If you run a business that manufactures products or provides services, then you probably have a huge customer base. It also means that you receive tons of customer feedback and that you’re aware of the challenges that come with making sense of that data.
However, manually processing and organizing textual feedback takes time, it’s tedious and inaccurate, and it can be expensive if you need to hire extra staff to sort through text.
An easier way to do this is to use an application called Text Analysis, enabled by machine learning.
Unstructured text data can be automatically mined for useful insights using text analysis (TA), a machine learning technique. Text analysis tools are used by businesses to swiftly consume web data and documents and turn them into useful insights.
With text analysis, you can sort survey replies by attitude and topic or extract specific data from tens of thousands of emails, such as names, company names, or keywords.
Depending on the findings you want, text analysis can vary across a variety of texts. It is applicable to
You can begin analyzing your data once you’ve decided how to segment it.
Let’s examine the process of text analysis in more detail.
Text analytics and text mining are frequently used interchangeably. Text analytics produces quantitative results, but text mining is typically employed to extract qualitative information from unstructured text.
By examining reviews and polls, text mining, for instance, can be used to determine whether customers are pleased with a product. Text analytics is used to gain a deeper understanding, for example, by spotting patterns or trends in unstructured text. Text analytics, for instance, can be utilized to comprehend a negative rise in consumer satisfaction or product popularity.
The outcomes of text analytics can then be combined with data visualization strategies to facilitate decision-making and facilitate understanding.
Text analytics can benefit corporations, organizations, and social movements in a variety of ways, including
In business, Text Analysis can be used in multiple ways. Here are some useful use cases of Text Analysis in business:
As a crucial component of obtaining value from unstructured data sets that you would otherwise be unable to process, text analytics offers several benefits to your company.
With text analytics, you are not limited to a specific format or media type. Because of this compatibility, you don’t need to pre-process your data as much before entering it into the system.
Your needs will evolve as time goes on in terms of your customer experience, employee engagement, and other areas. By using direct, verbatim comments and identifying the trends in the data, you may continue to look for ways to improve this.
Happy consumers support your business and come back for more, and loyal, highly engaged staff members reduce attrition. Your company will be in a position to have sustained long-term growth if you can maintain control of the areas that are most crucial to these parties.
The text analytics data influence your budgets. Targeted areas are those that require the most investment, whereas regions that don’t have as much of an impact on operations are given less importance.
Getting unstructured data into a form that typical analytics systems can use is one of the largest time-sinks of dealing with unstructured data. Without conversion or other tiresome manual operations, text analytics can use the data just as-is.
You have access to additional facts for your decision-making process thanks to verbatim remarks and insightful text data. You can go further into why survey respondents gave your business the ratings they did or see how well-received new goods and services are.
Overnight, your market can undergo a significant change as a result of new ideas, technology, or persistent customer feedback. With the use of text analytics, you may access this data and find new opportunities that could give you a competitive edge.
Businesses can leverage text analysis tools to efficiently process large volumes of unstructured data, transforming it into actionable insights that drive strategic decisions and enhance operational efficiency.
Voxco’s Text Analysis feature is quite robust. It can be applied to many use cases across different industries. To sound more convincing, here are some more reasons why you should use Voxco for analyzing text.
It extracts all the survey data and arranges it in different charts- Sentiment Performance, Word Cloud, Topics, Open-End Answers, Trends Chart, and Real-time Sentiment Analysis so you can:
Determine the volume of negative, positive, or neutral sentiments with the sentiment performance chart.
Extract recurring topics from survey feedback and get insights that help drive decisions with the Topics chart.
With the Word Cloud chart, glance at the most recurring words in open-end textual feedback.
Go through the number of positive, negative, and neutral responses denoted with respective colors assigned to sentiments with the Open-End Answers chart.
With the trends chart, analyze the changes in customer sentiment with respect to time. Make better decisions that align with what your target customers actually want.
With the real-time sentiment analysis chart, get an automatic analysis of open text to recognize the underlying sentiment within customer feedback in real-time.
Text analysis is no longer just a niche field for software developers with a background in machine learning. It has developed into a potent tool that assists companies in every sector in obtaining insightful, actionable information from their text data.
It’s never been simpler to save time, automate operations, and boost productivity, enabling businesses to offload time-consuming chores and support their teams in giving consumers better service.
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