Why B2B support needs sentiment analysis

0
132 views

Share on LinkedIn

Getting to the root of a problem for a customer is one of the most important tasks a customer support professional has. For this reason, agents have to be experts at understanding how to dissect a customer’s feedback or question to get to the actual cause of the problem at hand. But what can often be left unaddressed is how a customer speaks to the agent, which can be just as important as what is said.

This is especially true with online communication, where information is often interpreted in many ways. We have all received an email or text where we felt the person on the other end was frustrated or upset, but in fact, after having a conversation face-to-face or via phone we realize they weren’t frustrated at all. Miscommunication often leads to assumptions, affecting the way support agents address the customer. Customers can provide additional information beyond what is said when they send your team a message, so customer service software solutions are beginning to tap into this “hidden code” to enhance their communications.

Rather than replace humans, sentiment analysis powered by AI technology helps customer support agents be more effective in their efforts, allowing them to be more strategic and work smarter. By using software built to go beyond the words and analyze the sentiment of messages, B2B customer support teams can better understand their customers’ frustrations beyond what is included on the ticket and do so in record time.

At-a-glance understanding with sentiment technology
Every written message is filled with sentiment indicators that may be difficult for a human to quickly analyze, but a customer support team that deploys AI technology to analyze customer messages can save time and provide accurate insights into customer frustrations. By allowing AI to instantly analyze a message, your team will get a snapshot of how your customer is feeling without having to read the entire email or analyze the whole ticket.

For example, an especially frustrated customer might send a message with more than 1,000 words, outlining in detail every piece of your product or solution that is causing problems for them. On the flip side, you might have another customer who frequently writes lengthy messages, and while they might be happy with your product, they do not cut to the chase and explain the problem when submitting a ticket.

While both tickets may need customer support, the first will be analyzed through sentiment analysis as a customer who is feeling particularly distressed, allowing your team to immediately route the ticket to a supervisor. Your agent will be free to help the second customer whose issue will likely be more easily addressable within their skill set.

Beyond freeing up time to help support another customer, sentiment analysis is also helpful for junior customer support agents who may not have the same level of experience as a supervisor. By having sentiment analysis in place, these junior agents can swiftly pass the email on to an experienced member of the team, providing a quicker resolution for the customer. These sentiment indicators also are clear signs for junior agents to avoid “getting in over their head” by attempting to help a customer when they don’t have the knowledge to do so.

Flag customers for proactive communication
A software solution with sentiment analysis is a must-have for a support team managing B2B customer relationships. A customer support agent might read one customer’s consistent negative messages as a sign the entire company is dissatisfied with the product. With sentiment analysis, support teams can also step back within the software and view the company as a whole. If most of the employees at the company are communicating with a polite or satisfied sentiment, then the support team can expect that the customer is not as likely to leave as you initially thought.

This provides the customer support team an opportunity to reach out directly to the disgruntled user to better understand what their specific concerns are and how to better address them. The user may simply need a conversation away from a support ticket in the form of some one-on-one training or an open dialogue allowing them to voice concerns with your solution. By having this conversation, the customer support agent creates a personalized channel of communication, listening to customer concerns and demonstrating dedication from the customer support agent to find ways to permanently fix their frustrations.

In any case, disgruntled or not, be sure to choose the appropriate channel that matches the conversation. If a customer is upset, a support agent writing back through live chat or email won’t convey the agent’s empathy. A proactive phone call from a senior agent saying “I’ve seen you have had several tickets in the past week, is everything OK?” can make a big difference for maintaining a strong customer relationship.

Sentiment analysis can also indicate when an entire company of users might be slowly cooling on your solution. While none of their employees may openly express frustration in a ticket, sentiment analysis can identify that the overall satisfaction of the customer may be degrading. While your support team should already be holding regular conversations to maintain a positive business relationship with your customers, sentiment analysis can help flag when your customer needs more in-depth discussions about their expectations and your capabilities with your customer success team. Having this conversation early enough can mean the difference between losing a customer and retaining their business. If you make them feel heard and prove the importance of your solution to their business, you may even be able to use this conversation as an opportunity to upsell the customer. These are opportunities for customer success and customer support to work together in pursuing their primary goal – keeping a customer happy.

Better understand your own messaging
Your customer support team likely has some standard messages they use to engage with customers. Much of this is either agreed-upon or formalized language to give your customers a sense of consistency and uniformity from your brand when they reach out for support. More than likely, however, this messaging can be refined using sentiment analysis. By tracking changes in customer sentiment before and after an agent shares a response, your team can begin to understand what types of messages reduce your customer’s frustrations, and by how much. You may even notice trends among different types of B2B issues, suggesting not all issues should be handled using the same messaging or cadence.

Even how you greet your customers can make a difference. It may be tempting to say something simple like, “Thank you for contacting us. You can expect a response from a team member within 24 hours.” However, your customers may respond better to a message that reads, “Thank you for bringing your issue to our attention. Your problem is important to me, and I will do my best to help you fix it.” Other industries use AI to perform A/B testing for messaging and so should customer support.

To summarize, B2B customer support is better with sentiment analysis integrated into support software. Agents and teams will make their communications more efficient and effective, giving them the ability to make good decisions faster and better prioritize their time to maintain positive customer relationships. With customer support agents playing a major role in impacting customer success, sentiment analysis helps ensure customers feel understood and heard throughout the issue resolution process. A positive experience with customer support sets the tone for the rest of the company and plays a role in establishing a stronger relationship with the customer, which often helps in driving revenue and overall success.

LEAVE A REPLY

Please enter your comment!
Please enter your name here