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There exist tons of unstructured data that come from emails, website articles, survey responses, and blogs. The truth is that various types of textual information hold a good amount of business value and can unlock a number of exciting opportunities. With the help of artificial intelligence, machine, and deep learning, brands are able to structure data, reveal new features and correlations to enhance their strategy. The current technologies allow companies to perform more extensive text analysis and gauge clients’ behavior and sentiments to improve their interaction with the brand and drive more sales.
Let’s discover the key insights that can be withdrawn from unstructured textual information and learn about all the benefits it has for businesses.
Significance of Unstructured Data
Did you know that unstructured data is growing at more than 50% each year? Massive, right?
However, due to large quantities of such data and the inability to fully analyze it, it was not particularly useful until recent years. Nowadays, when there are quite a few AI-powered tools for data analytics that are created to automate many processes, it has become easy to access all those valuable insights provided by tons of unstructured information.
The most common data that does not hold a clear structure includes:
- Emails (specifically the body text);
- Text files;
- Presentations;
- Audio files;
- Blog posts;
- Articles;
- Social media comments;
- Survey results.
Analyzing unstructured data gives a better understanding of a company’s strategy, helps to act accordingly and make the right decisions. Not only can businesses prosper in competitive markets but also make use of an impressive amount of additional information that has been previously ignored.
Unstructured data can be used to conduct customer analytics. When businesses can integrate information from online reviews, social media comments, chatbot correspondence, and identify patterns taken from different sources, they will be able to improve customer experience with the brand.
On another note, unstructured data is helpful for marketing. Having an ability to scan datasets and identify patterns, brands understand what items are the most appealing for their customers and are able to adjust product offerings and move towards worthwhile marketing initiatives.
For those companies that are strictly regulated, there might be issues with compliance that are costly in time and money. Using unstructured data, organizations can uncover regulatory issues much earlier and avoid a negative impact. AI algorithms, natural language processing, and sentiment analysis are those mechanisms that have proven their effectiveness in data analysis and made it possible to use it properly.
Customer Analytics
When we talk about customer analytics, the first thing that helps brands understand their target audience is analyzing their feedback. People communicate with different brands through direct messages, chatbots, website reviews, social media comments, forum discussions, and many others. Things they mention, advantages they underline, and features they would rather not face again are the directions for further business development and improvement.
AI-powered text analytics makes it possible to process large quantities of information, group it by topic, opinion, sentiment, and find the key patterns right away. There are programs that offer you explicit dashboards with comment grouping, opinion analysis, data visualization, and trend detection. Businesses can understand how customer feedback affects them and work on improving both the product and client relationship with it.
Sentiment Analysis
One of the amazing features that customer analytics can provide is sentiment analysis. It detects whether the article, one paragraph or just a sentence has positive or negative polarity in terms of consumer opinion.
Analyzing people’s emotions is important for businesses if they want to understand customer needs and tailor their products and services accordingly. Knowing whether clients are happy with the pricing, quality, and customer service holds great potential and makes it easier to react faster and deliver the best possible value to the brand’s target audience.
Using sentiment analysis, companies can sort data, receive real-time factual analytics, and gain practical insights into what people think and, what is more important, how they feel about a certain product or service. When brands can evaluate the emotional mood of the customers, they can act on time, and avoid unneeded negativity or unexpected reaction.
Text Classification
Assigning different categories to free text is called text classification. It helps to organize and structure vast amounts of textual data. Articles and blog posts, for instance, can be categorized according to topics and languages, chatbot requests by urgency while brand mentions can be structured by sentiment.
Companies can benefit from existing text classifiers that can structure different types of business information like emails, documents, chatbot conversations, blog posts, social media tags, comments, and messages. AI-powered tools allow businesses to save their time and effort when it comes to analyzing data and prove to deliver amazing results. While there are organizations that keep doing it all manually, modern technologies are helping others to enhance efficiency and automate a number of processes.
Text classification offers scalability, real-time analysis, and consistency. It eliminates human mistakes, scans various types of unstructured information, organizes it, and allows companies to make use of literally all information on the Internet that is related to the brand, its activity, and offered services.
Benefits of AI-assisted Text Analytics
Text analytics reveals all the facts and assertions, converting them in a structured and organized form that can be studied further. With the help of artificial intelligence and machine learning, companies can use text mining to
- run queries over thousands of documents, reviews, and web articles;
- analyze posts in different languages;
- define patterns in customer preferences;
- enhance product quality and development;
- retain existing clients and win over new ones;
- prioritize important decisions based on received data insights.
The structured and organized data extracted with the help of AI tools can be integrated and used in databases and business dashboards for descriptive and predictive analytics. Text mining allows brands to be more attentive to customer concerns, resolve issues, and grow based on a set of preferences to deliver the ultimate experience.
Conclusion
New technologies keep evolving, bringing innovative solutions for all types of businesses worldwide. AI-powered tools have made it possible to finally get into a huge amount of unstructured data and extract actionable insights that can help enterprises better serve their customers and improve product development.
AI-assisted text analytics classifies textual data, provides an extensive report on customer feedback with sentiment analysis, and structures it all to identify existing patterns and how they influence businesses. All the operations are fully automated and save companies time on processing thousands of textual information, organizing all the received data in an explicit and compelling structure. Worth the effort and absolutely justifiable.