At their foundation, chatbots are computer programs designed to simulate human conversation. Meanwhile, machine learning is a type of artificial intelligence that allows computer programs to learn and become more complex without explicit programming. When put together, these two technologies offer the promise of chatbots that can learn dynamically, mimicking human conversation more closely than ever—but do ML chatbots deliver on the hype?
What are machine learning chatbots?
Originally, chatbots were scripted programs designed to give rote answers in response to specific queries. These scripted chatbots couldn’t really deviate from their programmed responses, which meant more unique queries had to be referred to a live customer service representative. This limited the chatbot’s usefulness, created duplicate work, increased operating expenses, and frustrated customers who just wanted a resolution to their problems.
The advent of artificial intelligence, and in particular machine learning, paved the way for new advances to be made in chatbot technology. Incorporating machine learning into chatbot programs meant that the chatbots could learn over time as they answered more and more questions without being explicitly programmed to do so.
This helps the chatbots answer more dynamic queries rather than being confined to whatever database they were originally programmed with, allowing them to more closely mimic human interactions and increasing their usefulness.
How can machine learning chatbots be used for business?
There are many different potential applications for machine learning chatbots, with the most obvious one being customer service. These chatbots can answer simple questions and help customers navigate company websites to find the information they need.
For e-commerce specifically, chatbots can be used as another marketing channel to drive the sale of goods and services, like a much more sophisticated pop-up banner. Chatbots can also be used to provide dynamic, personalized recommendations for customers who are actively shopping on your website to drive more sales. In a similar vein, chatbots can be integrated with social media platforms to proactively engage with potential customers where they are instead of waiting for them to come to your website.
Chatbots can also be integrated with a website, desktop, and/or mobile application to guide users through specific activities and tutorials. In this function, they serve as entry-level tech support and allow the human tech support team to focus on more complex issues.
Chatbots can also be embedded with customer and employee onboarding processes to automate more rote tasks such as inputting personal information. Chatbots can also be used to run interactive surveys and collect valuable customer or employee data in a dynamic way versus static surveys that display the same questions to everyone.
Read more on Datamation: Machine Learning (ML) Business Use Cases
Benefits and challenges of machine learning chatbots
On the benefits side, machine learning chatbots aren’t limited by time zones and can be programmed to speak multiple languages. This solves some of the limitations of using only human customer service reps.
Chatbots also respond right away without wait lines, which is a huge plus for understaffed customer service departments. On a related note, chatbots are often more cost-effective than employing people around the world and around the clock.
Some customers, especially Millennials and Gen Z demographics, often prefer to use a chatbot as opposed to waiting to talk to a human over the phone. However, other customers are resistant to talking to a chatbot, and being prompted to talk to a bot first can make them frustrated or even angry.
Another challenge is that machine learning is still in its infancy relative to other technologies, and it has a long way to go. Even the most sophisticated machine learning chatbots can’t match the improvisation of an actual human, especially one with a lot of experience with the product or service in question. Sometimes chatbots will provide the wrong answer or direct customers to the wrong place, especially in the early days before the program is fully trained, causing more frustration and potentially even leading to the loss of customers.
It can also take a while to train the chatbot until it functions as it’s supposed to, so it may not be an out-of-the-box solution for all companies. The greater the complexity of the chatbot, the more it usually costs, so it takes a real investment of both money and time to make the most of the technology’s potential.
The future of machine learning chatbots
While machine learning and artificial intelligence offer a lot of promise for chatbots, the technology has a ways to go before it can fully rival the work of a human customer service agent or a tech support expert. With so many experts working in the machine learning and artificial intelligence spaces, we’re sure to see machine learning chatbots advance significantly in the coming years.
Read next: 5 of the Best Machine Learning Tools