There's no doubt that conversational technology is powering a major shift in the way business and customers interact. It enables personalized, differentiated experiences; ones that were previously impossible to achieve at scale.
There is, however, confusion when it comes to selecting the right technology for the job. Recent growth in the conversational UX market has led to an increase in products and services, making the decision even harder.
In this article, we'll take a look at two broad categories of conversational experiences:
- those that use conversational AI
- those that use structured flows
It's important to understand these differences in the early stages of developing your conversational strategy. Doing so could help you save a lot of wasted time and money.
What is conversational AI?
Conversational AI or conversational Artificial Intelligence uses machine learning to understand the context and intent of a question in order to give a response.
Conversational AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms. This allows the software to learn automatically from patterns or features in the data.
There different subsets of AI, like deep learning, natural language processing (NLP), and natural language understanding (NLU). They all need to be trained, validated a tested.
The drawbacks of conversational AI
Using Artificial Intelligence correctly takes a lot of time and resources, and while it complements a lot of use cases, it's usually unnecessary to make them work well.
To develop usable AI, companies need access to a massive amount of quality data. Training data is essential to the development of any machine learning model, and it's something that companies don't have enough of.
Unless you're a company like Apple and want to develop a tool like Siri, centering your customer interactions around AI probably doesn't make sense for your use case.
What are structured flows?
Structured flows don't rely on any artificial intelligence. Instead, they use conditional statements to guide them. These frameworks are also referred to as decision-trees or rule-based bots.
With structured flows, you map out conversational a flow chart. You do this in anticipation of what a customer might ask, and how the software should respond.
Structured flows can use very simple or complicated rules. They can’t, however, answer any questions outside of the defined rules. Structured flows do not learn through interactions. Also, they only perform and work with the scenarios you train them for.
Advantages of structured flows
Structured flows result in conversations with guard rails. On one hand, that means that the conversations are limited specifically to what you design them to handle.
But on the other hand, those limitations make it clear to the user what they can and can't do. You can better guarantee the experience they will deliver.
Some other advantages of a rule-based chatbot are that they:
- are faster to train (less expensive)
- integrate easily with legacy systems through APIs
- streamline the handover to a human agent
- are highly accountable and secure
- can include interactive elements and media
- are not restricted to text interactions
The intersection of apps and structured flows
One benefit of structured flows that is worth looking at separately is the ability to include interactive elements and media. Conversational experiences that combine graphic user interfaces (like apps) and structured flows are much more intuitive.
When you need to build a complex user flow that involves several steps, rich UI elements are critical. Menus, buttons, and dialogs make it easier to create a structured flow, seamlessly guiding people towards a resolution.
The intersection of structured flows and graphic elements is often referred to as conversational apps. Conversational apps take the immediate, personal, and conversational style of text-based interfaces and combine elements of graphic interfaces like websites and apps.
Which one is right for you?
The best conversational strategies start with well-defined goals. Usually, our clients find that rule-based bots are flexible enough to handle their use cases.
AI works well for companies that have a lot of data, technological resources, and broad use cases. But, it takes time to get AI right.
For that reason, we recommend starting with minimum viable automation (MVA). You will use your MVA to gather information on how your customers want to use your conversational tool and learn from its limitations.
You can always invest in AI for the next phase if you decide that it’s right for you.
Contact the experts at Hubtype to get the conversation going.