The effectiveness of your chatbot lies in its ability to help you deliver a positive customer experience in every interaction. Some of your customers prefer using chatbots, while others think you’re using chatbots to prevent them from reaching a live agent who can assist them.
Their resistance means you need to look into chatbot sentiment analysis to help you understand their experiences with your chatbot. As you do this, you’ll identify persistent challenges that drive their resistance and address them to change their perception of chatbots.
In this article, we’ll discuss how chatbot sentiment analysis can help you improve customer service.
What is Chatbot Sentiment Analysis and Why Does it Matter?
Chatbot sentiment analysis evaluates your customers’ interactions with your chatbot. This involves studying the tone and words customers use when interacting with your chatbot to understand how they feel, whether that’s happy, sad, afraid, or angry. Based on these interactions, you can identify areas of improvement.
The sentiment is an emotion expressed in text, whether positive or negative. In customer service, analyzing customer sentiment allows you to collect feedback on a customer’s experience based on your interactions with them, through customer service, or on other touchpoints that require you or the team to interact with customers.
The development of AI chatbots in customer service has been an effort to improve customer service and save business costs. Given that customer expectations are not static, your customers won’t excuse a bad experience simply because they were interacting with your chatbot. Customers want your help in solving their problems with your product or service. If your chatbot can’t help them, they’ll go back to the default way of doing things — usually dealing with a live agent. If they can’t get help there, they’ll choose to work with a different brand.
Given that chatbots come in handy to save costs and improve efficiency, you must conduct sentiment analysis to understand them better and design chatbot interactions that leave them satisfied.
So, what if you have data from net promoter score surveys? It's a great start, but these numbers are part of your quantitative data, which doesn’t always provide you with answers to specific questions behind your customer’s responses in your net promoter score surveys that you run. Digging deeper through chatbot sentiment analysis helps you provide some context around the numbers you have. Then, you can connect the dots and see where you’re doing well and what you need to improve.
Besides, AI has improved the capability of chatbots in customer service. When you conduct sentiment analysis, you can customize customer interactions with your chatbots to improve their experience with your company, an initiative that your customers will appreciate through their loyalty.
Benefits of Chatbot Sentiment Analysis
Sentiment analysis allows you to dig deeper to understand what customers feel through chatbot interactions.
After all, an increase in chatbot usage means that 54% of your customers expect you to be adaptive, flexible, and able enough to keep up with their needs by using the tools you have to address their needs, no matter the platform they choose to contact you for help.
Here’s how chatbot sentiment analysis will help you improve customer support operations.
1. Proactive Customer Support
Customers typically use chatbots to learn how to complete a task or find information on a particular topic they’re interested in.
Analyzing customer sentiment allows you to measure the level of frustration or satisfaction with either of these issues. In turn, this helps you become more proactive by anticipating their needs and customizing your chatbot to meet them. It makes it easier for your customers to get the help they need through their preferred channels. If you’re already providing customer support across different channels, your sentiment analysis may reveal their specific requests in their chatbot interactions on each channel.
For example, customers who just bought your product are more likely to use the chatbot on your website. So, if you go through most of their requests, you will identify common issues and themes behind them.
To anticipate their needs, use pre-qualifying questions relevant to these themes every time your customer initiates a conversation with your chatbot based on the requests that come from different channels. Based on the answers they provide to your pre-qualifying questions, your chatbot will then direct them to a specific script relevant to their request, which helps them resolve their issue faster and more efficiently.
Being proactive also means improving the quality of responses your chatbot provides to your customers, especially if you realize that they are constantly requesting the chatbot to connect them to an agent to solve a common issue. Chatbots can’t handle all your customer service issues which means they complement what your customer support agents are doing. By analyzing their customer sentiment, you can tell when to hand over the customer to a customer service agent, ensuring seamless interaction.
When doing sentiment analysis, you may flag some words and phrases that some of your customers use when they need help from a customer service agent, so that it triggers a handover to a human agent immediately.
2. Sentiment Categorization for Flexible Customer Support
When analyzing customer sentiment, you’ll not only know how your customers feel but also understand the weight behind their feelings concerning different issues they need help with.
To do this, you’ll need to categorize customer sentiment by understanding the tone they use and the verbs, tenses, adjectives, and analogies in their conversations with your chatbot.
Once you’ve done that, create a scale of the emotions behind these sentiments, ranging from extremely positive (e.g. excited or satisfied) to extremely negative (e.g. frustrated or unsatisfied). Map the emotions behind these sentiments to the specific issues they need help with to determine how each issue affects their experience. Then, you’ll know where to start when introducing flexibility in your customer support.
Suppose you’re taking more than six hours to resolve customer issues and your sentiment categorization reveals it as an issue that bothers your customers. In that case, you may decide to focus your customer service strategy on the speedy resolution of customer requests. You may also use sentiment categorization to make your customer service more flexible by making a case for specific product improvements and even requesting developers to prioritize bug fixes that cause customer frustration.
If your customers are constantly requesting a new feature that you’re working on, then you may add a script inside the chatbot to update customers on your progress. This will help them set the correct internal expectations and ease their frustrations around your product lacking a specific feature they need.
3. Eliminate Bias When Analyzing Customer Feedback
Sentiment analysis helps overcome bias when analyzing customer feedback by helping you compare data from different sources to get an accurate picture of what's going on with your customers.
For example, if you run customer satisfaction surveys or net promoter score surveys, it's easy to gloss over the positive or negative feedback you receive and end up making biased conclusions about the quality of support you provide.
However, when you start digging deeper into chatbot interactions to understand the sentiments behind what customers are saying, then you can join the dots and establish the relationship between what you get from the surveys you run and chatbot interactions.
Also, when you’re looking through large volumes of text manually to derive insights from customer feedback, there’s a tendency to gloss over the details and move through the text faster. In the process, you end up missing specific sections of comments and ignoring others at the expense of having a balanced view of what customers think about your brand and their experiences.
With sentiment analysis, however, you have access to tools that help you dig deeper into large volumes of customer data to help you identify what elements of your customer service you need to improve.
4. Make Product Recommendations and Upsell
Given that your customers have already bought from you once, understanding the sentiment behind their chatbot interactions helps you predict what they need to improve their experience even further.
Sentiment analysis provides insights into your products that could go well with the original product a customer bought to help them get more value from their purchase.
For example, Landbot has native integrations with Airtable and Google Sheets. If the team at Landbot analyzes customer sentiment from chatbot interactions and realizes that your customers are struggling to find a good tool to manage data, then they can recommend Airtable or Google Sheets to help with data management and data analysis.
5. Filter Low-Value Customers
Sentiment analysis allows you to identify the different types of customers you’re getting and see if they’re aligned with your ideal customer profile. Your product isn’t for everyone, and some customers will always be unhappy with it despite your best efforts to give them what they need.
With sentiment analysis, you can identify patterns of customers who are always complaining about issues you’ve already addressed.
This way, you can then request them to use a different product that matches their needs. Alternatively, you can also work on creating relevant comparison content that helps you clearly state what your product is capable of and how you stack up against the competition.
Through sentiment analysis, you have an effective way that helps you to understand not only how your customers communicate but also the emotions behind what they say. It helps you find effective ways to improve the quality of service you deliver and reduce churn in the process.
This way, you can even set benchmarks or standards that only you can meet in the industry and gain an edge over your competitors. Use Landbot to build your chatbot and then conduct chatbot sentiment analysis to improve the quality of support you provide to your customers.