AI-powered chatbots have established themselves as a valuable asset to contact centers. Their ability to mimic human conversations resolves issues such as understaffing and high labor costs. Chatbot sentiment analysis enriches the value of chatbot technologies by giving them the ability to understand and interpret human emotions. 

More than that, chatbot sentiment analysis can recognize customer intent and make tailored responses based on the mood, emotions, urgency, and intentions of the customer. 

But is chatbot sentiment analysis really necessary for your contact center? Before we get into it, let’s quickly look at why sentiment analysis, in general, is so critical to retaining customers. 

The link between customer sentiment and customer success 

73% of customers expect you to understand their unique needs and expectations. For customer support teams, this means being in tune with nuanced customer sentiments—namely, their emotions, urgency, and intentions surrounding a particular issue.

Failing to recognize and alleviate negative customer sentiments—such as anger, frustration, and dissatisfaction—leads to high customer turnover rates. Not only will customers leave your brand for your competitor, but they may even deter others from doing business with you via word-of-mouth marketing and negative customer reviews.

With accurate insight into customer sentiments, you can deliver swift, empathetic customer service. The quicker you can gauge the sentiment of a conversation, the faster you can tailor the support experience to meet unique customer needs. In turn, you can improve customer satisfaction and your brand’s reputation. 

But beyond immediate conversations, sentiment analysis can improve your customer journey at scale. 

Using data gathered from a variety of sources, you can establish sentiments at different touchpoints and unlock insights into why these sentiments arise. These insights can be leveraged to make strategic improvements to your customer journey—and reduce the volume of angry, frustrated customers getting in touch. 

Still, trying to do this manually is an impossible task. More and more businesses are leveraging AI to improve business communications, including sentiment analysis. 

Chatbot sentiment analysis basics

Let’s break down chatbot sentiment analysis so that you can see how it works on a fundamental level. 

Use of NLP and machine learning

Natural language processing (NLP) is a branch of AI and machine learning that enables computers to understand, interpret, and analyze conversational inputs. Chatbot sentiment analysis specifically identifies a range of sentiments deriving from text (customer service call transcripts, live chat histories, etc.). 

Data collection from user interactions

Chatbots can collect data from a variety of customer interactions. As well as call and live chat histories, you can feed chatbots data from social media, customer surveys, websites, machine learning algorithms, customer reviews, and more. 

Processing and filtering useful data

The data collected by the chatbot is then processed, filtered, and converted from text into categorical features. This is called feature extraction, and it’s used to infer the context, intent, and semantic information of a conversational input. Feature extraction can be achieved through a variety of methods, most commonly bag-of-words, TF-IDF,  and Word2Vec. 

Sentiment classification and scoring

Using machine learning, natural language processing, and deep learning models, chatbots are taught to classify the sentiment of new conversational inputs. They’re fed labeled datasets of positive, negative, and neutral data and then use what they’ve learned to evaluate the sentiment of new user inputs.

The accuracy and reliability of chatbot sentiment analysis grows over time as the machine is exposed to more datasets. They can classify positive, negative, and neutral sentiments such as satisfaction, anger, and frustration and score them based on their polarity. 

Tailoring responses based on context

Once chatbots can successfully analyze and interpret sentiments, they can use natural language generation (NLG) to tailor their response based on context. They’re now capable of adapting their tone, language, and empathy so that responses are appropriate for the given sentiment. And they can perform the most appropriate action for the situation. 

Impacts of chatbot sentiment analysis on customer support

So, you know what chatbot sentiment analysis is and how it works. But what can it do for you?

Prevents agent burnout

According to Future Forum, 42% of the global workforce report burnout, characterized by chronic stress, exhaustion, and a lack of professional efficacy. Tellingly, employees working for companies who don’t embrace modern, workflow-optimizing technologies are 31% more likely to feel burned out.

Image sourced from Future Forum
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The result? Lowered motivation, productivity, job satisfaction, and retention. 

Utilizing chatbot sentiment analysis wards off burnout. Chatbots augment human agents, using sentiment analysis to assist agents with calls and relinquish the monotony of repetitive conversations. With automation handling calls, you can reduce the workload on your staff. This can prevent overworking and improve flexibility—a significant defense against burnout. 

