If you have been keeping an eye on the ever-changing landscape of business technology, you probably have encountered text analysis, text mining, or text analytics. It sounds like a technical topic and can be intimidating, but it could also give you a considerable edge in business.
Nowadays, we regularly come across a large volume of information we need help with. We know this data can help us improve our performance or strategies, but it can still be difficult to process due to the sheer quantity. That is where text analytics comes in.
Join us as we define text analysis, the types of text analysis, its applications, and how it can be helpful to your business.
What is Text Analysis?
Our first order of business is to define what text analysis is. Text mining and analytics mean the same thing and cover the same topics and actions. By understanding the process, we can effectively analyze how we can apply them to our business operations and reap the benefits.
Defining Text Analysis
Text analysis is a process. But what does it do?
As its name suggests, it analyzes unstructured and semi-structured data to obtain knowledge or ideas from summaries and patterns. One example of an application of text analysis is exploring a large volume of customer surveys for a particular product over a year. We will be discussing more examples for your understanding later on.
Text analysis, text mining, or text analytics use different tools to derive insights from a large amount of data. In the past, this was less necessary. However, modern amenities like the Internet have given rise to an influx of information that can be hard for a human to process alone
That is where text analytics comes in. Analytical tools, AI, machine learning, and other programs help businesses process data when a human alternative is too time or resource-consuming.
At the same time, using digital tools reduces the risk of human error or possible prejudice. It is an excellent option to understand critical elements or factors in business objectively.
The Difference Between Text Analysis and Natural Language Processing
If you've researched analytics before, you may have encountered the term "Natural Language Processing" or "NLP."
While text analysis is the process of deriving insights from assessing large volumes of text, natural language processing assists a machine in understanding and reproducing human speech and patterns.
Unlike text analysis, natural language processing considers the semantics and structures of grammar. NLP is also an interdisciplinary field that includes the concepts of text analysis.
Elements in Text Analysis
Text analytics don't simply happen instantaneously. As quickly as some results may appear, many processes are involved behind the scenes. What initiatives occur during the text analysis process? These are what happens during text analysis:
- Extracting Data
Before analyzing data, text analysis tools will need that data first, which is where data mining comes in. So how is this data extracted in the first place?
Data can be derived from social media, customer reviews, and news. At the same time, the Internet allows users to derive more data through customer databases, feedback forms, CRM software, and reports.
External data such as social media and news can easily be derived using application programming interfaces or APIs, web scraping applications, and other types of software.
- Language Detection
Language detection lets you sort information while adding or omitting language-specific items simultaneously.
- Text Preparation
This portion takes relevant vocabulary and assigns grading categories to them. At the same time, it takes away unworkable elements like punctuation marks and symbols.
- Sentiment Detection
The stored vocabularies are assigned to various topics and opinions. The program uses keywords and algorithms to classify content into happy, upset, positive, negative, or uninterested positions.
After analyzing all the data, text analyzing software will present the results as easy-to-understand diagrams like charts and graphs.
These visual aids make it easier for human decision-makers to understand the information presented and to be able to make intelligent choices based on said information.
Text analysis aims to transform large volumes of information and transform it into intuitive and easy-to-comprehend reports that help business owners in decision-making.
Text Analysis in the Business Setting
Now that you have a closer understanding of the definition of text analysis let's look at the various applications you can find in businesses.
Text analysis is particularly useful for businesses that constantly receive information through customer feedback, surveys, social media reactions, and many more.
How Small Businesses Use Test Analysis
There are different ways how businesses process text information to their advantage. Let's look at some examples and applications.
Social Media Monitoring
Social media is one of the predominant forms of communication. Millions of thoughts and opinions are expressed hourly, many of which can prove relevant to your business.
Text analytics lets you monitor positive and negative feedback about your business, the industry, related products and services, and market trends that you can use for your next product development idea.
Text analysis can be cleverly utilized to gather data about opinions correlating to your brand or business. Sentiment analysis collects data to gain insight into complaints and arguments. Additionally, text analysis can be used to study competitors and find ways to improve operations in comparison.
Brand Reputation Management
Another application of sentiment analysis allows you to study data from social media and analyze positive and negative thoughts on your business. This way, complaints are addressed efficiently, and you can also focus on adjusting the brand to better credibility and reputation among customers.
For customer service-based businesses, the conversation is a key player. Dialogues, tickets, emails, and chats allow the business to study customer needs and expectations.
One can also analyze factors like what phrases generate a more positive outcome and improve KPI or what statements improve customer retention.
Automated Routing and Prioritization in Customer Service Teams
For customer service, routing is a daily occurrence but remains a cause of upset for both customers and the employees who receive the complaints of angry clients.
Analytics can help study and determine the correct routing initiatives based on the issue, customer sentiment, and urgency.
Historical data on customer interactions can help businesses gain information on targeting the customer base.
One can extract opinions, wishes, compliments, or complaints from these interactions.
Text analysis helps derive and analyze all this data efficiently and without bias on a quantitative and qualitative scale.
Marketing should always be updated on customer trends to provide the expected products, services, and strategies.
Text analysis helps you identify trend-related factors such as influencers, channels, and leads that interest customers.
Benefits of Test Analysis
Now that you know the text analysis applications, we can explain how using test analysis can give your business a great advantage.
Better Customer Understanding
You better understand customer needs and expectations by studying customer reviews, posts, and feedback. This way, you can make more informed decisions and give better customer support.
A positive response from customers follows effective service. By showing competence and care in dealing with customers, you can expect them to have a favorable impression of you and your brand and encourage them to continue using your products and services.
Effective Response to Feedback
Identifying potential problems, issues, and complaints will help you create initiatives to improve your performance and deliver products or services needed by customers.
Furthermore, positive feedback will help you pinpoint the strengths that you can use as an advantage.
