Most of you may be already familiar with popular applications of conversational analytics even if you’re not aware of the specifics. Everyone has an in-built voice assistant in their smartphone and have used it at some point or the other, for the novelty if nothing else. Some of you may even rely on it. Let’s take an in-depth look at how these systems work.
NLP or Natural language processing is a branch of Artificial Intelligence and Machine Learning that enables interactions between a computer and a human being using natural language or spoken word. When looking at the business side NLP allows access to tailored reports and condensed insights in real-time for marketing, forecasting, product performance, and other useful information. What do all of the above-mentioned data sources have in common? They are extremely voluminous. Conversational Analytics finds usable information from this vast collection.
Most organizations are limited by GDPR and CCPA regulations that prevent them from exploiting third party sources, this is a good thing as most of that data is illegally obtained. Companies still use them but conversational analytics can reduce their impact and make them think about the costs versus benefits. Companies can use their own data sets to leverage opportunities in the market. In the internal structure, embedded voice reporting can be used to understand the company’s financial situation, market dominance, and HR efficiency. This allows top-level management in making the right decisions at the right time.
Viability of Conversational Analytics wholly depends on the amount of data that is available to you, from repeated conversations with voice assistants and chatbots. A vast amount of data can be produced by implementing a good NLP and this can be improved even further by implementing deep learning algorithms to produce the most mathematically efficient information in one click, or “Ok Google”. Data Virtualisation is another important aspect of conversational analytics, with this, the vast amount of data can be analyzed without information about formats and physical location. This gives a singular customer-centric view of the data.
Currently, Voice-assistants behave as a lay man’s JARVIS, in reality it’s not at that level yet. With more advancements in conversational analytics, a computer’s voice and speech patterns will eventually be indistinguishable from a human’s, this will help in getting more information to improve your business while giving your customers a truly unique experience.
