Brands and businesses are racing to utilize bots to engage customers and boost sales. But bots vary in capabilities and features — the aspects that bring users value and make them beneficial for companies to implement within their platforms.
To the end user, chatbots all initially look the same. But as soon as the conversation begins, it becomes clear which deliver the goods and which don’t. From rule/menu-based bots to personalized ones, this bot breakdown will help you understand the main differences between chatbot technologies.
As the most basic chatbot solution out there, rule/menu-based bots can be used to automate basic actions and queries such as weather forecasts and store locations. They are similar to old IVR systems where you would be prompted to “press 1” for your current bill, “press 2” for last month’s bill, and so on.
Rule/menu-based bots can respond to specific triggers according to predefined rules and flows. There is a large selection of platforms available and aside from being fairly easy to build, they offer a user-friendly interface. They can map out conversation workflows and interact with existing back-end systems, such as product catalogs or CRM systems. But while simple to create and fast to deploy, these basic chatbots offer limited value to users when compared to other popular communication channels. Choosing a rule/menu-based bot rarely offers much value beyond that of the novelty effect.
Natural Language Understanding
Slightly “smarter” than the rule/menu-based chatbots, NLU (Natural Language Understanding) bots recognize and understand the intention of an end user, allowing for a more intuitive and conversational interaction.
Bots with NLU capabilities can “understand” specific words or phrases and execute the corresponding conversation scripts. They’re sophisticated enough to understand users as they’re typing, though the degree of sophistication varies. From simple phrase matching capabilities (which interprets meaning from keywords), to cognitive capabilities (which identifies meaning and extracts relevant information from sentences), the spectrum is wide. Though it’s a step above rule/menu-based bots, pre-set variables are hardly enough for a decent chatbot interaction.
Although a chatbot with NLU capabilities offers a better and more advanced user experience than that of the rule/menu-based chatbot, it still falls short in creating advanced conversations, limiting the value for users and businesses.
For a bot that can handle advanced conversations and help the end user accomplish more complex tasks, a contextual recognition bot, which is less rigid and more user-friendly than rule/menu based and NLU bots, should be the solution.
How does a contextual recognition bot work? Say it requests a user to input a physical mailing address for the shipping of a free sample. If instead of entering a valid address, the user asks a question such as, “Can you ship overseas?”. The bot will be able to contextualize the input and respond accordingly with, “We ship to the USA and Europe. Please enter a valid address for that region”, rather than get confused by the unexpected input.
Most bots have two things in common: they rely on rigid, pre-existing scripts and they lack personalization. Personalized bots, which is the direction the industry is heading in, takes what other bots offer and builds upon them.
Using AI and machine learning to provide a one-of-a-kind experience, these bots are much more flexible and diverse than the rest. They can draw information from previous conversations, interpret user intentions and sentiments, and adjust. The bot can present the user with new products and offer additional information based upon what is learned from the user’s choice of language, prior interactions, and external data sources. I.e., such as user gender, age, location and more. Say a flight comparison site has a bot. It could identify a user’s destinations of interest based on previous questions and then recommend relevant flights.
Another advantage of this type of bot is its ability to learn more about the user with each interaction, creating a continuous conversation. For example, a vitamin store bot will be able to ask the user to rate their satisfaction with products they’ve ordered. Using their purchasing patterns, the next time the customer returns to order vitamins, they’ll be greeted as an old friend and perhaps be asked if they want to reorder the Omega 3 vitamins they’ve ordered in the past. Because personalized bots use AI and machine learning to tailor conversations to specific users and their needs, greater engagement, satisfaction, and higher conversion rates are often the outcome.
Choosing the “One”
The chatbot world is vast and spans from basic to highly sophisticated tech. A well-selected chatbot has the ability to increase user engagement and satisfaction, boost sales, accelerate brand awareness and take a company to the next level.
So, which bot is the one for you?
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