Top 5 Chat Conversation Tagging Challenges

Top 5 Chat Conversation Tagging Challenges

Chat is a powerful tool for business growth, allowing businesses to communicate directly with their customers. It means you can quickly and easily offer customer support, engage with users, and use it for lead generation.

Chat lets agents communicate directly with customers, as well as track and solve questions and issues. It can build a valuable bank of data to inform business decisions moving forward, based on information that comes directly from users.

Various teams need insights into customer issues and needs, from the C-suite to product managers, as well as designers, researchers, and marketers. With this data, stakeholders across the business can make better decisions based on customer feedback.

To keep track of consumer data, most customer support teams use chat conversation tags. This means they can categorize customer issues and requests to pass on to other teams across the business. However, most support teams are tagging issues manually, which leads to a plethora of problems.

Related Article: Why You Should Analyze Customer Conversations in Chat

Top 5 Chat Conversation Tagging Challenges

1. Inconsistent Chat Tagging

One of the most common issues raised by the hundreds of customer support teams we’ve spoken to is that tagging is inconsistent across their team.

Usually, support teams will have a clear framework for tagging support conversations. However, there are inevitably times when a customer support agent will tag a conversation differently from how you might expect. 

That’s because conversation tagging is subjective. Even with a clear set of rules, agents might not tag a conversation according to conventions because they perceive the issue differently. 

Take a customer who initially reaches out with a question about their subscription, but then asks for support with payment. How would you tag that conversation? With a manual tagging system, two agents might have a different perception of how to track that conversation.

We’re all prone to biases when making judgments and decisions, so it’s impossible to be sure that all conversations with a particular tag are related to the same concept.

It might not seem like a big issue to have a few conversations that are tagged differently, but it can really impact the accuracy of your reporting. It’s particularly important as your business grows, as large-scale inaccuracies in your chat analytics could lead to making business decisions based on wrong data.

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2. Poor Tag Coverage

Tagging conversations inconsistently is one issue – but not tagging them at all is an entirely different one with consequences that are potentially more far-reaching.

Unfortunately, this is one of the major issues customer success teams have reported to us when it comes to chat tagging – that it just doesn’t happen at all.

There are numerous reasons for this lack of tagging:

  • Naturally, customer service agents are focused on helping their customers. They want to support them as quickly and efficiently as possible, which means they don’t want to take up precious time deciding how to tag a particular conversation when they could be helping the next customer.
  • Marketing and product teams may have different perceptions of what’s an important insight. It might be difficult for customer support agents to visualize what sort of data these teams want to see from support conversations, so they don’t attempt to tag them.
  • In instances where it’s unclear what tag should be applied to a particular conversation, service agents often leave them untagged. Although they will do their best to apply tags to support conversations based on the agreed structure, it’s easy to overlook conversations that don’t neatly fall into a clear-cut category.

All of this has an impact on your chat analytics and reporting. Some issues will be underreported, and others will be overreported, leaving you with inaccurate data that’s difficult to glean any insights from.

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3. Cost Of Manual Chat Tagging

Surely there’s not a significant cost associated with adding simple conversation tags to customer support conversations, right? You might think so, but actually, that cost can really add up when you consider that dozens of customer support agents are applying tags to thousands of conversations on a daily or weekly basis.

The cost will depend on the size of your business and how many staff are in your customer support team. But let’s imagine that you have five agents on your team, who each spend half an hour every day tagging conversations.

The average Customer Support Agent is paid about $17 an hour. So over the course of a year, five agents spending half an hour every day on conversation tagging would cost your company $10,000. Not such an insignificant cost after all!

And that amount doesn’t even take into account the cost incurred by agents spending time on tagging rather than helping customers more quickly. When you take that opportunity cost into account, the amount of money saved – and the amount of extra revenue your business could earn – through a more efficient chat conversation tagging process is something to think about.

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4. Inability To Change Tags Retrospectively

This is a common issue when it comes to support conversation tagging. The purpose of tagging conversations is to gain insights into the customer experience and understand what they’re struggling with to improve in the future.

However, it’s only upon researching and prioritizing customer problems that businesses learn what the overarching issues are. After digging into the problems flagged by customers, you might want to test theories to do some more investigation around particular issues.

For example, you might want to look into how many times a customer has mentioned a particular issue – but you can’t, because the tags have already been applied at the point of the conversation, and they can’t be changed. This is frustrating and doesn’t let you test and learn from the data to apply the insights when making any sort of decision to improve the customer experience.

Related Article: How Support Tagging Boosts Product-Support Collaboration

5. Limited Reporting

What’s the point in tagging conversations? Support conversations are tagged to learn from customer complaints and questions. Data around the consumer experience is truly valuable for businesses when making decisions and improving the customer experience. There’s no point in tagging conversations if they can’t be used to produce insightful reports to drive actions across the organization.

Chat is really useful for customer engagement and support, and while most solutions have great basic reports on conversation tags, they were likely never designed to be an advanced analytics tool. 

That means that businesses can use chat to gather high-level insights into their customers’ experiences, but it’s difficult to uncover deeper insights. For instance, you might want to find out how many of your users on a pro plan signed up in the last three months and have mentioned canceling their account during a support conversation. But that’s not something you can do with most solutions’ built-in reporting facilities.

It’s possible to do some clever things with Excel or Tableau to get all the answers you want from your chat conversation tagging – but who has the time to do that? Creating a manual data analysis process alongside manual conversation tagging is time-consuming — and another challenge for your teams to stay on top of.

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The Fix For Your Manual Chat Conversation Tagging Issues

A manual tagging process for tracking customer support conversations in chat is far better than no tagging at all. However, manual tagging does come with several issues, as reported by hundreds of customer support teams and outlined above. These are:

  • Inconsistent tagging
  • Poor tag coverage
  • Cost of manual tagging
  • Inability to change tags retroactively
  • Limited reporting abilities

To address these issues, we created Playvox Customer AI. We automate manual chat conversation tagging so that your team can spend their time helping your customers and creating informed solutions to their problems, rather than worrying about how to categorize a particular conversation.

Playvox Customer AI automatically discovers topics in your conversations and measures sentiment, so you can quickly and easily discover what your customers need support with — and how they feel about your company. We also aggregate your mentions in one place to make it easier for you to delve into the data.

It’s an automated system, meaning it’s very consistent. You can get statistically significant insights from the data we collate, without the manual labor, meaning your team’s time is freed up and you can make informed business decisions without any worries about data inconsistencies or inaccuracies.

Want to see what it can do for your chat conversation tagging troubles? Request a demo.

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