Impact of Intent Classification in NLP on Business

Every business, big or small, has two major goals – sell their products/services and provide exemplary customer service. Over the years, several tools and software have been developed to enhance the customer and client interactions. These software help in automating the majority of the tasks, thus, ensuring high levels of customer satisfaction. With the advancement in technology, more and more tools and platforms are being developed to better understand the needs and intents of the customer’s queries. By clearly identifying the objective of their queries, it becomes easier for businesses to provide quick resolutions and build strong customer satisfaction and relationships.

With remote-working and hybrid working becoming the norm, the majority of the businesses are relying on automation of tools and processes to improve their productivity and efficiency. Today, Robotic Automation Process (RPA), Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), etc., have become common across different types and sizes of businesses. All of these help businesses to become more efficient, reduce their time, efforts, and money on repetitive tasks, and reduce the risk of errors.

In this post, we will take a look at what Natural Language Processing is, explore intent classification, one of the branches of NLP, the business impact of intent classification, and more.

Table of Contents

What is Intent Classification and NLP?

Natural Language Processing(NLP)

NLP can be defined as a branch of artificial intelligence designed to help computers understand text and speech as humanly as possible. It uses a combination of rule-based modeling of human linguistics and machine learning and statistical modules. Its primary aim is to fully understand the text and speech provided by humans, including the intent and sentiment of the information provider.

Human language is quite complex which makes it difficult to design a software that can completely and truly understand the intent behind the text or speech message. An NLP software helps to break down this text and speech data in different ways to make it easier for the system to understand. Some of the common NLP tasks include:

Speech recognition
Speech/grammatical tagging
Named entity recognition
Word sense disambiguation
Sentiment analysis

Intent Classification

Intent Classification is a branch of NLP which is responsible for automatically categorizing text data into various categories so that it is easier to understand the customer’s intent and requirement. Also known as intent recognition, it is primarily responsible for analyzing the text data and categorizing them into various intents, such as purchase, unsubscribe, downgrade, and demo request. These predefined intentions allow the solution to automatically detect the natural language speech.

Every customer who reaches out to your organization does so with a specific aim or intent. They may either intend to purchase a product/service, request for additional information, wish to unsubscribe, etc. In order to ensure high customer retention and satisfaction, you need to not only quickly provide the resolution but also understand the intent behind their queries. Intent classification is extremely helpful and useful in understanding the intent of your customers’ queries, automating various processes, and gaining deep insights.

Using Intent Classification or categorization, you can find out why your customer has contacted you and what they wish to achieve. Today, customers have the option to contact businesses in various ways with chatbots being one of the most popular forms. Intent Classification is a major aspect of chatbot conversation engine which helps in making the conversation more contextual and offering better customer experience by offering clearer and exact information. Some chatbots with intent classification can automate the interactions either partially or completely.

It is not just computers that can benefit from intent recognition. Human interactions can also benefit from it, for example, by ensuring that the incoming request is directed to the right agent who has the required skills and expertise.

How Does Intent Classification Work?

So, how does Intent Classification work? It largely depends on machine learning and natural language processing to automatically identify and classify various words and expressions into specific intents. The text input is identified by a ‘classifier’ which provides a detailed explanation of what the word means, thus, allowing the computer to understand and identify the intent. The classifier helps to categorize different words in various categories which in turn helps the chatbot to understand the input received.

The first step is to train the intent classifier using text examples or training data. You will also need to define tags with the relevant text examples. For example,

words such as buy, acquire, acquisition, procurement, etc., can be classified as an intent to purchase.
tags, such as need information, unsubscribe, autoreply, interested, etc., can be used for analyzing customer emails.

It is recommended to provide as many examples as possible in order to make the intent classifier as smart as possible. To make it more robust, you can also combine it with text extraction which will enable it to identify specific data, such as company name, location, date, etc. This will help the chatbots to further understand the customer’s intent and provide the most apt solution. For example, if the message received is, “I wish to buy a smartphone from XYZ company. Could you guide me please?” The intent classifier will classify ‘buy a smartphone’ as the intent and the text extractor would extract the company name.

The developers have multiple options to choose from for developing the classifications. These include:

Pattern matching – This option involves using regular expressions (regex) to find patterns in the input and classify them into various intents.
Machine learning algorithms – Machine learning algorithms can be used to create multi-class classification and make the chatbot conversations more contextual.
Neural networks – This is a type of machine learning technique which uses the Artificial Neural Network (ANN) concept to learn the text using word embedding. As per this concept, the systems learn by improving their performance rather than any task-specific programming.

Benefits of Intent Classification

Intent Classification plays a huge role in achieving one of the major goals of every business – customer satisfaction. It allows businesses to improve their customer interactions and build strong relationships with them. Whether you wish to respond faster to the leads, work with several questions, or offer a more personalized service, intent classification can help you do them all!

Apart from the major benefit of enhancing customer satisfaction, some of the other top benefits of using Intent Classification include:

Quick detection of purchase intent allows you to turn every lead opportunity into sales.
You can easily scale up your business as you grow because intent classification is capable of identifying every potential sale, irrespective of the amount of data.
With machines, there is always consistency in the criteria which ensures reduced errors and better data accuracy.
You can get improved analytics about your sales and marketing campaigns for categories like conversion rate, upsell opportunities, interested buyers, etc.

By now you will have a fair idea of how important Intent Classification in NLP is and the impact it has on your business. As mentioned earlier, chatbots are the new-age communication tool and intent classification is one of its key features which will establish whether or not the chatbot is effective in reaching and fulfilling your sales, marketing, and customer satisfaction goals.

In order to achieve your customer satisfaction goals, it is very important to understand their aim/purpose for contacting you and responding as soon as possible. By deciphering the intent of the message that the customer types in the chat box, the system can direct the request towards the right agent or bot in a timely and effective manner.

Along with automatically identifying and classifying the intent of the text and speech data, intent classification can also gauge the mood of the client and determine key information, such as whether they require any special attention. For this, it is imperative that you provide high quality training data so that the intent classification in NLP can accurately recognize and classify the intent.

As a business, you would need to follow two main steps to ensure that your chatbots are correctly set up to carry out meaningful and contextual conversations. The two steps are:

Pre-processing of Natural Language Understanding (NLU) – NLU is a subset of natural language processing that enables human-computer interactions. This subset performs two functions: analyzing the syntax which includes basic grammar, word combinations, word organization, etc., and analyzing the semantics by identifying each word’s context and determining the meaning and relation of the various phrases in the sentence.
Intent classification – The second step is the classification of the intent by providing quality training data. Ensure that you follow the three techniques: pattern matching, machine learning algorithm, and neural networks to ensure that the classifiers have the right training data to perform their classification and intent detection functionalities.

In today’s highly competitive market, these AI-powered chatbots with high quality intent classification are extremely important for businesses to succeed. Apart from focusing on enhancing their customer interactions, intent classification is also effective in enabling businesses to streamline their various operations, find relevant information quickly, take advantage of all their sales opportunities, and gain deep insights about all the critical KPIs.

Conclusion

It is important for businesses to take complete advantage of artificial intelligence, machine learning, natural language processing, etc., to stay one step ahead of their competitors. With intent classification, you can definitely utilize your chatbots to their maximum capabilities and enjoy a higher success rate of sales as well as customer loyalty and satisfaction.