What is Text Analytics?

Text analytics is the automated analysis and mining of text. Applying Text Analytics to unstructured feedback (also known as open-ended feedback) provides the ability to extract insights about trends, patterns and customer sentiment for the purpose of identifying and prioritizing ways to optimize the customer experience.

Why is Text Analytics important?

To better explain the importance of Text Analytics, we must first understand the types of customer feedback that you can collect. Customer feedback comes in two forms:

Structured Feedback
Structured Feedback

Often collected using close-ended single-select or multi-select questions
that include a pre-populated list of answers. The respondent must select the answer(s)
that best represents their opinion. 

Unstructured Feedback

Unstructured Feedback

Collected using open-text questions / text fields.
The respondent must type their answer in the text field provided.

How you benefit from this feedback Why it's great for your customers
It provides a clear window into the minds of customers, and can help fill in the gaps and make your Structured Feedback 

more powerful.

But keep in mind:
It can take a lot of resources and time to continuously review, identify trends and extract insights from Unstructured Feedback.

They can use their own words to describe their experience or what’s on their mind

But keep in mind:
It can take more time to provide this type of feedback, compared to Structured Feedback.

 
Unstructured feedback can be collected from a number of different sources, including (but not limited to):

Surveys and Comment Cards
Surveys and Comment Cards

Social Media Twitter Facebook
Social Media 
(Twitter, Facebook)

Live Chat
Live Chat

Call Center Transcripts
Call Center Transcripts

Speech-To-Text 
Speech-To-Text

Customer Reviews
Customer Reviews

Email
Email

Due to its nature, it can take a lot of resources and time for customer-centric organizations to review and analyze unstructured feedback and, ultimately, get the most out of this feedback. In fact, according to Forrester, most companies analyze less than 25 percent of their unstructured data! 

This is where Text Analytics comes in, and why it is so important for customer-centric organizations to leverage it.

Text Analytics uses the power of AI to quickly and more efficiently mine your unstructured feedback so you can:

Get to know your key customer segments and compare their feedback

Get to know your key customer segments and compare their feedback

Identify emerging trends or concerns about your brand or your digital properties

Identify emerging trends or concerns about your brand or your digital properties

Prioritize issues based on how much your customers are talking about it

Prioritize issues based on how much your customers are talking about it

IPERCEPTIONS AI TEXT ANALYTICS

IPERCEPTIONS AI TEXT ANALYTICS

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Text Analytics Techniques

The value of unstructured feedback lies in the fact that it is comprised of your visitors’ own words, and so it provides the best glimpse into your customers’ minds. There are countless ways you can dig deeper into this feedback to extract as much value as possible from it.

Here is just some of the key technology that comes into play with Text Analytics: 

Artificial Intelligence (AI)

 

Artificial Intelligence (AI)

The ability for a computer system to perform tasks that would typically require human intelligence to perform. These tasks include, but are not limited to, speech recognition and decision-making. This is the essence that powers Text Analytics to process large quantities of text and automatically categorize it to simplify the analysis of your unstructured feedback.

Natural Language Processing (NLP)

 

Natural Language Processing (NLP)

A component of Artificial Intelligence (AI) that revolves around the ability for a computer program to review and understand human languages. Text Analytics uses Natural Language Processing to review your unstructured feedback to understand what visitors are talking about and categorize the feedback in various ways to allow you to easily identify and act on key trends and patterns identified in your visitors’ feedback.

Machine Learning (ML)

 

Machine Learning (ML)

A component of Artificial Intelligence (AI) that revolves around the ability for a computer system to automatically learn from past experiences, and automatically adjust itself to improve its performance without the need for manual programming. Text Analytics uses Machine Learning to determine how new pieces of text should be categorized based on previous text that has previously been processed, and also to determine whether the categories being used to classify these pieces of text should be refined based on patterns it identifies in the text.

Deep Learning (DL)

 

Deep Learning (DL)

A supervised, specialized subset of Machine Learning that revolves around the ability for a computer system to process data and leverage it to make decisions about other data. Deep Learning can be used in Text Analytics to better model language and better understand the context in the unstructured feedback in order to improve the accuracy of the automated analysis of the text.

Sentiment Analysis

 

Sentiment Analysis

The use of Natural Language Processing (NLP) and text analysis to automatically process and analyze pieces of text to determine whether the attitude expressed in the text was positive, negative or neutral. Sentiment Analysis is leveraged in Text Analysis as a great starting point for you to start analyzing your unstructured feedback and quickly identify key and emerging issues, opportunities for improvement, and sources of praise from your respondents.

Rule-Based Text Classification

 

Rule-Based Text Classification

The use of a keyword dictionary or lexicon to instruct a computer system how to categorize any new texts it is asked to process. In Text Analytics, rule-based text classification is used to automatically assign sentiments or topics to any text it processes. A pitfall of this approach is that it requires manual configuration and for you to ensure your keyword dictionaries or lexicons are frequently kept up to date in order for your text to be accurately categorized.

Approaches to Analyzing Unstructured Feedback

Due to the nature of unstructured feedback, it can often require a substantial amount of time and resources to review, scrutinize and analyze.

Traditionally, Text Analytics has been performed using manual configurations that involve using keyword dictionaries, which often requires needing to wait months before being able to get insights.

This has paved the way for text analytics tools powered by Artificial Intelligence (AI) that allow for faster and more efficient review and categorization of this feedback in real-time, providing the ability to obtain insights within a matter of hours.

 

Text Analytics - Manual Configuration flow chart                          Text Analytics - Artificial Intelligence flow chart              

 

 Text Analytics and Voice of the Customer

Nearly half of the world’s population now has access to the internet, whereas less than 1% had access in 1995.

With the advent of technology and people’s increased access to the internet, it is easier than ever for people to share their opinions with others, and control how broad or specific of an audience they want their opinions to reach.

Voice of the Customer (VoC) takes advantage of this opportunity. Using surveys and comment cards like the ones shown below, you can easily prompt everyone from your existing customers and prospects, to your heavy users and casual users, to provide feedback about their experience your website or mobile app. 

 Text Analytics - Collecting unstructured feedback using surveys        Text Analytics - Collecting unstructured feedback using comment cards

 People now have more opportunities than ever before to let a company know how they feel, and use their own words to do so. With customer feedback offering a treasure trove of valuable insights about the customer experience, and with companies having increased access to this feedback, naturally VoC research and Text Analytics go hand-in-hand.

 

iPerceptions AI Text Analytics

At iPerceptions, we believe in the value and power of the open-ended feedback that your visitors can provide. As a result, we pride ourselves in offering a full range of Voice of Customer tools that empower you to easily collect and dig deeper into your visitors’ feedback to extract findings that are relevant to you.

Using advanced natural language processing and machine learning technology, iPerceptions’ AI Text Analytics tool makes it easy for you to stay on top of emerging trends and discover the unexpected in the voice of your customers by continuously evaluating and organizing the unstructured feedback from all of your sources of customer feedback, from surveys to social media streams to Interactive Voice Response (IVR) transcripts.

Sentiment Analysis using AI Text Analytics      Identify trends and correlations with AI Text Analytics

Plus, we offer the only Text Analytics tool that automatically adapts itself to any business context, without needing to manual configure or customize anything for it to work. No more dealing with keyword dictionaries – our quick and simple setup will have our AI Text Analytics tool working for you in no time, meaning that we offer you the fastest time-to-insight with minimal effort on your part.