Chatbots are not new but their proliferation in the customer service world is a paradigm shift that can no longer be ignored by businesses who want to stay relevant. Today’s customers anticipate and expect a chatbot to be their entry door to a customer service journey. No longer is it a disrupter. Today, it’s required.
That said, businesses are still finding themselves on the upward side of the learning curve, understanding these bots, their value, their use and their power. What they do know is that they need them.
Before businesses can implement any kind of chatbot, the need to evaluate the pros and cons and identify the types of chatbots that will help their customers seamlessly come on board. Their unique needs depend on several factors that range from business size, business goals, culture, and the chatbot’s purpose.
Ultimately, the goal should be to engage customers in a way that they appreciate, and the best way to do that is to make the bots feel as human as possible. Identifying your unique needs and understanding the differences and nuances among chatbots in general, and AI-powered chatbots specifically, will help inform which bot is the right bot for your customer service needs.
Chatbots of any kind, be they basic informational chatbots, or chatbots powered by AI, are a benefit to customer service. Chatbots powered by Artificial Intelligence take your bot to the next level, empowering it to learn from and to teach your customer service representatives to be more effective. If you want to provide the best possible customer service experience, employing an AI-powered chatbot is how to do it.
There are eight types of AI-based customer service chatbots that can enhance customer experience:
Scripted chatbots are the most primitive form of customer service chatbots. As the name goes, scripted chatbots interact with the end customer through a pre-defined script. These chatbots only respond to specific instructions, and the response also follows a scripted pattern. They are straightforward to build and deploy.
Scripted chatbots can help your customers, especially with FAQ kind of questions, and can be used to cut down call-center traffic.
Rule-based chatbots are an improvement over the native scripted customer service chatbots. They are also referred to as decision-tree bots, and for a good reason. As the name suggests, rule-based chatbots apply a set of defined rules. The chatbots classify the queries to a particular type and then respond based on the identified type's predesigned rule.
A lot like a flowchart, rule-based chatbots map out the conversations to anticipate what the end-user might ask and what the correct response should be to those queries. Rule-based chatbots cannot answer questions that it is unable to classify.
FAQ chatbots are a derivation of scripted chatbots. Despite their simplicity, they act as a powerful and accessible way for businesses to provide customer support. Customers find FAQ chatbots to be the best way to enable self-service (maneuvering through pages of FAQ results).
FAQ chatbots are programmed to provide instant answers to the user's questions seamlessly. FAQ chatbots can also provide links to relevant FAQ content if the user wishes to explore further.
Service chatbots gather relevant information from users to complete their requests for a particular action. You are most likely to experience a service chatbot in the airline industry, assisting you with flight bookings, cost of seat reservations, checking flight status, and other related issues. Service chatbots are similar to the more primitive chatbots in terms of understanding but, are trained to take action like any other customer service chatbots.
Customers want an immediate solution to their problems, and it is always preferable to engage with them where they are. Social messaging chatbots are integrated within social media platforms and provide customers a more personal touch simply by being present wherever they are. Social messaging platforms such as WhatsApp Messenger and Facebook have become the hot spot for businesses to connect with their customers and even provide them with assistance for their queries in the same places.
Speech recognition chatbots accept voice inputs by the end-user. These voice-enabled chatbots are also capable of responding through voice. Speech recognition chatbots use speech-to-text technology to understand a user query, and after processing the same use, text-to-speech technology responds vocally.
Businesses can build speech recognition customer service chatbots by using speech to text and speech APIs. Speech recognition customer service chatbots provide a more personalized and seamless experience for the users. Additionally, one can integrate these chatbots with data delivery channels and other related services.
Advanced tools allow the creation of a common set of AI capabilities, letting you build your virtual assistance once and deploy it to both your digital and voice systems.
With progress in the field of Natural Language Processing (NLP), customer service chatbots have only gotten smarter. NLP algorithms allow tokenization of a text to identify sentences, words, and parts-of-speech. This is done based on training the algorithm against large text corpora.
For example, consider the sentence: "I want to book a hotel." NLP training allows the bot to understand that in the question, the word "book" implies "reserve" and not a "publication." More and more chatbots are powered today by natural language processing.
While NLP-powered chatbots are popularly marketed as AI chatbots, they have certain limitations. Especially when it comes to interpreting long texts, simple NLP chatbots fail to preserve the context.
The traditional AI-based customer service chatbots preserve context throughout a discussion, mimicking a human conversation. Algorithms like Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) have enabled AI chatbot advancements. AI chatbots, however, can be difficult to build and deploy. The accuracy level is often a challenge, and prolonged training is needed to make them industry ready.
Ensuring they are relatable to customers is vital to customer satisfaction with their interaction with a bot.
Chatbots are an effective way for companies to automate customer service and provide the seamless, self-service experiences many consumers want – there’s just one problem: too many chatbots aren’t up to the task. To be effective, chatbots need to be intelligent and capable of providing the same level of empathetic customer support a human agent would. That is a tall order for some, if not most chatbots.
If you’ve implemented a chatbot that can’t understand what your customers are telling it, or one that sounds too robotic when engaging with customers, you’re likely doing more harm than good for your customer experience.
Here are four ways to make your chatbot feel more human:
Agents with personable, empathetic personalities can do a lot to improve customer service – the same could be said about chatbots. When you design your chatbot, give it a personality that fits with your brand and make sure it’s intelligent enough to hold its own during conversations. Chatbots that come across robotic or repeatedly state “I don’t understand” won’t live up to expectations and can dissuade customers from using your chatbot again, defeating the purpose of your implementation.
Think about how the best human agent would respond and try to emulate that experience.
You can also take personalization up a notch by giving your chatbot additional humanizing aspects, like a name or even an avatar –just never pretend your bot is human– customers hate that. Make it clear from the start they’re talking to a chatbot.
Though AI and chatbot deep learning are helping chatbots get smarter every day, they’re still best suited for handling repetitive tasks and answering simple questions that can become tiresome to human agents. By allowing chatbots to handle tasks such as inventory checks, order status, account balance, flight info, etc., you provide customers with fast, self-serve experiences and free up your agents to focus on more complicated customer journeys.
Chatbots are also unmatched at searching for data and can find information and answers much faster than human agents ever could. Let chatbots work behind the scenes to help your agents find what they need to quickly answer customer questions and watch your Average Handling Time (AHT) decline and your Call Center Customer Satisfaction (CSAT) soar.
Don't force automation for the sake of automation - use chatbots where they work best.
When a customer is upset, human agents are able to pick up on that right away and respond appropriately – your chatbot should do the same. Phrases like “I understand,” “I am here to help,” or “Let me get that information for you,” feel conversational and human and show the customer you care.
You should also use sentiment analysis to analyze the intensity of customer emotions so your chatbot can determine when to hand them over to a human agent. Triggers can include keywords like ‘manager’ or ‘human’ or phrases like ‘I already tried that!!!!’ When your chatbot does hand a customer over to a human agent, make sure it’s handing off the full conversation history so the customer doesn’t get further upset by being forced to start their journey again.
Though customers are getting more comfortable engaging with technology, it doesn’t mean you should trap them in conversations with chatbots. Design your customer journeys so chatbots take the conversation as far as possible, but make it easy for customers to connect with a human agent if they feel like they aren’t getting the answers they need. Keep track of where escalations occur so you can determine if there is a way to fix the journey to prevent it from happening again.
The look and feel of customer service has changed but its vital importance has not. Using the value chatbots provide, and the power to help your customer service needs, will redefine how supported your customers feel by your business.
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