In this blog, we show how robotic process automation and machine learning optimize chatbot performance, which advances support service and improves customer experience.
A poorly built chatbot can sour a customer on an entire company. Once, I tried to use Alexa to access an exclusive discount with a major pizza chain.
The chatbot gave poorly-worded instructions and couldn’t recognize my many attempts to advance, wiping out my six-pizza order three times. I was hangry (not a good feeling). Using the chatbot took far longer than it would have if I had just called the store. Because of this frustrating experience and several similar attempts, we don’t order from this chain anymore.
What Makes a Bad Chatbot
Many people don’t like chatbots because they rarely do what they are supposed to do: help customers quickly and efficiently. Instead, many virtual chats trigger automations with limited functionality that hinders their ability to address customers’ specific issues. When this happens, a chatbot transforms from the facilitator it’s intended to be into a gatekeeper that prevents customers from communicating with support staff directly.
Customers want chatbots to help them like support agents who understand a problem and fix it. However, when companies launch this feature, they may not have enough information on what customers need most or how they ask for it. As a result, customers spend more time wrestling with virtual chat communication and less time seeing their problems resolved.
Using Robotic Process Automation and Machine Learning to Build a Good Chatbot
Creating a chatbot application is not a one-time event, it’s an ongoing process. Companies should first analyze how it responds and resolves requests to create an even better tool. Then, they should evaluate how robotic process automation (RPA) and machine learning (ML) functionality improves the chatbots’ capabilities, facilitating successful customer experiences and allowing human teams to tackle complicated requests.
RPA offers a methodical approach to building a chatbot. A chatbot programmed with RPA carries out specific tasks the way a customer service agent clicks, types or interacts in response to information. For example, an RPA-embedded chatbot might offer customers a list of options and then perform a task with the data, whether that’s to make an appointment or request a return. When a customer clicks an option, the chatbot could ask another question or provide a form needed to complete the request.
By combining options that resolve most requests and fulfilling those requests with robotic process automation, chatbots have evolved from prescriptive programs with minimal functionality to applications that respond appropriately with robust self-service solutions.
Machine learning is a type of artificial intelligence (AI) that helps us problem solve by continuously learning from its performance. Unlike RPA, which is programmed to carry out tasks in a specific way, machine learning is flexible and intuitive, replicating methods rather than task-oriented actions. Chatbots with machine learning features train on Q&A – learning from what they get right and wrong – so their next response is even better.
A chatbot programmed with machine learning welcomes new information, which allows a customer to receive a response not unlike a human conversation. These chatbots offer customers a dynamic human-like experience, addressing requests with swift, natural responses while improving the functionality for the next customer.
The Three Features of a Good Chatbot
RPA and ML are only part of what makes a chatbot experience a good one. Customers are often looking for three key features:
1. A robust range of topics and responses
The best chatbots offer a full suite of capabilities that regularly grow. To improve the percentage of user inquiries they can accommodate, the best tools employ mechanisms that learn. Machine learning enables virtual chats to analyze input with and without responses. These chatbots may notice patterns developers didn’t initially program and adapt to new customer needs. They routinely analyze their interactions, updating offerings and optimizing performance.
2. Automation that addresses common user needs
A good chatbot offers automations that address common user needs. For example, my auto insurance provider mentioned chatbot functionality during my research on initiating a glass claim. I expected to enter a bunch of info and get a call back the next day. Five minutes later, I walked away with a claim and appointment with a vendor at a location of my choice. All account and vehicle details were pre-populated, allowing me to complete the full request in a few taps.
3. Natural language processing
OpenAI set the bar for chatbots with ChatGPT, producing fast, high-quality search results in conversational language. Chatbots including free-form text or search should follow Google’s or Microsoft’s example and combine natural language processing (NLP) with automation. NLP handles requests in everyday language, like slang, abbreviations, and typos, responding in kind. Natural language processing relies on machine learning to better understand requests and respond in the best way possible.
The Future of Chatbots
Despite the name chatbot, we aren’t looking for “chat.” We want these apps to find and launch functionality. These self-service virtual assistants provide effective resolution more quickly than we can get otherwise.
We typically think that customer service will resolve our issues better than a chatbot can. We don’t worry about people understanding what we’re trying to say or whether customer service can provide a solution.
However, human interaction isn’t perfect, as effectiveness varies greatly from one contact to the next. Long wait times deter people from calling customer support. Often immediate response times make the chatbots’ interaction super convenient.
Companies create the best chatbots with continuous process improvement in mind. Businesses monitor responses to validate that they’re best meeting customer needs successfully (and routinely). Chatbots with machine learning will constantly improve on their own.
Apps bolstered with RPA and machine learning offer a robust range of topics, automations for common tasks, and the natural language processing of requests. As a result, these virtual assistants will grow to become a preferred method of customer support, with efficient tools introducing marked improvements to the customer experience.