When chatbots first became commercially accessible, companies big and small embraced them with open arms.
Have a robot handle easy customer service questions in seconds?
The problem was, these early chatbots were less C-3PO and more an annoying barrier to an actual human.
Lets take a look at where chatbots go wrong and how AI can help.
It’s free, every week, in your inbox.
may be replaced by howzit?
10 pounds becomes 10 quid.
Its not until youve spent time around locals that you really learn how to speak a language.
Early bots were a lot like new language learners.
Their knowledge of human language was limited to a preloaded set of questions and responses.
Forget about slang or nuance, even saying hi instead of hello couldthrow them off.
Natural Language Processing (NLP) enables your chatbots to level up their human language skills.
ChatGPT is a good example of an AI tool that leverages NLP to better understand users queries.
On top of that, the more NLP chatbots interact with customers, the more they learn.
This means that over time theyre able to provide more accurate and relevant responses based on past interactions.
Enhanced communication
So, AI-enhanced chatbots can bang out the talk.
But can they speak the language?
Voice recognition and speech-to-text conversion are truly putting the chat in chatbot.
The emotional component
The purpose of a chatbot is in the name to chat.
By definition, they should be conversational.
But chatting isnt just about words its about understanding emotion and nuance.
Which makes it all the more difficult for chatbots to understand what weactuallymean.
Through machine learning techniques, modern chatbots can be trained to recognise the underlying intent behind messages.
Because were only human, we tend to use emotive language, even if we communicate with bots.
Predictive analytics might help you secure a sneaky little upsell too.
By analysing customers past shopping data, predictive analytics tools can make personalised product recommendations for individual customers.
