The conversational chatbot is likely to play a large part in the future of digital marketing. With continued growth in messaging applications like WhatsApp, WeChat and Facebook Messenger, there is clearly a consumer demand for machine-based communications. In a survey by Usabilla at the start of 2019, 54% of respondents said they would always choose a chatbot over a human customer service representative if it saved them 10 minutes. Consumers expect certain tasks not to require human intervention with 83% saying they would expect to check a bank balance without human interaction for example.
However, the challenge for businesses is that whilst chatbots fill the technology gap, 59% of consumers in a PWC survey felt that companies have lost touch with the human element of customer experience. Companies need to give customers an experience that fits their brand persona and goes beyond an efficient service. As far as the consumer is concerned, the chatbot experience need to feel like as if a real human is interacting with them.
Chatbots are evolving and becoming increasingly sophisticated in an attempt to simulate how humans converse. This is achieved by using applications of artificial intelligence (AI) such as machine learning (ML) and natural language processing (NLP). The algorithms built using these methods have the power to deliver a personalised experience by harnessing huge amounts of data from multiple sources, and thereby, uncovering behavioural patterns.
In theory, this is an amazing concept. A chatbot that uses data to know the user and presents the most applicable conversation for a personalised experience. What’s the catch? Machine learning needs data to operate and when launching a chatbot, generally, the data doesn’t exist yet. Take a practical example. A new user comes to your chatbot and says “Hi.” Your chatbot data only has the word “Hello” programmed as a greeting so it doesn’t know how it should respond. This is also the issue with NLP it that it needs to be able to comprehend what the user says before it can find the data for a response.
Data is key to a chatbot if you want it to be truly conversational. This article will explore how you can get that base data (aka training data) to train the chatbot, make sense of the data by efficient labelling and the broad methods to develop the chatbot.