Customer Conversations are no longer restricted to traditional voice calls due to the broad innovation and use of technology and continual connectivity. Contact centres have evolved from using Private Business Automated Exchanges (PABX) to Automated Call Distributors (ACD). However, challenges have also evolved along with technologies such as increasing call volumes, Agent Attrition, High Customer Churn, changing Customer preferences and tougher regulations and compliance. Manual call monitoring and analytics are woefully inadequate for today’s demands.
As a result, innovative voice technologies, predictive analytics, and Speech analytics have emerged. Speech analytics is a natural language processing technology that extracts and analyses voice data containing speech patterns and specific keyword mentions to provide in-depth insights for your company. This data is used to promote cross-sell and upsell opportunities, as well as a better customer experience, which leads to increased loyalty and happiness, which leads to increased profitability.
Speech analytics collects data that may be used to improve a variety of business operations, including sentiment scoring, keyword monitoring, and call resolution. It analyses vocal interactions using linguistic and semantic analysis to determine the subjects covered, their context, and the attitude of the speakers during the contact.
Speech analytics is a piece of software that turns unstructured spoken interactions into structured data. Speech analytics, also known as audio mining, employs a range of methods to convert recorded conversations or real-time audio streams into information and transcripts. An enterprise may categorise, search, analyse, and use this output in a variety of ways.
Phonetic indexing and search, as well as transcription, are two popular methods for analysing speech. They begin by recognising phonemes, which are the basic elements of speech. The automatic recognition of known words is the foundation of transcription, also known as Large-Vocabulary Continuous Speech Recognition (LVCSR) or speech-to-text. It enables data mining and natural language processing to uncover the fundamental causes of unknown difficulties automatically. Phonetic indexing and search, on the other hand, allows users to search for any word or phrase, regardless of whether it occurs in a dictionary (which is very useful for product names), and instantly monitor trends in call categories.
Detecting an emotion, in addition to words, expands the capabilities of classic speech analytics tools. Analyzing speech patterns for certain audio elements such as tempo, rhythm, stress, pitch, and tone to determine the speaker’s emotional state, which gives your company an accurate estimate of a customer’s present emotional condition.
The approaches described above may be used for both real-time and post-call speech analytics. Metadata obtained as a major output from post-call speech analytics is available in two formats: phonetic representations of conversations (phonetic engines) and a transcript of a discussion (LVCSR engines). It adds value by determining the cause of a customer’s call and is extremely successful at spotting new and emerging patterns. Contrary Real-time speech analytics analyse live interactions and provide managers, supervisors, and/or agents with actionable warnings or recommendations in real-time. Real-time speech analytics is being utilised to spot instances where agents are failing to meet regulatory rules, stick to the script, or follow standard operating procedures.
As a result, speech analytics has the power to change the outcome of a customer conversation, which may have a favourable impact on contact centre indices and CSAT scores while also lowering the churn rate. It may also be used to assess agent performance and limit the risk of customer migration, as well as provide 100 per cent call coverage for compliance. Hearing the customer’s speech has always been a vital KPI, which is made simpler with Speech analytics, which can be used as a cost-cutting and revenue-enhancing tool in your company operations. When it comes to Speech Analytics, one should take a consulting approach. We should investigate the business environment in-depth and do a fitment analysis before recommending the best method to meet the business demand. In terms of deployment methodologies and OEM partners/ engines to use, we should be adaptable. Keyword Spotting, Sentiment Analysis, Voice of Customer, Cross-sell/Upsell, Agent Productivity, Call Compliance, Fraud Detection, and Real-Time Analytics are just a few of the Speech Analytics services amongst the various techniques available to improve customer experience, and agent efficiency and general improvement of interactions and quality