Unlocking the Power of Chatbots: How AI is Transforming Customer Service
“Please state your name.” “Please say or enter your account number.” “Please give a brief description of the reason for your call.” You know when you’re talking to a robot; that emotionless voice on the other end of the line, the canned responses to anything you say, the limits on the available options. Chatbots, they’re called. We see them in online chats with customer service as well, hopefully just before they hand us off to a real person. Like it or not, chatbots are here to stay. I’m not here to tell you that you should love them, I’m just going to give a rundown on how they work. We’re going to see more of them in the future, and that’s a reality.
Types of Customer Service Chatbot
The two main types of customer service chatbots are rule-based chatbots and AI-powered chatbots. They’re similar in some ways and very different in others.
Rule-based chatbots
We’re going to talk first about Rule-based chatbots. These are chatbots that require a set of predefined rules and scripted responses. It’s an “if-then” scenario, and there’s no option for the chatbot to offer anything that falls outside of that scenario. Here’s an example: “IF” the caller has the word “order” in their request, “THEN” navigate to the response “order options” and provide the list of customer-available options. The “if-then” scenario also includes an “else”, which means that if none of the words the customer supplies match anything in the chatbot’s vocabulary, then the chatbot performs the “else” action, such as advising the caller, “I’m sorry, none of those options are available. Would you like to try again?” In an “if-then” scenario, the only options are yes and no. There is no “maybe,” and there is no “close enough.” You and the chatbot each have to decide whether or not to move ahead by supplying an answer the chatbot can act on.
A rule-based chatbot has a vocabulary and a list of questions that it “knows.” Anything not specifically in that vocabulary can either be reasonably matched or not. Each item in the vocabulary has a predefined response to go with it, which is why you will sometimes get an answer to a different question than the one you asked. Some rule-based chatbots use a decision tree, where you will progress through the call based on your responses to questions. This is similar, but not identical, to the call trees where you’re instructed “Press 1 to speak with a nurse. Press 2 to leave a message.”
Rule-based chatbots don’t learn from the interactions, and they can only operate within their programming and vocabulary. They are what they are until an update changes their programming. They’re perfectly adequate when the questions and requirements are easily anticipated, but when the customer needs become more complex, they’re done.
AI-Powered chatbots
AI-powered chatbots leverage artificial intelligence, particularly Natural Language Processing (NLP) and machine learning (ML) to interpret the requests and respond to them. AP-powered chatbots don’t rely on pre-programmed rules, but instead understand and adapt to more complex and varied language. They learn over time and they improve their responses.
An AI-powered chatbot breaks down the user input into parts of speech – nouns, verbs, etc., and it then identifies the key elements of the sentence, like intent and people/places/things to understand what it is the customer is trying to do. Using that NLP, AI chatbots can maintain context throughout a conversation. This lets the chatbot “remember” what the customer said earlier. If the customer changes topics and then references a previous segment of the conversation, the chatbot can keep track of it.
Using machine learning, AI chatbots can improve their performance over time. The chatbot analyzes each conversation, identifying patterns in the behavior of the customer and improving future responses. AI chatbots are trained on huge datasets, and these datasets include a lot of different types of customer interactions so that the chatbot is exposed to a wide range of questions, even if it never answered them before. Furthermore, AI Chatbots are capable of processing feedback, like when a customer corrects a response or chooses an option more appropriate.
AI chatbots don’t require exact wording matching the vocabulary to process an answer. If a customer asked “Where’s my order,” or “Can you track my shipment,” the chatbot would realize that they’re both essentially the same question. This can reduce frustration for the customer. It also allows AI chatbots to manage more complex and dynamic questions, including multi-step questions, and even request further clarification from the customer.
Part of the real power in using an AI chatbot is its ability to integrate with customer databases and customer relationship management systems (CRMs). They have access to order status, account details, and transaction histories. As a result, they can process transactions (take a bill payment), book appointments (create a travel itinerary or schedule a doctor appointment), and other user interactions.
It’s easy to see the benefits of using an AI-powered chatbot instead of a rule-based chatbot, but they’re really expensive to develop, train, and maintain. They’re not perfect, either, and are subject to the same “hallucinations” we might find in ChatGPT, but perhaps less so due to the guardrails trainers can use on input material. It’s still only ever going to be as good as the training material it receives.
Benefits of Customer Service Chatbots
Love them or hate them, there are benefits to customer service chatbots. First, they’re faster than humans at responding to an incoming call. They don’t need to wait for an audible signal to answer the call. They’re also available around the clock and every day of the year. They don’t need bathroom breaks, rest, sleep, or meals, and they never get sick. Okay, they may crash and require a reboot, that’s as close to “sick” as a robot gets. They generally cost less to operate, due to not being a payroll-related expense. They never get into a bad mood and they always respond consistently, regardless of how a customer treats them. A chatbot handling a bank of 20 incoming lines requires one training session, period, and there’s no turnover requiring additional training sessions.
Limitations and Challenges
Despite how good chatbots can be at some things, they’re not a perfect solution, and they don’t handle every situation perfectly. Sometimes they have trouble understanding complex or ambiguous questions. Paradoxically, at times, that’s what chatbots do best. Just depends on their mood, I guess.
They can also cause frustration in customers if the chatbot can’t solve the problem or understand where to go next. If the chatbot isn’t well-programmed or well-trained to hand off to a human, the whole customer experience, and by extension, the brand, can be damaged. I’ve gotten into circular loops when I was trying to get some help on something, and there wasn’t any escape from it.
The Future of Customer Service Chatbots
As we see advancements in Natural Language Processing, we are also seeing chatbots getting better at understanding all of the complexities of human language. They can do more sensitive interpretation of context, tone, and sentiment. With that, chatbots can detect frustration, happiness, and other emotions, and adjust their responses to accommodate those emotions on a more personal and empathetic level.
The improvements in contextual awareness let chatbots hold longer conversations and recall past statements, even statements in previous conversations. They’ll be able to ask more probing questions for follow-up, rather than the generic “I don’t understand” that I hear way too often now.
We can also expect to see chatbots becoming more proactive in offering solutions, more emotionally intelligent in interactions with humans, and more seamlessly integrated into a broad range of evolving technologies. We’ll also still need to be proactive ourselves in checking the information we get, because there’s always going to be a danger of hallucinations.
Your Turn
I get it; chatbots are not humans. We can usually tell if we’re talking to one. But they’re not going away. But the prospect of a smaller business being able to offer round-the-clock customer service is going to be appealing to a lot of companies. Furthermore, with integrations with language interpreters, healthcare advice for non-native speakers could provide much-needed preventive and early intervention care, reducing the need for emergency room visits, and allowing healthcare providers to offer better service to underserved population segments. As much as I’d rather not use a chatbot, if my use can help further the technology to bring health and welfare to someone who might not otherwise have access to it, bring it on!
How about you — I’d love to hear funny stories from chatbot encounters, or if you have an insight I missed, drop it in the comments below!
My photography shops are https://www.oakwoodfineartphotography.com/ and https://oakwoodfineart.etsy.com, my merch shops are https://www.zazzle.com/store/south_fried_shop and https://society6.com/southernfriedyanqui.
Check out my New and Featured page – the latest photos and merch I’ve added to my shops! https://oakwoodexperience.com/new-and-featured/
Curious about safeguarding your digital life without getting lost in the technical weeds? Check out ‘Your Data, Your Devices, and You’—a straightforward guide to understanding and protecting your online presence. Perfect for those who love tech but not the jargon. Available now on Amazon:
https://www.amazon.com/Your-Data-Devices-Easy-Follow-ebook/dp/B0D5287NR3
