This is where most applications of NLP struggle, and not just chatbots. Any system or application that relies upon a machine’s ability to parse human speech is likely to struggle with the complexities inherent in elements of speech such as metaphors and similes. Despite these considerable limitations, chatbots are becoming increasingly sophisticated, responsive, and more “natural.”
With all of this taking place in the world of marketing, as we speak (err, read), there are sure to sprout Facebook Messenger Chatbot Tools claiming to make wonders happen. What is needed to be understood here that one can actually build a chatbot from within the platform and also find some plug-ins to embed? But it comes with its zillion complications. Talking about the sprouting tools, some of them are excellent pre-built tools that can actually make things happen for you. Let’s take a look at some of the best in the industry that comes with the perk of having to require no coding knowledge.
Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatbots also combine real-time learning with evolutionary algorithms that optimise their ability to communicate based on each conversation held. Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval.
Marketer’s Take: The bot was surprisingly effective yet fell short several times when queries like “Show me Blue Jeans” came with a canned bot response, “Sorry, I didn't find any products for this criteria.” Yet I know they sell “blue jeans”. Still, the bot was one of the best eCommerce bots I’ve seen on the platform thus far, and marketers should study it.