The way a customer talks to you is often just as critical as what is being said. Customer sentiment is a perfect label for this type of communication. As more customer service software is focusing on improving customer sentiment, companies should be paying attention to use these kinds of sentiment analysis tools to efficiently improve their businesses and customer service satisfaction.
We will discuss a few easy ways in which sentiment analysis in customer service unlocks a deeper understanding because it enables you to interpret the actual meaning behind a written review or Tweet about your company and because it allows you to extract and monitor the underlying mood in customer feedback.
One of the key uses of sentiment analysis is the ability to gain insight into the feelings of a customer without reading every single word.
For example a 600 word ticket is sent to a customer service agent at your company. If the sentiment is “negative”, you can advise the agent to automatically allocate the ticket to a supervisor, who is qualified to handle this sort of situation. This prevents the agent from having to read the whole ticket and helps them allocate their time and resources on solving issues within their abilities. This is a great way for customer service teams to save time and work more efficiently.
Sentiment analysis can make a difference in communication enabling your customer support team to be proactive instead of reactive.
Using an AI based analytical approach with the use of SaaS sentiment analysis can be an excellent asset for managing customer relationships. Instead of only viewing the individual sentiment of one single customer, take the whole sentiment of how all customers feel about you. This will give you a sense of whether negative feedback is just isolated opinions or points to an urgent problem. With a sentiment, customer enquiries can easily be divided into different segments.
Looking at customer sentiment gives you insight into the effectiveness of your customer service team.
Ultimately, a greater outcome of consumer sentiment analysis is how it can be utilised to optimise messaging to boost the general impression of the customer support team. In order to do this, study the sentiment of the tickets that come in before and after the team’s initial reaction. Does the sentiment improve, if so, by how much?
Is this shift in improvement across all forms of B2B, B2C issues (product, events, service, inventory, etc.)? First, look at the shift in general opinion, then dive further into problem categories to find comprehensive space for enhancement. And little stuff like how you close tickets can make a difference.
Use the captured sentiment to treat customers more individually. Rather than being generic and saying “Thank you for your message”, be more personal and say “Thank you for bringing this issue to my attention, it is important to me and I will solve it as quickly as possible”. This can shift the entire sentiment of the conversation between the customer and the service agent. You can find out how to respond to feedback even more individually in our article on the topic of Surveybot.
In conclusion sentiment analysis matters to customer service because it makes communication effective and smarter.
Sentiment analytics tools enable your customer service representatives to make better decisions about how to use their time more effectively and are a great way to continuously measure the impact of customer communications on your business.
Schedule a demo with a consultant and learn how to start analyzing open-ended responses.