Are You Taking a Data-driven Approach to Managing Customer Support?

There’s no way around it – in the modern business world, companies need data from all areas of their business to keep up. One industry that hasn’t typically been at the forefront of the data revolution is customer support. However, with many businesses now realizing that support isn’t always a cost center (and can sometimes even pay for itself) the demand for actionable data has been on the rise.

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Why Sentiment Analysis Customer Service in B2B Matters

Sometimes how a customer speaks to you is just as important as what is being said. A compact label for this type of refined communication is customer sentiment. As more customer service software solutions are placing an emphasis on sentiment, it should come as no surprise that companies are looking to leverage this “hidden code” in communication to work smarter and more efficiently.

With this said, here are a few ways sentiment analysis in customer service matters in B2B and why it’s become a focal point for many teams… 

You can understand true meaning at a glance with sentiment technology – One of the best uses of sentiment analysis is the ability to get a snapshot of the “feeling of a message” without reading every single word. For example, a 1,000-word ticket is submitted to your customer service team. If the sentiment is “frustrated”, you can instruct your agent to immediately assign the ticket to a supervisor who can handle it. This saves the agent from needing to read the entire ticket and lets them focus their time and effort on solving issues within their capabilities. This is an excellent way for customer service teams to save time with AI technology.

Sentiment analysis creates proactive communication opportunities to save relationships – Selecting a software solution that provides sentiment analysis information at the customer level can be a great asset for managing B2B customer relationships. Instead of deeming a company “at risk” because one employee there doesn’t like you, take a step back and look at how the entire company feels about you. If the rest of the employees at the company had past correspondence with a “polite” or “satisfied” sentiment, then the company may not be as likely to churn as you may have initially thought. This information also provides you with a nice discussion point to have an impactful customer conversation with the disgruntled employee, letting you reach out and allude to the fact that their colleagues are happy with your business. It’s a softer way of asking “What can we do to fix your concerns?” by indicating their negative sentiments are unique and isolated.

 

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Looking at customer sentiment lets you evaluate your service messaging and responses – Finally, a broader usage of customer sentiment is how it can be leveraged to refine messaging to improve the overall perception of your service team. To do this, examine the sentiment of tickets that come in both before and after the initial response from your team. Does the sentiment improve, and if so by how much? Is this improvement consistent across different types of B2B issues (IT, operations, inventory, etc.)? Look at the change in overall sentiment first, then dig deeper into issue types to find detailed room for improvement. You’ll notice even little things like how you close tickets can make a difference. Instead of saying “Thanks for contacting us”, being more personable with “Thanks for bringing your issue to our attention, it’s important to me and I will do my best to resolve it” can shift the overall sentiment of the ticket conversation.

 

Wrapping up, sentiment analysis matters to B2B customer service because it makes your communication smarter and more efficient. The technology enables your agents to make better decisions with how they prioritize their time and is a great way to measure the impact customer communication has on your business. Sentiment analysis has evolved from a fringe technology to a mainstay in customer service teams, with more companies adding sentiment data into their business processes.

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