In 2023, AI has already revolutionized the way businesses operate: with its ability to analyze vast amounts of data and quickly provide the needed insights, AI has become an essential tool for business analytics.
One of the most powerful tools of AI in business analytics is text analysis, as by using it, companies can gain valuable insights from unstructured data such as customer reviews, social media posts, and emails. In this article, together with specialists in various types of sentiment analysis and text analysis, we will explore how AI text analysis benefits business analytics and how it can help businesses make data-driven decisions. Here we go!
AI Text Analysis: What Is It?
AI text analysis is simply a procedure of extracting information from a large amount of text data. In natural language processing (NLP), programs actually understand and process this data. For businesses, text analysis and natural language processing can help automate the process of understanding customer reviews at scale, enabling them to make data-driven decisions to improve their business.
Why Is Text Analysis Needed?
The benefits of machine-enabled data procurement and text analysis are vast, especially when it comes to text analysis. Here is the top list of reasons why businesses need text analysis:
- Text analysis enhances business scalability
Sorting through vast amounts of data manually can take months or even years. However, with text analysis tools, emails, documents, and other information can be quickly organized and structured. This process can be completed within minutes, freeing up valuable time for other essential business tasks.
- Real-time analysis of any text is possible
Businesses are inundated with information, and customers contribute even more by adding content online. Unfortunately, this makes it challenging for companies to monitor all types of content. Text analysis technology can help by locating critical data in real-time, no matter where it is located.
- AI-powered text analysis gives consistent results
While humans may be more intelligent than computers, they are also prone to making errors. By using AI-enabled text analysis, all data and related criteria can be analyzed consistently, without the risk of human error.
- Performs large-scale text analysis
NLP techniques analyze all types of documents, no matter their size. Assuming the exact text is analyzed manually. But with NLP and AI, a complete analysis can be done in seconds or minutes. Furthermore, NLP can scale up or down based on needs and computing power.
- Optimizes the process and reduces the cost
As mentioned earlier, NLP can work at any scale, at any time, day or night. For manual text analysis, you need a team of people working 24/7. But with AI text analytics, you can keep your team size to a minimum.
- Increases customer satisfaction
An automated NLP that operates in real-time works best for customer feedback. You will get to know about the problems your customers are facing with a product or a service instantly. This ensures that no processes are repeated and that procedures are streamlined.
Plus, AI-powered NLP tools facilitate automated analysis and sorting of customer service tickets. These tickets can be further categorized by urgency, sentiment, intent, topic, etc. This will never lead to any customer being left hanging.
- Provides genuine and actionable advice
Unstructured data is difficult to skim through. For instance, online surveys and open-ended responses have a more complex analysis that is facilitated by breaking the text into smaller pieces that are easily understood. However, AI-led NLP tools can explore the mountains of unorganized information and provide accurate and actionable knowledge.
- A deeper understanding of the market
Market research is a time-consuming process that is susceptible to inaccurate or incomplete data analysis. However, when you utilize NLP to understand your customer base, you'll have a greater understanding of the market's segmentation. Also, artificial intelligence will help you direct your efforts and decrease customer attrition.
Examples of Some Real-World Applications of AI and NLP
In general, AI analytics helps interactively visualize themes in customer data: the system analyses the free-text feedback submitted in customer survey forms, which has previously been difficult to analyze as companies spend time and resources and still struggle to do this manually.
By using Natural Language Processing (NLP), and sentiment analysis to assign a sentiment score capturing how positively or negatively customers feel, businesses can analyze language to identify recurrent patterns in what customers are saying.
Now let’s look at some real-world examples of using AI text analysis tools in various business niches:
- Natural language generation to answer questions via customer service chatbots
Retail companies can use AI chatbots to provide 24/7 customer support and answer frequently asked questions about their products and services. Or, for example, an airline company can successfully use AI chatbots to assist customers with flight bookings, cancellations, and rescheduling.
- Voice-controlled human assistants like Alexa and Siri
Smart home technologies companies uses voice assistants to understand the needs of the speaker and control home appliances through voice commands. Or, for example, a music streaming service uses a Siri-like assistant to play requested songs and create personalized playlists based on user preferences.
- Language models, such as the autocomplete feature that predicts your following words based on what you have typed
This technology is widely used in smartphones and Google Docs, and in any other messaging apps, not only to propose the next words but to suggest emojis and GIFs based on the text being typed. A search engine like Google will also use it to predict search queries and provide more accurate search results.
- Streamlining the recruitment process, where people’s skills and experience are quickly scanned
Many recruiting agencies on LinkedIn use AI-powered search and recommendation algorithms to find suitable candidates for job openings, and even to test job applicants' knowledge and skills in specific areas.
- Usage of NLP in tools that can correct errors and simplify writing styles
For example, a content marketing agency can use Grammarly to proofread and edit blog posts and social media content for grammar, spelling, and punctuation errors. It’s also widely used by law firms to ensure legal documents are error-free and written in a clear and concise style.
- AI text analysis in smart TV technologies
Here, AI text analysis can be successfully used to understand customers’ feedback and get actionable insights, especially relating to metrics such as viewing experience and customer service NPS, as well as what they can do more to increase customer retention.
- AI text analysis in other businesses
Financial institutions and banks also start using AI text analysis to analyze customer feedback and identify areas for improvement in their services. The same goes for healthcare providers and centers: they use AI text analysis to analyze patient reviews and feedback to improve patient satisfaction and quality of care.
Wrapping Up
Using AI text analytics in a company offers many advantages, as it’s a perfect tool for keeping your business up to date. It's no longer just something IT can experiment with, but all businesses can use it to their advantage and get the most out of your data. harness the power of AI-based text analysis to boost your business KPIs and growth!
No comments yet