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How Text Analysis Tools Help You Understand Customer Sentiment and Grow Your Business

If you are an internet business, Text Analysis is something you’ve probably heard of. It’s a must-have for any business operating in today’s online space. It goes without saying that for your business to succeed you must understand your customer, but learning who your customers are has become increasingly more difficult in a modern era dictated by an online presence.

As an online business, you will be flooded by an overwhelming amount of data from your customers. Navigating through this can become time-consuming enough to become your entire job. It’s no wonder that many businesses are turning to text analysis to do the job.

What is Text Analysis?

Text Analysis software utilizes A.I., machine learning and sentiment analysis among other techniques that can draws out relevant information from unstructured text. The outcome is structured data that is more easily interpreted and understood.

Text Analysis comes in two main models. Text classification assigns tags or categories which are predefined to text data. Text extraction pulls out specific information from an unstructured text, which may include examples like prices, keywords, or company names.

Text Classification Examples

Sentiment Analysis: Sentiment analysis rates views of users as negative, positive, or neutral. This gives you insight into what customers perceive about your product.

Topic Analysis: Topic analysis detects topics or themes that appear prominently in a given text. Incoming text such as online reviews or support tickets can be grouped using topic classifiers.

Consider this example of a user product review: “This app is quite intuitive. It’s easy to activate and set up.”

This comment can have multiple tags assigned to it. For example, we would say that this comment has the tags “app” and “setup” in the text. Those tags would be called out for you to see quickly and sentiment analysis would show how the user felt about those tagged words.

Intent Detection: You can quickly determine customer intentions and respond immediately with the right classifiers in place. Classifiers like this will tell you what intent lies in a text. For example, you can use predefined tags such as “not interested” or “unsubscribe” to classify text that helps you take appropriate actions faster.

Language Detection: this is an automated process that classifies the text based on language. This can be incredibly helpful, especially if you have international customers and want to reroute tickets to local support staff who can serve them better.

Text Extraction Examples

Keyword Extraction: this detects and extracts notable words that appear in text data. It will generate a summary, along with insights.

Entity Extraction: through this, your company can retrieve certain information such as names, contacts, phone numbers, emails, etc. You can find out answers to such questions as, “Which of your business branches are generating more comments?”

Text Analysis Applications and Tools

Text Analytics is an incredibly useful tool as it can help companies experience accelerated growth. You can quickly analyze customer feedback, including open-ended feedback like survey responses. It can provide you with valuable first-hand client information, which can lead to service improvements.

Other benefits include

  • managing content
  • recommended content
  • product development
  • business intelligence
  • marketing optimization

Text Analysis vs. Manual Process

Companies typically deal with incredible amounts of data on a daily basis—these range from emails, social media posts, chats, survey responses, and customer support tickets. Though this data is like a goldmine, it can be near impossible to deal with. This is mainly because 80 percent of it is unstructured.

Unstructured data isn’t the most straightforward data to understand.

With access to such vast customer feedback in an unstructured format, businesses are facing data overload. While it is feasible for humans to process smaller amounts of data, this is impossible with the volumes typically received online. Scaling is also poor when you consider manpower costs and quality of output as the volume of data explodes. This has made automated text analysis the only way to handle this high influx of data.

Understanding text analysis requires understanding machine learning, which lies at the heart of most text analysis tools.

Machine Learning, A.I., and Text Analysis

Machine Learning (ML) is an approach in Artificial Intelligence (A.I.) that uses the experience of models to improve computer algorithms in an automatic process.

The machine is trained through relevant data from which it acquires experience. It then applies its experience to text analysis and natural language processing (NLP) to understand text found in social media comments, survey responses, online reviews and more.

The advantage of A.I. and machine learning is that it dramatically speeds up the process of translating your unstructured text into data that is easier to understand and therefore more useful. Moreover, it is more accurate, presenting your business with unrivaled valuable information to aid your business decisions.

Text Analytics, Text Analysis, and Text Mining

These three concepts seem to mirror each other but it is good to know if there are any differences between them. Text analysis and text mining are interchangeable terms that refer to the same thing. They both entail extracting information from unstructured data.

Even though text analytics is often mentioned in the same breath as text analysis, it differs subtly in meaning.

Text mining or text analysis is considered a first step in dealing with unstructured text. It pulls out specific information from the data. Text analytics is the final step, which represents the data in a way that is easier to understand.

While they play different and related roles, both are essential to give a complete picture of information in your data. Text mining emphasizes the qualitative aspects of your data, while text analytics gives you the quantitative elements. The quantitative outcomes are easily visualized via reports and charts.

The Usefulness of Text Analytics to Companies

Increasing volumes of data has rendered conventional ways of picking patterns and themes in text impractical. This data is often ambiguous, massive, and impossible to make sense of. Text Analysis is an automated method that is the only sensible way to overcome this challenge.

Text Analysis uses techniques for information retrieval, statistics, and computational linguistics to unearth insights buried in data. Often these insights cannot be discovered any other way. Businesses can leverage text analysis to gain excellent results, serve customers better and rapidly grow their business.

Conclusion

To make any reasonable progress in your online business, you must intimately know your customer. This means accurately capturing the customers’ feelings at every point in time.

It’s the only way to outpace your competitors by responding faster to your customer’s needs.

Thanks to the stunning capabilities provided by AI-powered analytic tools, one can quickly derive useful information from customer feedback, no matter how dense or vast the data. These tools can quickly break down the data into categories to facilitate analysis and decision making.

 Author Bio

Matt Canada has a background in graphic design, customer service and management. In his current role, he oversees marketing activities for Tatvam and also supports in sales and support.

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