Table of Contents[Hide][Show]
Your company has access to several data sources containing input from clients, consumers, workers, vendors, and others. This unstructured data holds the key to reaching your customer experience goals, but successfully evaluating it necessitates specialist solutions.
Text analytics technology presents an automated technique for analyzing and displaying unstructured text data for qualitative measures. Consider receiving actionable information from every social media post, email, chat message, issue ticket, and survey.
Text analytics enables your company to discover more about what customers are saying, thinking, and feeling as they interact with your goods and services.
In this post, we will look closely at text analytics, how it works, the differences between text analytics and text mining, as well as its benefits, use cases, challenges, and much more.
So, what is text analytics?
Text analytics is a method for deriving meaning from unstructured data, such as written communications and text, in order to gauge factors like user feedback, consumer opinions, product ratings, and other metrics.
It’s a method for transforming a lot of unstructured data into something that can be studied, in other words.
When analyzing articles, tweets, social media posts, reviews, comments, and other types of writing, many firms employ text analytics to apply machine learning techniques and algorithms to extract meaning and gather information.
Types of Text Analytics
Not all text analytics are created equal. Text analytics, like the broader realm of business analytics, can be divided into several areas based on function and outcomes. Text analytics techniques are usually classified into three groups:
Descriptive Analytics
Text analytics procedures in this area center around reporting. Data is taken from unstructured text, given logical form, and examined for trends. Topics and basic themes can be linked together to offer a clearer view of overall user mood, shopping patterns, and more over time.
Predictive Analytics
Predictive analytics focuses on projecting future occurrences. Unstructured material is captured and analyzed in predictive text analytics with this end result in mind.
This form of analytics assists firms in producing accurate projections for inventory management, purchasing behavior, and even risk avoidance.
Using open customer support tickets to identify the optimal number of employees to maintain on-call for a certain specialized kind of assistance is an example of predictive analytics’ applicability in a contact center environment.
Prescriptive Analytics
Text analytics could also be prescriptive by assisting in the development of a backup plan for particular future occurrences. This sort of analytics approach employs predictive analytics to better inform evaluations.
Because of the inherent usefulness of this type of analytics, whether text or otherwise, it is frequently favored among company executives trying to enhance their brand’s market share.
Text analytics Vs Text mining
To truly grasp text analytics, you must also be familiar with text mining and natural language processing. Text mining extracts information from enormous amounts of unstructured data.
Without this technique, you would have to manually screen textual inputs and determine whether they are of high quality. Once this data has been extracted into structured data, it can be evaluated to uncover valuable insights.
Text analytics can generate reports, highlight interesting trends, and give companies with new tools to make data-driven decisions.
Natural language processing methods are widely used in text mining and text analytics. It is a type of artificial intelligence capable of converting human language to a computer-readable format.
The end user is not required to know certain keywords or syntax in order for the computer on the other end to interpret their request. Instead, natural language processing takes over.
This technology employs a model to learn from the data that is supplied to it. The accuracy and relevancy of its insights grow with time, which is a form of the machine learning process.
How does text analytics work?
The text analytics method begins with the collection of enormous amounts of text data. Depending on the breadth of your project and the resources available, you can draw from social media comments, website content, books, organized surveys, feedback, or phone records.
You can work with a single collection of data or examine numerous aggregated resources. The text analytics system can also include text mining tools that allow it to begin sorting this data.
In certain circumstances, you might combine two or more methods to obtain the extracted data sets required to locate relevant information. Breaking down the phrase, tokenizing the text, and customizing the language are all examples of what happens at this stage of the process.
The software’s natural language processing capability can change the data in a variety of ways, such as labeling, grouping, and categorizing it. The following stage for the text analytics tool can be taken when the fundamental, low-level processing is finished.
This technique is frequently used to do sentiment analysis on a batch of data. The platform can determine a client’s level of satisfaction, the subjects they are enthusiastic about, and significant feedback on the customer experience. To ascertain the true message contained inside the text, it analyzes the grammar and surrounding context.
Your business can use text analytics to mine large data sets that are impossible to manually assess for useful research data.
This information can be utilized to guide product development, budget allocation, customer service practices, marketing initiatives, and a number of other functions.
