In today’s digital world, businesses are overwhelmed with unstructured data. Without adequate technology, it’s virtually impossible for businesses to analyze and process the massive volume of data.
That’s where Natural Language Processing (NLP) comes to the rescue…
Read on to know more about why natural language processing is difficult?, how does NLP work for startups, SMEs and Enterprise. why NLP is important for businesses, and discover some significant applications of NLP for businesses.
What Is Natural Language Processing (NLP)?
NLP is a subfield of artificial intelligence (AI), majorly concerned with processing and understanding human language by machines. By enabling machines to understand human language, NLP improves the accuracy and efficiency of processes. This is achieved by automating various repetitive tasks. Some of the examples of natural language processing applications include; ticket classification, machine translation, spell checks, and summarization.
With the development of NLP technology, today, it is able to perform sentiment analysis for human language. One such great tool is converse smartly. This enables it to detect emotions in the text, which is one of the most widely used NLP applications by businesses; enabling them to detect brand sentiment on the internet (search engine, and social media reviews). By enabling brands to identify customer issues on the internet, businesses are in a better position to respond and take necessary rectifying actions for positive customer satisfaction.
Why Is Natural Language Processing Important?
The biggest benefit of NLP for businesses is the ability of technology to detect, and process massive volumes of text data across the digital world including; social media platforms, online reviews, news reports, and others.
Also, by collecting and analyzing business data, NLP is able to offer businesses valuable insights into brand performance. In addition, NLP models can detect any persisting issues and take necessary mitigation measures to improve performance.
Google speech to text is able to achieve all of this by training machines to understand human language in a faster, more accurate, and consistent way than human agents. The technology is able to consistently monitor and process data. This helps brands remain updated with their online presence, and not get riddled with inconsistencies.
Applications of Natural Language Processing for Businesses
– Immediate Customer Support
Even brands which haven’t heard of natural language processing must be aware of the chatbots and their applications in customer support. Chatbots are AI-powered tools that can hold conversations with humans on websites or mobile applications. The AI technology powering the chatbots is NLP, which enables these tools to understand, analyze, and respond to customer queries instantly 24/7.
Chatbots today play the role of customer service executives by responding to the customers’ queries about specific themes with predefined answers. To add to its utility, chatbots can also offer specific customer support like booking a service, sharing a link to detailed guidelines, or finding the right products. In essence, the applications of chatbots are endless and depend upon unique business needs.
In fact, owing to the advances in machine learning algorithms, chatbots today can perform sentiment analysis, detect users’ intent and respond appropriately.
– Save time and money
Businesses need to make a profit, and cutting expenses is the best way for businesses to optimize revenues. Natural language processing (NLP) has proved to be a highly effective technology for companies to save time and money while optimizing business processes.
Let’s understand it with the example of chatbots.
According to statistics, companies can save as much as 30% on customer support by implementing automated chatbots as customer support agents. By employing NLP-powered chatbots, companies get to free up time for employees. This allows them to stay focused on creative tasks like engaging customers for generating leads, and sales.
In addition, NLP models are able to improve and become more accurate over time without any additional investment. This is achieved when NLP models can work with more data, which automatically improves the performance and accuracy of the NLP models.
– Moderate user-generated content
Today, users are generating a massive volume of data on digital mediums. The data is generated on various platforms, making it impossible for brands to keep a check and respond actively.
Natural Language Processing (NLP) tools provide businesses with an effective tool to consistently monitor a massive volume of user-generated content across all the digital platforms. By employing NLP tools, companies can actively engage customers by responding to their queries or concerns. Also, NLP tools help companies to enforce various content filters like blocking unwanted content, or implementing spam filters on websites, or social media pages.
NLP models are also used by businesses to maintain the quality of content on forums. This ensures that there aren’t any malicious backlinks or unsolicited advertising. It can also detect “banned” words or vulgarism to auto-filter user-generated content that may be classified as hate speech.
4. Boost your conversion rate
Improving conversion rates is often the most pressing issue for marketing executives, who employ all sorts of strategies to convert visitors to customers. Higher conversion rates not only lead to improved revenues for businesses but also lowers the cost of customer acquisition.
NLP models have proven their effectiveness for conversion optimization. NLP-powered tools like chatbots, auto-complete text, and advance search functionality; vastly improves the overall customer experience. This in turn enables companies to gain customers’ trust and improve conversion rates.