and their feature requests, questions, and concerns. If your contact center has been using chat as a customer service channel 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 chat conversations unless you methodically analyze them regularly with one of these methods:
- Manual tagging of conversations in your chat software;
- Use the chat software’s tagging rules to apply tags to incoming conversations automatically
- Use an automated chat tagging app like Playvox Customer AI
Tag Application Method
The first step is to turn raw data (chat 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 predefined tag taxonomy. Alternatively, this process can be automated by software that applies tags based on rules or machine learning classification models.
Automated tagging is the ability to apply tags to conversations based on messages’ content automatically. The benefits of automated tagging include significantly reduced manual effort, increased accuracy, and more consistency, as well as better coverage than manual tagging methods. Other factors you should consider:
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 act on the insights you find.
It can be challenging to come up with tags to cover all customer issues and requests up front. Tag discovery systems, which automatically scan conversations for frequently mentioned issues, can help you discover new tags and improve your tagging coverage.
Related Article: Top 5 Chat Conversation Tagging Challenges
Reports And Capabilities
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. 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 and capabilities you should consider when considering the right tagging method for your customer service team.
The 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
The Volume Change Report helps you compare tag mention volume on a period-over-period basis and identify growth patterns in your tags.
This report helps you track the relative change in tag mention volume to help identify outliers.
Attribute Filtering allows you to filter tag reports by chat 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 helps keep your tag reporting focused and accurate.
Related Article: How Support Tagging Boosts Product-Support Collaboration
Cost And Time Investment
A key consideration when choosing a tagging method is assessing how much it costs to run.
Manual tagging is typically included in your chat software subscription at no additional 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.
Automated tagging methods such as using your chat software’s tagging rules or using a dedicated analytics tool like Playvox Customer AI both require significantly less staff effort to run and maintain. While there will be 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.
Related Podcast: Support Ticket Tagging: How Automation Turns Data Into Action
Investing in an automated tagging method has other benefits you won’t see with manual methods.
Sentiment analysis can add a new dimension to your reporting, helping you gauge not just which issues are trending among your user base but also which experiences cause the most negative emotion and friction.
Additional Data Sources
Chat analysis will give you a good sense of the issues your customers are experiencing but might leave you with blind spots as not all users proactively get in touch. By bringing other sources of customer feedback into the mix such as surveys and third-party online reviews, you can build a more rounded picture of your current and potential customer needs. The right tagging solution will go beyond your chat software to capture this data as well.
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 trend changes via email.
In the table below, you can see a side-by-side comparison of the three tagging methods in terms of their capabilities and features.
Choose The Right Tagging Method For Your Customer Support Team
Proper tagging is your gateway to more powerful insights into what your customers need and think about your company and your products. Be sure you’re capturing every bit of data you can by selecting a tagging method that’s best suited to the size of your team, your ticket volume and other data sources, and your goals for growth.
Dig deeper into tagging in our free download, The Ultimate Support Tagging Taxonomy Guide. You’ll get an introduction to different types of tag taxonomies, step-by-step directions for creating your own taxonomy, a free tag taxonomy template, and more.