Customer feedback data can be incredibly useful to businesses, as it contains the key to understanding the concerns and wishes of current and prospective customers. However, in a modern business environment, data travels at an astounding speed. It is easy to become overwhelmed by an unending torrent of information coming from several sources at once. This poses a challenge for any company looking to capitalize on unstructured data.
What is customer feedback analysis?
Customer feedback analysis is the gathering of valuable insights from data provided by consumers of a product or service. Customer feedback data can come from a wide variety of sources including customer surveys, support tickets, online reviews, social media content, e-mail marketing campaigns, and much more.
Why is it important to analyze feedback?
The analysis of customer feedback data is essential to offer the best user experience. Listening to your customers guarantees that you understand their needs and desires. Thanks to customer feedback analysis, companies can devise services and marketing strategies based on verifiable knowledge of what customers want.
What type of data is feedback?
Feedback data is any type of customer information that holds an inherent value. With the help of artificial intelligence solutions, even a data set that seems incomprehensive to the naked eye can produce actionable and functional information.
There are several ways to categorize feedback. When gathering information from consumers, two types of customer feedback can be acquired: quantitative feedback and qualitative feedback.
Quantitative data is relatively easy to measure and understand because it represents rigid values. For instance, the results of a multiple-choice questionnaire are quantitative, as they represent the exact number of people that have made a particular choice. This data can be used to gauge customer service performance as well as the success of products and campaigns
While more complex than quantitative data, qualitative data holds the advantage of being descriptive. The ideas and opinions of customers can’t be quantified numerically, but through the use of AI-powered customer feedback analysis techniques, they can still be studied. Automated customer feedback analysis tools possess machine learning technology capable of understanding the emotions conveyed in written text.
What are the different methods you can use to get feedback?
Manual data analysis
The most rudimentary method of conducting customer feedback analysis is to do it by hand. An individual can rely on his common sense and an inherent understanding of language to perform research on his own. Nonetheless, modern data environments tend to not be user-friendly. It might be faster to find a needle in a haystack than a specific value in a large data set.
Manual data analysis is prone to several potential issues. For instance, many people have natural biases that can cause them to mislabel data without consciously noticing it. Moreover, people dealing with the repetitive task of cataloging fragments of data one by one are liable to losing their focus and missing out on important details. Manual data analysis can’t be scaled, as the amount of data that can be analyzed is limited to the analyst’s time and capacity.
Natural language processing
Natural language processing is a novel textual analysis method that combines linguistic analysis techniques, artificial intelligence, and computer science. The purpose of natural language processing is to teach computers how human communication works and thereby strengthen the relationship between people and computers.
The algorithms that drive natural language processing strategies are able to learn and adapt. These algorithms can grasp the nuances in human speech, helping them identify key concepts that can be easily ignored by manual analysts and inferior software. Natural language processing is useful to precisely comprehend user sentiment within large volumes of data.
The feedback analysis capabilities of natural language processing software can report on the overall emotions of users based on the type of feedback it has received. A fragment of data can produce neutral, negative, or positive feedback, which can be used by decision makers to understand the success of their strategies and plan for the future accordingly.
Sentiment analysis is one of the dominant data analysis techniques. Its wide range of applications makes it incredibly useful not only for customer feedback analysis but also when running all kinds of analytic procedures across a vast range of fields and disciplines. Sentiment analysis is also known as opinion mining.
This is the main method used to analyze how a customer base feels towards a brand. Moreover, companies can also gauge the appeal of their products, services, events, and campaigns. Customer sentiment can be gathered from any source that allows customers to voice their opinions, including support tickets, e-mails, comments on social media pages, and more.
When looking at public reviews, for example, sentiment analysis software can gather people’s opinions and tell the user what feelings their audience is trying to transmit. While positive feedback is always anticipated, negative reviews also hold their own value. By assessing negative feedback, decision makers can learn from their mistakes and redesign their strategies.
Information extraction is a data management strategy that automates the retrieval of specific information found within a data set. The user of an information extraction tool only needs to specify a selected topic to gather information from and artificial intelligence will do all the heavy lifting. The data sources targeted by information extraction can be both external and internal, giving users a comprehensive view of the themes they wish to learn more about.
The competencies of information extraction make it extremely useful for data-driven activities of any kind of complexity. For instance, your email app of choice may use information extraction when adding the data from a message to your calendar. Taking it up a notch, information extraction can also help structure legal documents, medical records, social media interactions, online news, corporate reports, and many other forms of textual data.
A company composed of several departments or branches can apply information extraction solutions to monitor, observe, gather, and make summaries of the data generated from all their sources. By integrating visual feedback tools, information extraction software can instantaneously produce complete and functional visual reports and documents ready to be presented to employees, managers, partners, shareholders, and customers.
In the past, technology that was able to understand, learn, and act upon unknown information existed only in the realm of science fiction. Thanks to the latest advancements in computer science, however, machine learning has become a reality.
Also known as deep learning, this novel technique allows an AI solution to increase its efficiency by studying the information it is given. The AI can draw conclusions on its own, without the guidance or input of a human operator. Not only is this skill set useful in all kinds of business and institutional operations, but by performing analysis on current trends, machine learning algorithms are also able to make predictions about future events.
Data classification is a series of particles used to organize information into separate categories. Artificial intelligence can reorganize the elements and variables within a data set in predetermined groups or classes. In addition, machine learning integration allows the software to be more accurate and devise new ways to reorganize content.
Data classification algorithms have become commonplace in our day to day, as they are present in the AI behind email folders and search queries. Thanks to data classification, organizations of all types and sizes can maintain order as they operate.
The following are some of the most popular methods to categorize feedback used by data scientists:
- User-based categorization: Performed without the aid of artificial intelligence, this method sees an individual using their own skill set and expertise to reorganize content. As the use of intelligent technology becomes the global standard, this method is falling out of fashion.
- Content-based categorization: When taking a content-centered approach, the AI will look for sensitive information it is able to inspect and interpret.
- Context-based categorization: This method will give the AI a set of instructions, leaving it to search for relevant data based on context. Gathering contextual information requires to use of a classification model, which may tell the algorithm to organize data based on time, location, purpose, or other rules.
- Category hierarchy: Using a level-based structure, this method uses a decision tree-like approach to organize data, helping users observe information in functional sub-categories.
- Category drill-down: Using a context-based strategy, a category drill-down allows users to explore particular fragments of data. This can be used to learn where the data comes from, who produced it, and under what circumstances.
Which analytics method is best used for analyzing customer feedback?
The most effective method for analyzing customer feedback is to use text analytics software with machine learning and natural language processing capabilities. This type of feedback analysis tool can be used to run analytic procedures on a massive scale and automatically produce clear and high-quality results.
In contrast to manual feedback analysis, analytic processes run by artificial intelligence are free from biases and mistakes. In addition, the visual feedback tools found in state-of-the-art customer feedback management solutions create appealing reports instantaneously. Instead of receiving a dull spreadsheet, users of the software enjoy automatically produced, visually stunning reports they can easily share with their colleagues.
How to get actionable insights from your customer feedback
To get the best out of your customer data, it is indispensable to implement customer experience management software. Analyzing data with Semeon’s innovative analytics platform allows businesses to know their customers’ opinions about their brand and what to do in order to maintain or improve it. It is the ideal tool for complaint management, customer experience and competitive benchmarking.
Semeon’s customer feedback analysis software can help you drill down into specific data categories, letting you know what drives your customers depending on their demographic data, location, preferences, and much more. Armed with this knowledge, companies can provide better customer service, increase customer loyalty, and devise new and exciting strategies.