Your chatbot can make or break your customer’s experience of your brand. While customers appreciate the fast response times and always-available customer service, they also expect a conversation that is as natural and helpful as possible. Read on to discover how to improve chatbot effectiveness and, as a result, boost customer satisfaction.
The Challenge of Mimicking Human Conversation
Before we go into how to improve chatbot performance, it’s useful to first understand human conversation, and what it is we are trying to replicate through chatbot interactions.
Human communication is complex and multifaceted, and therefore difficult for chatbots to accurately mirror. During conversation, we are continually picking up different psychosocial cues from our conversational partner. We either consciously or subconsciously respond to these cues and adapt our tone and communication style to have the desired effect on our relationship with the communicatee.
For example, if your partner is upset that you haven’t unloaded the dishwasher, you will naturally adjust your tone to maintain harmony and resolve the issue while preventing conflict. If your manager asks you to work overtime and you have other commitments, you will again change your tone to be assertive while still communicating your commitment and dedication to your work.
In both of these instances, your communication style will reflect the social context and the emotionality of the situation.
In customer service, this adaptability is particularly relevant and important. In their day-to-day work, customer service agents have to respond to customers in different emotional states who need assistance in various ways. Customer service agents have to respond to simple informational requests, walk customers through various administrative procedures and help resolve issues for customers who have had a negative experience.
Throughout their interactions, their job is to restore, maintain and build relationships for enhanced customer satisfaction and ideally, loyalty.
For chatbots to mimic this ability effectively, you need the help of advanced AI that is specifically trained to pick up on psychological cues in communication.
Recent advancements in Natural Language Processing (NLP) technology are enabling chatbots to more accurately mimic natural human interaction for enhanced customer service.
As we go into how to improve chatbot effectiveness, we will intermittently discuss these advancements and what they mean for the future of chatbots.
How to Improve Chatbot Effectiveness: 6 Steps
1. Direct complex cases to human support team
Do not simply treat your chatbot as a FAQ directory, leaving customers at a dead end if their questions are more complex or not satisfactorily covered by your existing database. There will be occasions (many, to begin with) where chatbot support will need to be supplemented with the support of human representatives.
In these instances, chatbots are a great tool to gather necessary information about the customers problems, and direct them to the appropriate support team should they need more assistance.
2. Regularly update your database
Download and monitor chat transcripts to identify new frequently-occurring problems. Update your chatbot’s database with the new questions and their responses to improve chatbot effectiveness over time.
Chatbots are not a one-and-done solution. There should be a feedback loop between your customer support team, customers, and your chatbot in order to continuously improve the customer experience.
3. Improve accuracy with NLP
When a robot doesn’t understand you, it’s frustrating. Anyone who has ever struggled with in-car voice recognition systems can identify with this fact. So, when your already-frustrated customers come to you seeking a resolution, the last thing they want is to deal with inaccurate chatbots.
It’s vital for your chatbot to be able to recognise exactly what your customer is requesting or seeking answers to. NLP is now advanced enough to be able to recognise complex sentence structures, providing a smoother customer experience.
4. Generate empathetic responses
As well as understanding intent, some NLP technology is also capable of detecting emotionality. Our researchers at Symanto have outlined emotionality as one of the five key psycholinguistics characteristics of human interaction.
During conversation, our emotional state is highly responsive and reactive to triggers within the interaction. Our emotions change rapidly and vary in intensity depending on how we are spoken to.
In conversation, it is important that the emotional party feels heard and validated with an empathetic response. This is something that chatbots often get wrong. It’s vital for AI to accurately identify emotionality in order to be able to respond appropriately.
Symanto technology is highly effective at accurately deciphering emotionality in even very small amounts of text data. Our NLP technology helps chatbots capture customer sentiment and emotion through implicit cues (i.e., without explicitly asking the customers how they feel) and deliver empathetic responses for improved customer satisfaction.
5. Match tone with NLP
Researchers at Symanto have identified four communication style preferences in human communication. People have individual preferences about how they choose to share information. In human-human conversation we instinctively match our communication style to the person we are talking to in order to show respect and build empathy and trust.
These four communication style preferences are:
- Self-revealing: People with a self-revealing communication style talk openly about their personal and subjective experiences.
E.g. “I’ve been buying your products since I was a kid and I’ve never had a problem until now.”
- Fact-orientated: Fact-oriented communicators prefer to share facts, data and objective statements.
E.g. “The battery drains within three hours of standard use.”
- Action-seeking: People with an action-seeking communication style make direct or indirect requests and suggestions, expecting action from other people.
E.g. “Can you update your software so other apps can access the information?”
- Information-seeking: Information-seeking communicators ask direct or indirect questions searching for information. E.g. “Where can I find your nearest store?”
Since our NLP technology is highly effective at identifying communication style preferences, it also opens up possibilities for creating chatbots with personalised responses to match the tone of the customer.
For example, self-revealing types are more likely to respond better to conversational communication, whereas fact-oriented communicators would most likely prefer to-the-point, factual responses.