Every day of our lives, we generate an unimaginable amount of data. A simple call to a service center or interaction with a chatbot is data that can be store and later analyzed to help improve the customer experience. The method used to analyze all of this user input is NLP-Natural Language Processing. As humans, we are capable of abstract and complicated thinking; for this reason, we do not realize how nuanced our every day speech is. Understanding any sentence requires a good understanding of context and intonation (or punctuation, if written). Sarcasm for example, is almost undetectable by machines. As a result, the same word can have various different meanings depending on the surrounding words. All of these particularities are what make NLP a rather challenging process.
One way to improve a machine’s ability to understand free text is to continue to train it with labeled data. The Google Cloud Platform includes a natural language module that can be used to find specific elements, such as date or locations, from a large number of text inputs. Knowing that the algorithm needs continuous training in order to help the machine become familiar with all sorts of topics, vocabulary and syntax, Google offers users the ability to put in and evaluate their own data. This serves the user because they now have a custom NLP algorithm that they can use for their specific purpose. It is also beneficial to Google as the user just performed tests to better train its algorithm. Google also offers tools to analyze spoken word, such as the speech-to-text module, which can be paired with the NLP module, to understand the sentiment of a massive number of phone calls.
One of the main applications for voice NLP in marketing is to analyze customer service phone calls. With voice NLP, customer service agents can be evaluated based on the customers’ overall sentiment at the end of the discussion. This can be a faster and more objective customer satisfaction metric once the technology becomes mature. It is faster because the company doesn’t need to wait for the customer to fill in a survey. It is more objective as it happens in real time instead the customer having to remember their state of mind at a given point in the past. Moreover, it is more objective as it standardizes the different perceptions of state of mind of each customer. Voice NLP could also be helpful to customer service agents in deciding which question to ask next and what type of information the customer is looking for, using patterns determined from past customer conversations.