Speech Analytics vs Voice Analytics
Businesses today have access to more consumer data than ever before, especially through their customer support and service centers. The essence lies in understanding the optimal way of extraction and utilization of that data. Speech analytics and voice analytics are two approaches to call analytics that can be used for the same function. Even though they may both analyze phone conversations between customer support representatives and customers in order to reveal customer insights, their mechanisms are quite distinct. Discerning these differences is crucial to determine which solution of call analytics is ideal. Moreover, both the methods are used by customer support and service centers to gather information about the market performance of products. However, speech analytics and voice analytics are very different tools. The operating principles of analyzing used are very diverse.
Call Analytics refers to the collection, measurement, analysis, and reporting of data collected over phone calls. Retailers and brands can use the insights gained from call analysis to optimize call handling and marketing campaigns. Call analytics also allow for viewing and analysis of both the macro and micro phone traffic patterns and sort the collected data into informative call reports. There are two different methods of call analysis, i.e speech analysis, and voice analysis.
Speech analytics analyzes the spoken content of a phone conversation by analyzing what is spoken between support representatives and the customers, and the context of the conversation. It does so by using phonetic indexing or converting speech to text for the organization of the content. Speech analytics makes it feasible to search and locate the speech of a representative or a customer and their response to each other. The context of the conversation is divulged by the method of isolating specific words and phrases in proximity to one another.For example, if a customer calls a business to ask about the shipment of an order and when it is expected to arrive, then the execution of a search for the words “order” and “shipment” of the customer with close proximity to a search of the customer care representative using the word “shipment,” crucial information such as the reason for the customer’s call as well as whether the customer received a satisfactory response may be determined.Important factors such as keywords and syllables based on a frame of reference searches set up by the business play a vital role in speech analytics. Unveiling the most common phrases and words used by customers during such a conversation enables speech analytics to give businesses better insights into the latest trends. This, in turn, prepares the business to be able to create informed marketing strategies and make decisions that provide the customers with the best experience possible.
In contrast to speech analytics, which focuses on the words and phrases used in an interaction between the representatives and the customers, voice analytics targets the intonation of how it was spoken. Voice analytics work by analyzing the audio patterns for vocal elements such as the tone, pitch, tempo, rhythm, and syllable stress, to gauge emotional quotient. This provides businesses with a deeper knowledge of the mood of a customer.For example, if a customer uses the word “amazing”, voice analytics can be used for the detection of cues, such as anger or sarcasm, which utterly changes the meaning of the word. It is crucial to understand the demeanor of a customer in order to be able to provide them with a satisfactory experience.
After the assimilation of raw vocal data, it is run against an emotional voice database, comparing factors generated by the voice analytics system with known factors associated with emotions such as anger, happiness, fear, and sadness, to correctly identify and classify the emotional state of the customer. In essence, voice analytics captures the emotional aspect of speech in a conversation.
Speech analytics is essential in cases where specific keywords and phrases show a strong indication of potential sales opportunities as well as situations such as cancellation of orders. Speech analytics determines the needs of a customer by the use of keyword detection whereas voice analytics, saves time and labor by guessing the meaning of words and phrases used in a conversation.
Furthermore, by analyzing a customer’s response and emotional state, voice analytics can help predict future behaviors. This is used in second call targeting by focusing on customers likely to make similar purchases. Thus, differentiating between what has been said with respect to how it was said can provide with different kinds of information, that may be used to improve the quality of operations in various ways.