“A Speech Analytics software is a technological tool that uses speech recognition, natural language processing and conversion to extract valuable information from verbal feedback received in the form of telephonic conversations, and video or audio messages.”

When talking about business intelligence, analytics, and customer feedback management, we cannot ignore the need to analyze verbal communication. Customer service agents are adept at interaction and problem resolution. However, they cannot make note of everything the customer says. This often leads to a superficial understanding of what the customers want from the company.

Speech Analytics Software helps businesses extract and use every detail from verbal messages, making it easier to gain insight.

How do Speech Analytics Software function?

Audio Recording (Data collection)

Speech analytics software is meant to deal with verbal communication. Hence, the primary data it requires is auditory. Calls received from customers to reach the customer service department are recorded and stored. While storing the data, the tool also adds metadata to each entry. The metadata consists of signifiers such as customer identity, service agent’s name, and time of call. Based on this metadata, the entries can be sorted by product, date, agent, or customer attributes.

Speech Recognition and Consistency Check

This process is crucial when analyzing speech. What makes speech analytics tools easy to use and efficient is the speech recognition technology. This process converts soundwaves into textual transcripts. The transcript is then edited by erasing blank spaces, eliminating redundancies and highlighting important information. Transcribing calls allows an organization to club this data with other text-based customer service data. Consistency of data format makes integration with text analytics and business intelligence tools easier.

Emotional Detection

Speech analytics tools are also trained to detect tonal and acoustic hints. They can tell when a customer is angry, agitated, frustrated, calm or excited and segment the call accordingly. They also detect whether a problem has been resolved, the responsiveness of the service agent and the emotional change by the end of call.

Linguistic pattern detection

Speech analytics aims at highlighting verbal patterns used by customers. For example, two people from different regions of the same country may speak with different accent and use different linguistic structures to convey the same meaning. Speech Analytics tools use machine learning and natural language processing to detect these parallelisms and linguistic variations. Customers can also be tagged as using specific words repetitively. These words are then highlighted as keywords that can be used to generate ad copy and personalized messaging.

Data Categorization, Visualization and Scoring

Based on several semantic and syntactic rules, entries are categorized and then compiled into graphs, charts and other visual representations that show the organization where they need to improve in customer service, product designs, marketing and advertising efforts and how they can gain a competitive advantage in the market. Each category is scored on a pre-determined scale to give the organization a comparative view of business operation as compared to KPIs and desired metrics.

It is also worthwhile to mention that the use cases of speech analytics are not limited to customer feedback management. Speech analytics tools are now being integrated with sentient artificial intelligence and is being experimentally utilized in cybersecurity management, customer experience personalization, interactive voice response, information retrieval and customer intelligence.