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What is Text Analysis?

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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.

What is text analysis?

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

  • Whole documents: gathers data from an entire text or paragraph, such as the general tone of a customer review.
  • Single sentences: gathers data from single sentences, such as more in-depth sentiments of each sentence in a customer review.
  • Sub-sentences: a sub-expression within a sentence can provide information, such as the underlying sentiments of each opinion unit in a customer review.

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.

How is Text analysis different from Text mining?

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.

Why should you use text analysis?

Text analytics can benefit corporations, organizations, and social movements in a variety of ways, including

  1. Assist companies in recognizing customer trends, product performance, and service excellence. As a result, decisions are made quickly, business intelligence is improved, productivity is raised, and costs are reduced.
  2. Aids scholars in quickly exploring a large amount of existing literature and obtaining the information that is pertinent to their inquiry. This promotes quicker scientific advancements.
  3. Helps governments and political bodies make decisions by assisting in the knowledge of societal trends and opinions.
  4. Search engines and information retrieval systems can perform better with the aid of text analytics tools, leading to quicker user experiences.
  5. By classifying similar content, improve user content recommendation systems.

How to use Text Analysis in Business?

In business, Text Analysis can be used in multiple ways. Here are some useful use cases of Text Analysis in business:

Customer Experience

  • Increasing Loyalty – Unearth key insights on the top issues promoters face so action can be taken to stop promoters from becoming detractors.
  • Preventing Churn – Smart identification of competitor mentions with highly dissatisfied customers. You can also use text analytics to close the loop on negative sentiment or key topics like Churn Potential that appear in your customer feedback.
  • Cross Sell/Up Sell – Combining Operational data like Customer Spend or Lifetime Value with upcoming renewal dates & analyzing their comments for topics like Loyalty, Reward, Incentives, etc., it’s possible to predict cross-sell potential using a combination of AI & text analysis.

Employee Experience

  • Employee Attrition – Combine structured data like Engagement scores with Low sentiment around topics like Manager Support etc.
  • Employee well-being – Using real-time alerts on topics like Depression & Anxiety, intervention can be done where required.
  • Work-Life Balance – Using text analysis to understand topics around Work-life Balance, identifying which segments of employees are most affected, and taking action accordingly.

Product Experience

  • New Product Launch – Using text analysis to get valuable feedback on what features to improve or drop in the next release.
  • Product Usage – Analysing warranty data can give key insights on what features to invest more in to increase usage, reduce service costs, etc.

Brand Experience

  • Campaign Effectiveness – Analyse the top drivers of satisfaction for your campaigns alongside Operational data like Campaign Spend, Reach, etc., to ascertain ROI.
  • Brand Tracking – Understanding top themes that come to mind for the Brand & competitors.

What are the benefits 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.

1. Utilize Verbatim Remarks in a Variety of Media or Languages

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.

2. Enhance User Experiences for Clients, Staff, and Other Stakeholders

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.

3. Boost Sales for Your Business

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.

4. Gain more control over your spending

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.

5. Increase the effectiveness of using unstructured data

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.

6. Make more decisions based on data

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.

7. Utilize fresh opportunities promptly.

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.

Why Use Voxco for Text Analysis?

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.

What is Text Analysis? Image rating

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: 

1. Understand the key drivers of customer satisfaction

Determine the volume of negative, positive, or neutral sentiments with the sentiment performance chart. 

2. Unlock semantic structures within texts 

Extract recurring topics from survey feedback and get insights that help drive decisions with the Topics chart.

3. Learn what your customers are talking about 

With the Word Cloud chart, glance at the most recurring words in open-end textual feedback.

4. Distinguish sentiments 

Go through the number of positive, negative, and neutral responses denoted with respective colors assigned to sentiments with the Open-End Answers chart.

5. Discover valuable insights in minutes 

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.

6. Analyze in real-time 

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.

Conclusion

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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