It is important to first understand the types of customer feedback that you can collect. Customer feedback comes in two forms:
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.
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
But keep in mind:
They can use their own words to describe their experience or what’s on their mind
Unstructured feedback can be collected from a number of different sources, including (but not limited to):
Surveys and Comment Cards
Call Center Transcripts
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.
Get to know your key customer segments and compare their feedback
Identify emerging trends or concerns about your brand or your digital properties
Prioritize issues based on how much your customers are talking about it
The value of unstructured feedback lies in the fact that it is comprised of your customers’ 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:
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.
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 customers 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 customers’ feedback.
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.
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.
The use of 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 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.
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 analysis 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 and Voice of the Customer
Nearly half of the world’s population now has access to the internet, whereas less than 1 percent 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.
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.
At iperceptions, we believe in the value and power of the open-ended feedback that your customers 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 customers’ feedback to extract findings that are relevant to you.
Using advanced natural language processing and machine learning technology, iperceptions’ iper.text 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.
Plus, we offer the only 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 tool working for you in no time, meaning that we offer you the fastest time-to-insight with minimal effort on your part.