Customer Analytics

No-code intent classification to analyze your customer's needs

December 5, 2022

Businesses are increasingly aware of the need to understand their customers better. After all, customer experience is a key differentiator in today’s competitive landscape. One of the most effective ways to achieve this is through the use of intent classification.

In a nutshell, intent classification is a technique that uses machine learning (ML) to analyze customer behavior and understand what they are trying to achieve. It's a powerful tool that can help you improve the customer experience by understanding their needs and catering to them accordingly.

In this article, we will explore what intent classification is, how it works and how you can implement it in your product or service offerings.

What is intent classification?

Intent classification is the process of assigning intents (or categories) to user actions or statements based on machine learning, natural language processing (NLP), and natural language understanding (NLU). It helps businesses understand their customers better and improve their marketing campaigns accordingly. 

Intent classification can be used in many different ways. For example, customers may contact a business through multiple channels such as phone, email, or chat. By understanding the customer's intent, businesses can better route inquiries and improve customer satisfaction. Additionally, analyzing the content users read on a website or share on social media can help businesses focus more on the content that interests them most. 

Intent classification can also be used in marketing campaigns to target users according to their interests. For instance, if you’re running PPC ads or email campaigns, you can use intent classification to target users more effectively. Similarly, if you have a chatbot, intent classification can be used to improve the accuracy of responses given by the system. 

In short, intent classification is a powerful tool that can be used in a variety of ways to better understand customers and improve marketing campaigns.

How does it work?

There are many ways to classify user intents, with traditional methods like rule-based systems and more sophisticated machine learning models. Akkio offers a no-code platform that makes it easy to apply use-cases like text classification and product feedback classification to any business.

Text classification

Text classification is the process of assigning predefined tags or categories to unstructured text data. This can be used to automatically categorize customer support queries, for example. Akkio offers a point-and-click interface that makes it easy to build and deploy text classification models with no coding required.

Customer support is the bedrock of any good business. It can be difficult to keep up with the volume of queries and even more difficult to categorize them. Businesses that fail to do so effectively risk losing customers. 

Akkio offers a solution that can automatically categorize customer support queries, saving valuable time and resources. Businesses could even build a model that prioritizes certain types of queries, ensuring that the most important customers always receive a speedy response.

Product feedback classification

Similarly, product feedback is a valuable source of information for companies. It can help them improve their products and make better decisions about future products.

However, sorting through all of the feedback can be a daunting task. That's why it's important to have a system for classifying it.

There are a few different ways to classify product feedback:

  • By type of feedback (positive, negative, neutral)
  • By product (which feedback is related to which product)
  • By specific issue (which feedback is related to which specific issue)

Classifying product feedback can help companies make the most of it. By knowing what kind of feedback they're dealing with, they can focus on the areas that are most important to them.

With Akkio, you can easily build models to classify product feedback. We provide an automated machine learning platform that makes it simple to get started. Try it out for yourself today and see how easy it is to get insights from your product feedback data.

What techniques are used?

When it comes to understanding the intent of a piece of text, the naive approach would be to look at the words used and try to infer meaning from them. However, this is often not enough. Take, for example, the sentence "I'd love if they provided free breakfast and late check-out options. Next time I'm going to book a hotel with those amenities."

By simply looking at words like "love", "free", and "next time", it would be easy to conclude that the speaker is happy with the hotel they're currently at. However, we can see that the speaker is actually expressing a desire for something more. They want the hotel to provide free breakfast and late check-out options, which suggests that they were disappointed with the lack of those amenities during their stay.

This is where algorithms come in. By using techniques like Neural (Network) Architecture Search, Akkio is able to find the right algorithm for the task at hand. This means that, rather than relying on a human to manually select the best machine learning algorithms, Akkio’s state-of-the-art solution is able to automatically select the best one for the job. This means Akkio searches over neural networks, deep learning, LSTMs, and other artificial intelligence algorithms to create the best language model for any situation.

The first step is to connect training data, which could be a CSV, JSON file, or any other dataset source. Akkio will then automatically build a model depending on the column you select, such as for sentiment analysis, or intent recognition. An intent detection model classifies different intents, such as a user who wants to cancel their appointment, reschedule it, or leave feedback.

Once a trained model has been created, Akkio will automatically conduct validation to ensure high accuracy. Businesses can deploy their intent classifier in any real-world setting with Akkio’s API, or with our no-code integrations.

With legacy systems, engineers would have to use coding languages like Python Pandas, architectures like the BERT transformer, tokenizers, embeddings, and other complex techniques in order to build language models.

How intent classification helps businesses

In a world where the average person is bombarded with over 4,000 marketing messages every day, it's more important than ever for businesses to be able to cut through the noise and identify the intent behind customer text. Here are 7 ways that text intent classification can help businesses.