You just need to get engaged at the beginning to develop the learning models and supply the system with data sources, and then at the end describe how text analytics handled the data because the majority of this process is automated.
Text analytics techniques
Word Grouping
A collection of words can often give more insight than a single phrase. For example, if you put together the phrases “expenses,” “expensive,” and “monthly,” you might reasonably assume that many clients believe the monthly costs for one of your products or services are too costly. However, you can always view the individual comments to have a closer look.
Word Frequency
This is text analytics at its most basic, where subjects (e.g., pricing, service, account, etc.) are tallied and ranked depending on the frequency with which they are referenced. This is helpful for swiftly finding frequent themes and difficulties that emerge among your visitors.
Sentiment analysis
Sentiment analytics is a method used in Natural Language Processing (NLP) that enables users to evaluate the seriousness of feedback based on the use of positive, negative, and neutral terms as well as the sentiment connected to frequently used phrases.
You now understand the frequency and grouping of particular phrases thanks to the preceding strategies, but is this feedback favorable, unfavorable, or neutral?
Gaining insight into sentiment shouldn’t be a problem if you have the correct instrument in place since, fortunately for you, your consumers are inclined to share their opinions on issues they care deeply about.
Text classification
It is the most advantageous NLP (Natural Language Processing) technology since it is language-independent. It can sort, arrange, and segment almost any data. Text categorization allows unstructured data to be assigned predetermined tags or categories.
Text categorization encompasses sentiment analysis, topic modeling, language, and intent identification.
Topic Modeling
Topic modeling aids in the categorization of materials based on certain themes. Topic modeling is less personalized and helps digest diverse texts and abstract reoccurring ideas. Subject modeling categories and assigns a percentage or count of words in each text to a certain topic.
Named Entity Recognition
Named Entity Recognition assists in the identification of nouns in data sets. Consider numbers preceded by ‘INR’ to be monetary; similarly, “Ms.” or “Mr.” or “Mrs.” followed by one or more capital words is most likely a person’s name.
The main issue is that, while certain nouns describe key categories such as geographic location, name, or monetary worth, others do not, which causes a lot of confusion.
Benefits
- Assist organizations in understanding customer trends, product performance, and service quality. This leads to faster decision-making, improved business information, higher productivity, and cost savings.
- Helps governments and political entities make decisions by knowing broad trends and attitudes in society.
- Allows scholars to quickly sift through a large amount of pre-existing material, extracting what is pertinent to their study. This speeds up scientific progress.
- By classifying similar information, you can improve user content recommendation systems.
- Text analytic approaches aid in the improvement of search engines and information retrieval systems, resulting in faster user experiences.
Use cases
Social Media Analysis
Apart from being a means of remaining connected, social media has also evolved into a platform for branding and marketing. Customers chat about their favorite companies and share their experiences on social media.
Using text analytics tools to do sentiment analysis on social media data helps to identify the positive and negative feelings of users toward products/services, as well as the influence and relationships of companies with their consumers.
Furthermore, social media analysis can help companies create trust with their customers.
Sales & Marketing
Prospecting is a salesperson’s worst nightmare. Sales teams make every attempt to increase sales and performance. Text analytics tools automate this manual job while giving essential and relevant insights to nurture the marketing.
Chatbots are used to respond to consumer inquiries in real time. Analyzing this data assists the sales staff in predicting the chance of a consumer purchasing a product, doing target marketing and advertising, and making product improvements.
Business Intelligence
Businesses can use data analysis to determine “what is happening?” but struggle to determine “why is this happening?”
Text analytics applications assist organizations in extracting context from numerical data and reasoning out why a scenario has occurred, is occurring or may occur in the future.
For example, a variety of things influence sales performance. While data analysis provides numerical figures, text analytics approaches can assist determine why there is a reduction or spike in performance.
Conclusion
Text analytics enables businesses to identify useful information from a wide range of data sources, from customer service requests to social media interactions.
Text analytics can find patterns, trends, and actionable insights by combining the results of text analysis and employing business intelligence tools to convert the statistics into easy-to-understand reports and visualizations.
After evaluating customer comments or reviewing the content of customer support requests with text analysis tools, you can use text analytics to help you uncover chances for improvement and adjust your product or service to your client’s requirements and expectations.
Leave a Reply