What are the Differences Between Text and Voice Analytics?

Getting to know the customers is the essence of marketing. Listening to what people have to say about products in their own words is a valuable way of connecting with an audience.

Both voice and text analytic tools create data and actionable insights from actual remarks made by customers. There are advantages to each, and both provide significant value.

What Is Text Analytics?

Text analytics is an automated process that transforms unstructured user-generated content into quantitative data. Textual analysis tools perform sentiment analysis, which assigns an emotion to a tweet, social media update, or review and indicates how customers feel about a product or service.

The aim is to take random texts created online by consumers and translate them into data that can be measured, added to charts and graphs, and incorporated into business strategy.

The amount of unstructured data on the internet is staggering. Most people on the planet are now online, added to the number of social media updates or reviews generated a day, representing an astronomical amount of words, feelings, and opinions. Leveraging this sea of data in a way that can be analyzed and acted upon is the purpose of text analytics.

This data can be used to predict market trends and develop new products accordingly. Data derived directly from consumer statements about preferences can ensure marketing campaigns and promotions hit their mark.

Text analytics checks brand reputation, and the availability of massive amounts of data means that these impressions can be updated on an ongoing basis.

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What Is Voice Analytics

Voice analytics uses customer service calls as the raw material for creating quantitative, usable data. There are three different ways these calls can be used. If the conversation is transcribed, it can be analyzed by text analytics tools. If the call has not been transcribed but is still in its audio form, it can be analyzed with speech and voice analytics tools.

Speech analytics is related to voice analytics, but the two are not the same. Speech analytics pays close attention to what words are used, how they are used and the attitudes implied by the way some phrases are formed.

The repetition of certain words or saying one word adjacent to another word creates a different emphasis and uniquely communicates emotion.

For instance, according to speech analytics, it is significant how often the customer uses the word “you” to refer to the company or “I can’t.”

A customer may use the word “you” often to signal annoyance, and it can sound accusatory. Frequent use of “I can’t” may be interpreted to mean that the company should find more ways to make their products user-friendly or to give instructions more clearly. It clearly shows the customer is frustrated.

Voice analysis is often combined with speech analysis to detect attitude from the tone of voice, pauses, and other auditory clues. A vocal analytics tool monitors the tone, pitch, tempo, and volume.

For instance, if a caller says “terrific” in a sarcastic tone, voice analytics would pick up on this, whereas text analysis alone might not. Voice analytics tools work by comparing the voice with a database of intonations to interpret the clip’s emotional coloring.

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Voice analytics provides an extra layer of communication insight to the analysis. Just observe a conversation, and it is clear how much of it depends on what is not said verbally.

It is reminiscent of exercise actors saying the same dialogue but with different intonations to convey vastly different feelings. Voice analytics can catch these essential nuances by looking at just the text, and the words cannot.

What Is the Difference Between Text and Voice Analytics?

Text analytics can be similar to speech analytics regarding transcriptions from customer service calls. Speech or text analysis looks for the emotional coloring of the words used and pays attention to how they are used in context. This is like looking at the script of a play or movie without the actors delivering it.

Voice analytics, however, focuses on the way things are said rather than what is said. It deals with intonation, and the way feelings are expressed through the delivery of the words. It measures attitudes and feelings that are not spoken but are felt by those listening to the speaker.

What text and voice analytics have in common is that they both analyze a passage produced by a customer. This can be a review that the customer typed out or a call or audio message in which the customer expressed their feelings about the company or product in speech.

However, they use different means of arriving at conclusions about customer sentiment–one through text and the other with speech and voice.

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The disadvantage of voice analytics is that it can only cover a limited amount of user-generated data. Most social media postings, reviews, and comments are made with text. However, voice analytics provides a dimension not available with text analytics to create an in-depth view of how a customer reacts.

Text and voice analytics need not be mutually exclusive. Analyzing the vocal clues and delivery and the text of a call can fill in the gaps and create a fuller picture of the customer’s experience and sentiments. A comprehensive approach to both voice and text analysis is an effective strategy.

A Comprehensive Approach

Most user-generated content is in the form of text, which makes text analytics particularly important. However, for audio comments or customer service calls, voice analytics can highlight intonation and attitudes that may go unnoticed in text analytics. Incorporating voice and text analytics can enhance understanding of how customers feel about a brand.