Your chatbot's prowess lies in its knack for delivering an outstanding customer experience each time it interacts. 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 piece, let's explore how chatbot sentiment analysis can be a game-changer for enhancing customer service.
What's the fuss about?
Chatbot sentiment analysis delves into your customers' interactions with your chatbot. It's like dissecting their tone and words to gauge their emotional spectrum, whether it's joy, frustration, fear, or fury.This analysis helps pinpoint areas of improvement.
Sentiment, in this context, is the emotion expressed in text, be it positive or negative. In customer service, delving into customer sentiment helps gather feedback on their experiences with your interactions, whether through customer service or other touch points.
Why is chatbot sentiment analysis vital?
As AI chatbots have infiltrated customer service, the aim is to enhance service and save costs. But since customer expectations are ever-evolving, a bad experience with a chatbot won’t be pardoned just because it's a bot. Customers want solutions, and if the chatbot fails, they’ll revert to seeking assistance from live agents or switch brands altogether.
Given that chatbots offer cost savings and efficiency, understanding them better through sentiment analysis becomes crucial. It helps design chatbot interactions that leave customers satisfied.
But what about net promoter score surveys?
They're a good start, but they're part of your quantitative data, not always providing the specifics behind customer responses. Deep-diving into chatbot sentiment analysis offers context, allowing you to connect the dots and discern areas for improvement.
Benefits galore: Why chatbot sentiment analysis rocks
Proactive customer support: Customers turn to chatbots to learn or seek information. Analyzing sentiment helps gauge theirs atisfaction or frustration, enabling proactive measures to meet their needs.
In turn, this helps you becomemore proactive by anticipating their needs and customizing your chatbot to meetthem. It makes it easier for your customers to get the help they need throughtheir preferred channels. If you’re already providing customer support acrossdifferent channels, your sentiment analysis may reveal their specific requestsin their chatbot interactions on each channel.
For example, customers who justbought your product are more likely to use the chatbot on your website. So, ifyou go through most of their requests, you will identify common issues andthemes behind them.
To anticipate their needs, usepre-qualifying questions relevant to these themes every time your customerinitiates a conversation with your chatbot based on the requests that come fromdifferent channels. Based on the answers they provide to your pre-qualifyingquestions, your chatbot will then direct them to a specific script relevant totheir request, which helps them resolve their issue faster and moreefficiently.
Being proactive also meansimproving the quality of responses your chatbot provides to your customers,especially if you realize that they are constantly requesting the chatbot toconnect them to an agent to solve a common issue. Chatbots can’t handle allyour customer service issues which means they complement what your customersupport agents are doing. By analyzing their customer sentiment, you can tellwhen to hand over the customer to a customer service agent, ensuring seamlessinteraction.
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.
Sentiment categorization for flexible support: Categorizing sentiment helps understand the weight behind customer feelings.This insight aids in making support more flexible, addressing specific issues promptly.
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 customerservice 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.
Bias-free feedback analysis: Overcoming bias in feedback analysis is vital. Sentiment analysis helps compare data from various sources, giving a clearer picture of customer sentiments.
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.
Product recommendations and upselling: 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.
Filtering low-value customers: Identifying patterns of customers who aren’t aligned with your product's vision helps focus efforts where they matter. 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.
Leverage our platform to build and analyze your chatbot interactions, thus elevating the quality of support you offer.
