The Difference Between Rules-Based vs. Machine Learning Chatbots

You need to reschedule a cancelled flight. You have a question about a phone bill. You want to make a return. All of these situations have something in common: you can use a chatbot to address them. Chatbots can make or break customer experience for the better.

But, not all chatbots are created equally. Some are rules-based and some are powered by machine learning. Of course, companies can get quite sophisticated when setting up rules. So, let’s explore the differences between how the two work and why chatbots — no matter what kind — can be critical for effective customer communication.

Rules-Based vs. Machine Learning Chatbots

A rules-based solution allows brands to deliver experiences to specific segments of people based on the manual creation and manipulation of business rules. For instance, a brand may set up a chatbot rule that states “If a person mentions the word ‘return’, have the chatbot reply with our Q&A page on how to return a product”.

Machine learning-powered chatbots, on the other hand, analyze past conversation data to present the most relevant content or experience for each and every visitor in real-time. It works like this: If a customer has a question about a return because something doesn’t fit, the machine won’t provide a link to a return page because of a rule that has been set up. Instead, the chatbot may provide a specific answer based on past conversations in which similar customers specifically asked about “returns” and “fit”.

In short, the main difference comes down to a machine’s ability to understand what someone is saying and respond accordingly without help from a human. As specific as a rules-based chatbots can be, they are only as effective as a company’s ability to anticipate every user question and comment.

How Do I Know Which Is Better?

Rules-based isn’t all bad. ML isn’t all good. Ultimately, it depends on your needs. Rules-based software can be effective if you want to achieve something you’ve seen work before on a basic level. For example, in the past, anyone who asked us X question expected Y response. ML applications require more data to train, and as a result, can make more informed decisions about what types of behavior may lead to purchase or what types of chatbot answers are most likely to resonate with a customer in real-time.

Why Do Chatbots Matter?

According to HubSpot, about 71% of consumers use chatbots for a faster solution to problems online. The same study found 53% of people prefer a business with texting service enabled. These stats show that the majority of consumers prefer to solve their problems without having to interact with a human if possible. That said, intelligent chatbots can improve more than just the customer experience — they also help improve internal productivity. According to a Salesforce study, 64% of customer service agents with AI chatbots are able to spend most of their time solving complex problems, versus 50% of agents without AI chatbots. In fact, this efficiency increase explains why global chatbot market is expected to hit the billion-dollar mark before 2025. It implies a steady growth of 24.3%.

While chatbots are often associated with customer service, their usefulness extends well beyond it. A Verizon Ventures representative explains chatbots’ potential for transforming communication, “Chatbots represent a new trend in how people access information, make decisions, and communicate. We think that chatbots are the beginning of a new form of digital access, which centers on messaging. Messaging has become a huge component of how we interact with our devices, and how we stay connected with the people, businesses and day-to-day activities of life”.

Let’s focus a bit more on the role chatbots play in shaping messaging. From a marketing perspective, messaging means using the right words to explain your value to customers. Chatbots allow us to understand what types of topics are most important to customers. And most importantly, they also give brands direct insight into how consumers speak about their needs and expectations word-by-word.

Chatbots & U.S. Data Privacy Laws

As U.S. data privacy laws go into effect in 2020, having a direct line to customer feedback will be more important than ever. The safest type of data to collect will be zero-party (aka data that customers have voluntarily provided to a brand). Specifically, zero-party data includes feedback surveys and opt-out data. While chatbots don’t fit into the zero-party data category, introducing them into your communications strategy will ensure you have another channel from which to collect valuable insight.

Ultimately, chatbots are a convenient way to ensure customers have a positive experience with your brand. In particular, ML-powered chatbots are a great way to incorporate artificial intelligence into your customer engagement efforts and ultimately, make it easy to anticipate their needs. Interested in exploring how machine learning can improve your relationship with customers? Get in touch with one of our AI experts today.