Automated Business Processes
For customer service-oriented groups, text analysis can identify trends and keywords that customers respond to. It can also study different topics and levels of urgency using sentiment analysis. This way, you can improve routing, ticketing, and prioritization strategies.
Text mining or analytics can be used to monitor the effectiveness of marketing campaigns and initiatives. For example, you can track customer responses on social media or study their interactions with the brand online.
Having a better insight into what the customer wants or expects makes it easier to develop or improve products and services that receive greater satisfaction.
Brand responsiveness, effective marketing, problem-solving, product innovation, and efficient communication garnered from text analysis are ingredients to a satisfied customer and a positive brand reputation.
With this comes a higher chance of customer engagement and an increase in revenue.
Examples of Text Analysis Techniques
The benefits of text mining are tremendous and give you a considerable advantage in the playing field. Now we'll talk about examples of tools and techniques used in text analytics.
- Sentiment analysis
This technique detects various emotions in text using keywords and grammatical structures. It identifies customer sentiments such as satisfaction, dissatisfaction, happiness, and anger. The data is extracted from various sources like reviews, surveys, and social media posts.
- Conversational analytics
Like sentiment analytics, conversation analytics focuses more on general conversations between a business and its customers. Its sources come from customer care calls, reviews, chatbots, and support tickets.
- Topic analysis
That machine learning technique classifies large amounts of texts according to specific themes and topics. It is an effective tool to identify feedback containing key aspects your business is concerned with. For example, for a food establishment, you would want to find comments with keywords like customer service, taste, ambiance, and hygiene.
- Intent detection
Like topic detection, this tool identifies intent or a call to action in the text. For example, it will pinpoint keywords like Unfollow or Unsubscribe. From there, you can plan how to convince customers to stay with your brand.
- Text extraction
Text extraction analyzes and extracts text data like the names, keywords, phone numbers, and other topics that are relevant to your operations. For example, entity extraction gets the names of people and companies. So if there's a comment saying, "X Brand is cheaper than Y Brand," you immediately identify a potential competitor and a reason as to why others may prefer the other brand.
Where to Get Tools for Text Analysis
We have now familiarized ourselves with text analysis's various tools and applications. In this section, we'll show you a handful of programs used for this purpose that you can try for your own business.
We've compiled various programs that implement text analysis that is helpful to small businesses. These programs below vary in learning difficulty level and scale:
Your CRM software may contain the necessary text analysis tools you need. CRM stands for Customer Relationship Management and is software used to track a business's customer relationships and interactions. Salesforce and Zendesk are examples of CRM software.
Google's Auto ML and Natural Language API
These programs are compatible with Google Cloud Storage and integrated with Google Workspace applications.
Machine learning technology is used to extract insights from text, and it contains basic needs like sentiment analysis, text classification, and entity extraction.
That is another tool that uses machine learning technology to obtain insights from text. It contains language detection, keyword extraction, sentiment analysis, and topic modeling, among other features. It is also one of the programs that are beginner-friendly.
Azure Text Analysis API
Azure offers demos for users who want to try it out first. It is mainly used for conversational and sentimental analysis. Azure text markets itself primarily because it can process medical text and unstructured medical data.
Chattermill uses AI to collect data from different feedback and customer interaction sources and primarily focuses on sentiment and theme AI. The program is available in other languages, and one of its main selling points is its compatibility with platforms like Google Play, App Store, TypeForm, Slack, Zendesk, and many more.
Thematic is a valuable tool, particularly for customer-oriented businesses. It uses a text-processing AI to convert unstructured data into insights. It is integrated with platforms like Discourse, SurveyMonkey, and Zendesk. One of its remarkable features is its presentation of analysis and metrics in easy-to-comprehend infographics.
What is LimePhone?
LimePhone is an application that provides users with a virtual number along with many other features. Together with it all of it’s functionality, it offers a wide range of features such as unlimited SMS, MMS, and inbound calls, as well as advanced tools like automated responses, call routing, and voicemail.
So how is LimePhone relevant to text analysis? That's because its tools can effectively cater to customers' needs in response to text analysis. Its superior call forwarding, routing, automated and scheduled answers work perfectly with customers' demands.
LimePhone also offers reports and analysis tools in the form of call recording, call, voicemail transcription, and call analytics.
Furthermore, LimePhone is compatible with many social media and CRM platforms like Facebook, Slack, Zendesk, Salesforce, and Intercom, making integrating with the CRM software you're already using easier.
LimePhone is practical affordable, compatible with many devices, and accessible by teams. Lastly, you can avail of a free demo of its features with only a quick sign-up process and provision of your contact details.
Try LimePhone now and upgrade your customer responsiveness and operations with text analysis.
Frequently Asked Questions
- Is text mining different from text analysis?
- They are the same and can be used interchangeably.
- What is the popular CRM software in the market?
- Examples of widely used CRM software are Zendesk, Salesforce, Intercom, SAP, HubSpot, and Oracle NetSuite.
- What beginner-friendly text analysis programs can I start with?
- When you venture into text analytics, Google Cloud NLP, Amazon Comprehend, Microsoft Azure, and Chattermill are good places to start.
- Is text analysis legal?
- Yes, there are currently no legislations on text mining. However, it is essential to note that data privacy and intellectual rights should still be upheld. It is also wise to include a disclaimer for any platforms where you gather data, like feedback, surveys, and reviews.
Text analysis proves to be a useful tool in the business setting. It improves customer understanding and helps identify key strengths and weaknesses for a business to improve, develop, or troubleshoot. It also allows you to gain insight into customer opinions without being burdened by the large volume of information.
Implementing text analysis will be a competitive advantage to businesses, especially when integrated with practical operational and communication tools like those offered by LimePhone.