With so many conversational AI platforms out there, how can you find the one that’s best for your business?
The digital customer service landscape is littered with countless chatbot vendors vying for attention. The truth is, it can be overwhelming for a company to settle on a conversational AI solution that not only delivers excellent results but offers a guaranteed return on the level of investment put into what can oftentimes be a lengthy and expensive process.
Choosing the wrong vendor can turn your virtual agent into a source of frustration for customers, throwing them into infinite loops of “I don’t understand” responses. That damages trust, satisfaction and loyalty toward your brand. Luckily, it is easy to steer clear of sub-par technology by just knowing the difference between three key differentiators for virtual assistant platforms (VAP). Gartner identifies these differentiators as they relate to each platform’s main architectural elements - computational linguistics, machine learning and rule-based programming.
In this article, we outline each approach and discuss its benefits, challenges and where boost.ai fits in.
This is a common architecture that most simple chatbot solutions are built on. By identifying specific keywords and other language markers, a rule-based chatbot can trigger a set of pre-defined responses. They are not, however, generally considered to be artificially intelligent and can easily be tripped up if a customer request falls outside of the limited scope in which they are programmed to respond.
While it is true that rule-based solutions are relatively easy to implement (requiring no data scientists or technical personnel) they do not have the ability to scale well. This means that companies may often start with this solution but then find that as their need for automation increases, this method is no longer able to satisfy their requirements - making it necessary to scrap the project and start from the ground up with a machine learning or computational linguistics approach.
The computational linguistics approach tackles customer requests received by a virtual agent at various linguistic levels. This makes it especially flexible when dealing with multiple different languages allowing it to tackle compounded words, phrases and complete sentences, competently analyzing them for a customer’s intent.
Virtual agents built on this architecture can be quickly adapted to new languages and the compact size of the machine learning model used means they may require less compute and storage resources. However, because of the enormous amount of variation in the types of questions a virtual agent receives from customers, this method has the potential to become unruly, requiring a lot of effort to be spent on accounting for all the possible different ways a customer might ask for something.
The most advanced forms of artificially intelligent virtual agents are built using machine learning and data science methods. Conversational AI based on this approach uses a large set of training data that enables one or many deep neural network algorithms to classify intents. This method is most suited for deployment in the cloud due to the level of processing power necessary.
While the initial push to get a virtual agent built on this architecture off the ground can require the supervision of specially-trained personnel, the end result (if well-executed) is a model that learns and adapts to customer requests with the highest level of accuracy of the three methods.
Our solution falls squarely into the ‘machine learning’ category but with a number of key advantages. We negate the need for a lengthy implementation process by offering pre-built, industry-specific content that makes deploying a virtual agent in banking, insurance or telco something that can be easily turned around in a matter of weeks, not months.
Our natural language understanding (NLU) is some of the best in the world, allowing our conversational AI to handle customer requests in multiple languages simultaneously and at scale. We then layer our proprietary ASU technology on top of this for an unprecedented level of understanding that makes easy work of even the most complex queries while reducing the chances of false positives to an absolute minimum.
Boost.ai understands the need for businesses to offer a technologically superior customer experience today - but we also get that it shouldn’t need to be a daunting task. Customer support staff can be upskilled into AI Trainers via an integrated e-learning platform that leverages their existing skills set in improving and maintaining a company’s conversational AI via user-friendly and intuitive software.