B2B customer support has become more complex than ever…in a good way. By utilizing the many tools available—and more technologically advanced than ever—B2B companies are able to gauge how their customers are feeling and how satisfied, or not, that they actually are.

Managing B2B customer support relationships is very different than managing individual consumers (B2C). When handling customer issues at a company level, you have to manage multiple contacts per customer, multiple tickets per company, and usually multi-layered issues.

That’s a lot of information to keep track of, and today’s customers expect you to know what’s going on at all times. Customer support software that isn’t B2B-specific can leave your agents in the dark about key relationships and other important data points, delaying the time to ticket resolution and frustrating customers with questions they may have already answered. Not only that, because of the high-value nature of B2B customers, missing the signs that a customer may be unhappy or at risk of leaving can prove costly.

Sentiment analysis is a commonly used tool by B2B customer support teams and refers to assigning a metric to a piece of text that details how positive or negative that text is. The simplest type of algorithm uses a dictionary to look up which words or phrases indicate which sentiment. For example, if a text says, ‘all you need is love,’ it marks it as positive. If a text says, ‘I still haven’t found what I’m looking for,’ it marks it as negative.1

Using text alone may not provide the complete picture, however. Couple simple with advanced algorithms that use machine learning to learn from large data sets to also capture nuances or tone.

TeamSupport offers real-time sentiment analysis within tickets so agents can instantly gauge the tone of a customer response. Powered by IBM Watson® technology, this automated technology helps B2B customer support teams prioritize their ticket workflow and also creates proactive customer support opportunities. We’ll explore the importance of using this information to build meaningful customer relationships in Part 2 of this series.

How does customer sentiment analysis differ from a Customer Distress Index, or CDI™?

TeamSupport’s proprietary CDI provides a 0-100 score indicating how happy a customer may be with your business. This score can be customized by providing weights to specific values. For example, if ticket resolution time is of high importance to your customers, you can weigh this more strongly than total number of tickets or another value. This score can be a great asset in building and maintaining positive customer relationships.

It’s one of the best features of our customer service database software because it lets customer service teams monitor customer satisfaction and measure overall customer health in order to be more proactive in mitigating the risk of churn.

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In other words, customer sentiment analysis uses text to indicate a positive or negative tone to the communication. The CDI uses data to indicate whether a customer may be satisfied or frustrated.

So, why should B2B customer support teams pay attention to customer sentiment and CDI? And what should they do with the information they glean from each tool? Find out in Part 2.

1 Five Practical Use Cases of Customer Sentiment Analysis For NPS, Medium.com, June 6, 2017. https://towardsdatascience.com/five-practical-use-cases-of-customer-sentiment-analysis-for-nps-a3167ac2caaa (accessed June 28, 2020).