Mastering the Art of Unsupervised Learning: Techniques and Guidance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
In the competitive nature of today’s business, organisations must leverage on technology to gain more competitive advantage. Among those technologies that has proven to have enormous benefits to various enterprise across different industries is voice analytics.
It is an effective way to gauge customer sentiment, identify emotions, and analyse customer experience. All by analysing conversations with customers and clients.
Voice analytics is the process of analysing recorded conversations such as from video recording, digital conferencing, and phone conversations to uncover more information and insights.
Voice analytics is a product of natural language processing (NLP) and machine learning. It translates speech, identifies intent, recognises emotion through analysing audio patterns. Depending on organizational requirements, insights drawn from the platform can be straightforward or complex and multi-layered.
During its early stages, it was used to produce accurate transcripts, through the years it has evolved to a more sophisticated platform using artificial intelligence and machine learning to what it is capable to do today.
Artificial intelligence and machine learning were crucial in improving voice analytics process, providing the necessary capability to produce deeper understanding, better insights, and generate faster results.
Natural Language Processing or NLP is a computer function that enables it to process language the way humans process language. This is the function at the core of voice analytics. Through this function, computers and programs can now identify the true meaning of spoken words in conversations.
Machine Learning enables automation of voice analytics processes. This effective reduces time dedicated to manual transcription and processing. Algorithms can be created to detect in conversation phrases and keywords. This can be a product name, a person, or a word to help understand and give better context to the conversation. Through this ability, real-time insights and patterns can be identified. Using this, sales agents, for example, can change strategy to better close a sale, and a service agent can better assist callers through a better understanding of the assistance needed or the circumstances which may be surrounding the concerns.
One of the most important applications of AI in voice analytics is the ability to build predictive behaviour models or predictive analytics. By being able to predict customer behaviour organisations will have better control on conversations, using keywords to direct conversation, identify highly converting clients, improve customer service, and avoid churn.
Predictive models are developed by training the model with substantial numbers of recorded conversations and calculating speech parameters, then testing them and continually improving the model. Through this, it is able to identify the emotions of the speakers, personality traits, intent, and sentiments.
Voice analytics is utlised for a number of business functions such as:
Establish Sentiment Analysis
Understanding pitch, tone, and pace, combined with a choice of words. The program can predict customer sentiment and identify emotions. Users can then filter and categorise calls based on their sentiment – positive, neutral, and negative and take necessary corrective measures.
Extract keywords used in conversations, and filter out keywords – product name, feeling, and other critical words which is important in understanding the context of the conversation. This will also help the organisation create a strategy for calls that mention certain keywords providing better direction for sales and customer service teams.
Develop Topic Analysis
This function allows identification of keywords and time stamping. A useful function that helps organisations develop a topic analysis by categorising and organising topics. Armed with these data, insights based on pattern can be extracted and used to develop strategy and develop prediction models.
All these features are helping develop voice analytics. And this tool is already in use in many industries to help them improve their functions and outcomes.
Mortgage and Financing
Mortgage companies, loan institutions and refinancing companies can use voice analytics to efficiently identify top-converting prospects through the help of machine learning.
Insurance companies armed with insights generated from voice analytics and machine learning are able to improve conversion rate. This is because they can lead the conversation better using insights and predictive analytics. Using client’s needs and emotions to draw them in and close sales.
Call centers use voice analytics to improve training and customer experience through an understanding of keywords to be used with a higher success rates in closing deals. In cases where customers are unhappy, they can lead the conversation and turn the conversation around and improving resolution rate.
Voice analytics has a number of potential benefits and use cases for different enterprise. With continued technological advancement, it can be more helpful to businesses, more than ever, to deliver better outcomes.
If your business is ready to explore how voice analytics can do for your organisation, contact AICG.