1. Understanding customer needs better

Text intent classification can help businesses quickly identify and understand the needs of their customers. This information can then be used to create targeted marketing campaigns and product offerings that meet those needs.

One obvious source of this data is Amazon product reviews. Product reviews are often full of unstructured data, making them difficult for businesses to analyze. However, using text intent classification, businesses can quickly and easily identify patterns in customer needs. This information can then be used to improve product offerings and marketing campaigns.

Further, call center (and, more broadly, contact center) data is another great source of customer needs data. Often, this data is unstructured and includes a variety of different channels (e.g., phone calls, emails, and chat logs). Text intent classification can help businesses make sense of this data and identify patterns in customer needs.

Social media and forum posts are another great source of customer needs data. Businesses can use text intent classification to quickly identify which topics are being discussed most frequently and then use this information to create targeted content and product offerings.

2. Identifying opportunities to offer solutions or products

Product and service innovation is a never-ending process for businesses. To be successful, businesses must constantly be on the lookout for new opportunities to offer solutions or products to their customers.

Text intent classification can help businesses identify these opportunities by quickly analyzing large amounts of unstructured text data. This data can come from a variety of sources, including social media posts, customer reviews, and contact center logs.

By using text intent classification, businesses can identify patterns in customer needs and then use this information to develop new products and services that address those needs. This process can help businesses stay ahead of the competition and better meet the needs of their customers.

3. Identifying where the sales funnel breaks

The sales funnel is a key concept in marketing that refers to the journey a customer takes from being aware of a product or service to becoming a paying customer. There are many steps in this journey, and it's important for businesses to identify where the funnel breaks down so they can take corrective action.

Text intent classification can help businesses identify where the sales funnel breaks down. For instance, perhaps a business is losing potential customers at the very beginning of the funnel because they are not providing enough information about their product or service.

By using text intent classification, businesses can quickly identify these sorts of issues and then take corrective action. This process can help businesses improve their marketing efforts and increase sales.

4. Developing targeted marketing campaigns

Personalization is one of the hottest trends in marketing today, and for good reason. By understanding the intent behind customer interactions, businesses can deliver more relevant, targeted content that leads to better customer experiences and ultimately, more conversions.

Consider, for example, a particular customer who searches for "running shoes" on your site. If you can classify that customer's intent as "researching," you know that they're likely just starting their journey and may not be ready to buy just yet. On the other hand, if you classify their intent as "purchasing," you know they're further along in their journey and more likely to convert.

Using this knowledge, you can adjust your marketing campaigns accordingly. For the customer in the research stage, you might want to focus on building brand awareness and providing helpful resources like product reviews and size guides. For the customer further along in their journey, you might want to focus on special offers and discounts to push them over the edge.

5. Improving customer support

In today's fast-paced, digital world, customers expect quick and efficient resolutions to their problems. Having a clear understanding of customer intent can help support teams provide the best possible service. Text classification can be used to automatically categorize customer queries, making it easier to route inquiries to the right department or individual. This can save valuable time and resources, and lead to happier customers.

In addition to routing inquiries more effectively, text classification can also help customer support teams develop new training materials. By identifying patterns in customer queries, support staff can identify areas where customers may need additional education. This information can be used to develop targeted training materials that address common pain points and help customers get the most out of your product or service.

6. Generating leads

In the world of online marketing, one of the most important things you can do is to generate leads. This can be a tough task, as there are a lot of options out there for potential customers. However, one tool that can be extremely helpful in this process is text intent classification.

For example, let's say you sell products that help people to improve their health. If you know that someone is searching for information on how to get healthy, you can target them with health content that specifically speaks to their needs. This will make it more likely that they will take action and become a customer.

7. Improving product development

Text intent classification can be used to automatically categorize customer feedback by type and sentiment. This can help product development teams better understand customer needs and improve the products they develop.

Text classification can be used to identify the overall sentiment of customer feedback. For example, if a large number of customers are giving negative feedback about a particular product feature, the development team can quickly take action to address the issue.

In addition, text classification can also be used to identify specific types of feedback. For example, if a large number of customers are requesting a particular feature, the development team may want to consider adding it to the product.


If you're looking to become a no-code machine learning expert, Akkio is the perfect platform for you. Akkio is an easy-to-use, no-code platform that's specifically designed for businesses. 

With Akkio, you can predict customer behavior, churn rates, and more. This will help you make more informed decisions about marketing campaigns, focus your efforts on the most likely leads, and more. 

Best of all, Akkio is very affordable and doesn't require any external infrastructure or data scientists. So if you're ready to take your business to the next level with machine learning, sign up for a free trial today.

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