That said, chatbots don’t mean you shouldn’t still monitor for burnout and try to allocate your staff efficiently. This is why you should always look to combine chatbot tools with things like reporting HR software. This will help you manage your team more effectively and flag any telltale signs of burnout, such as overworking or unusual work hours. 

Speeds up response times and issue resolution 

Customers want quick responses and first-contact resolutions—a feat that isn’t always possible if you’re understaffed or overwhelmed. Chatbots speed up response times by being available to respond to customer queries immediately, on a 24/7 basis.

Chatbot sentiment analysis gauges customers’ emotions and uses this knowledge to construct relevant, empathetic responses. They’re powerfully adept at adapting their tone to the customer and independently driving them toward a solution. 

Alternatively, they can also route the call to the appropriate agent, prioritizing customers based on sentiment to facilitate swift issue resolution for upset customers.

Provides real-time customer insights

Chatbot sentiment analysis provides real-time insights into how your customers feel about your company, from your products to your customer support experience. For example, Ochatbot delivers in-depth AI reporting and analytics, allowing you to delve deep into customer conversations. 

Unlock insights that enable you to:

  • Identify pain points and obstacles that prevent customers from moving along the customer journey.
  • Learn which touchpoints generate tickets and what common questions are asked by customers at these touchpoints.
  • Promptly be made aware of bugs and errors so that you can perform quick technical fixes.
  • Identify the issues that commonly trigger customer frustration and anger.

You can use these insights to improve the effectiveness of your customer support and inform wider marketing campaigns and business initiatives. 

Enhances CX personalization

Using the sentiment analysis insights gained from your chatbots, you can segment customers based not only on the words they use but also on the pain points they communicate, the topics that interest them, and a host of other relevant information. 

This gives you the power to create targeted marketing messages. For example, you can adapt your tone of voice and language style for each customer segment so that your communication style resonates with them. You can also deliver content that relates to the segment’s specific pain points or make personalized recommendations based on their topics of interest.

Reduces customer turnover

As well as being able to handle calls immediately—which improves response times and customer retention—chatbot sentiment analysis can identify negative sentiments in real-time. This empowers them to independently diffuse negative situations or, if necessary, escalate the issue so that frustrated, angry, and upset customers are responded to promptly by human agents. 

On a bigger scale, sentiment analysis performs emotion mining across a variety of customer interactions—customer support call transcripts, automated live chat transcripts, complaint emails, and more. In doing so, you can identify where customers churn and why they churn and then use this data to improve your customer experiences. 

Lowers operational and staffing expenses 

With the ability to handle routine inquiries, chatbots can reduce the headcount of human agents needed to meet customer needs. As a result, you can reduce operational expenses—including recruitment, salaries, and benefits—and reallocate more funds into activities that improve customer and employee experiences.

Gartner predicts that by 2026, contact centers will be using conversational AI to automate one in 10 agent interactions and thus reduce their agent labor costs by $80 billion

Improves service quality with minimal training

Customers expect high-quality customer service. They want your team to be available 24/7, to be intuitive to every emotion, need, and pain point. Remember, 73% of customers expect you to understand their unique needs and expectations.

But humans are fickle. We get tired, have bad days, and forget things from time to time, all of which can lower service quality. Plus, meeting service level expectations during busy or out-of-hours periods is a challenge unless you hire and train new employees, which can get expensive.

The great thing about chatbots is that they never get tired or have a bad day. Not only can they answer tickets and analyze customer sentiments round-the-clock, but they can also gather a wealth of valuable data. 

Of course, chatbots can never replace human agents. But, with chatbots in place, employees can exert more productivity toward resolving complex customer issues that require a human touch. And they can partake in more satisfying career development opportunities rather than mundane training. 

Consider using HR software solutions to track and manage agent performance. With accurate, real-time insight into productivity, you’ll be able to assess the impact of your chatbot sentiment analysis deployment on agent productivity. 

Improve customer support with chatbot sentiment analysis

In a world where just one negative customer experience can widely influence your brand’s reputation, sentiment analysis has immeasurable value. Using a customer service chatbot solution such as Ochatbot, you can enrich customer experiences by delivering fast, empathetic, and personalized customer service.

Plus, you can reduce operational and labor costs, optimizing staff allocation and improving workflows. So, it’s a win for your employees as well as your customers. 

Greg Ahern
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