How eCommerce uses Natural Language Processing NLP in 2022

However, the complexity of this, alongside typos can disorient textual search. Natural language is hard to understand for search engines, and it can not differentiate between product names and product descriptions. That is why sometimes it offers irrelevant or results – which can leave the user frustrated. This technology binds human-computer relationship, and leaps and bounds benefit business houses. By leveraging NLP technology, e-commerce businesses can improve efficiency, reduce costs, and drive growth.

NLP in e-commerce

NLP is an inbuilt and powerful technology that helps users find the exact products on the shopping websites without having to choose from different options available from the static searches. Even a dynamic search that is provided on the website suggests the words even before we type may not be all that interesting with the coming age of Artificial Intelligence (AI). NLP can be used to automatically generate product descriptions, which can save businesses time and effort.

NLP applications in eCommerce

An overabundance of knowledge leads to the ‘reinventing the wheel’ syndrome, which has an impact on the literature review process. Thus, scientific progress is hampered at the frontier of knowledge, where NLP can solve many problems. Analysis of customer feedback can be challenging due to the high level of qualitative nuance contained within the material and the vast volume of data obtained by businesses. Because qualitative comments, reviews, and free text are more difficult to quantify than quantitative feedback1, evaluating them may be more difficult.

NLP in e-commerce

In this regards, Kongthon et al.4 implemented the online tax system using natural language processing and artificial intelligence. The majority of high-level natural language processing applications concern factors emulating thoughtful behavior. One of the most significant applications of NLP in e-commerce is sentiment analysis.

Open Datasets for Natural Language Processing

The incorporation of NLP in ecommerce can bring significant improvements in various areas such as search, customer experience, customer support, language detection, sentiment analysis, and targeted marketing. For instance, semantic search uses NLP to understand the intent behind a search query and return more accurate and relevant results. Similarly, NLP-powered chatbots and virtual assistants can improve customer support by providing accurate, natural language responses.

This would make it much easier for shoppers to find what they’re looking for – they could simply search for “black dresses” or “size 10 dresses” and get relevant results. NLP can be instrumental in building this search functionality by understanding the structure and meaning of text. It can help ecommerce businesses automatically categorize products, extract key information, and even generate new product descriptions. According to a study by Capgemini, 68% of consumers are more likely to buy from a website that offers personalized search results.

“Artificial Intelligence in Banking: Enhancing Customer Experience and Streamlining Operations”

Advances in Machine Translation (MT) opens doors for online retailers to expand into international markets and enhance the customer experience across multiple languages. For example, Alibaba Cloud has developed NLP and deep learning technology alongside it’s enormous repository of e-commerce data to provide accurate translation services to partners across the globe. An example of how sentiment analysis can be used in ecommerce is on a fashion retail website. The website can use NLP to analyze customer reviews of a specific product, such as a dress. The sentiment analysis can determine if the reviews are generally positive or negative, providing valuable feedback to the website about the product.

  • It will reduce site abandonment and improve the number of purchases if you present products that are relevant to the customers’ demands.
  • According to Gartner 2019, Natural language product search wasn’t a driving element in the adoption of AI for eCommerce because of our unnatural search patterns.
  • Yes, your “Hey Siri” or “Alexa play rock songs” talks are more complicated than you realize.
  • It combines data with deep learning to represent the product catalog as a “sea of stars” chart.
  • The use cases of NLP in ecommerce are evidently wide-ranging, from improving product search and customer support to targeted marketing and advanced personalization.

Finally, the above model is compiled using the ‘binary_crossentropy’ loss function, adam optimizer, and accuracy metrics. After that, Multi-channel CNN was used, which is quite similar to the previous model. The second layer is the embedding layer, which is applied to the primary layer and contains 100 neurons. The subsequent layers consist of a 1D convolutional layer on top of the embedding layer Natural Language Processing Examples in Action having a filter size of 32, a kernel size of 4 with the ‘ReLU’ activation function. After the 1D convolutional layer, the global max pool 1D layer is used for pooling. After getting the output from the pooling layer, two dense layers are used, with the penultimate layer having 24 neurons and a ‘ReLU’ activation function and a final output layer with one neuron and a ‘sigmoid’ activation function.

Intelligent search functionality

It can deliver better performance than humans in complex tasks such as questioning-answering and machine translation [62]. In addition, the recent advances in the technology and practices are promising for improving scalability and robustness. That is, they herald a shift in how business organisations consume computing resources and deploy NLP applications. This avoids the issues that BERT suffers from by using a technique called ‘permutation language modelling’ [63]. These methods have improved NLP tasks, outperforming state-of-the-art performance as well as obtaining high accuracy.

That includes questions such as why is the shipment delayed or even top customer searches for the past month. Intelligent search helps the employees (but also the customers) to find the information they need faster and easier. Users can use intelligent search to view any information they require, which helps them save time. Without it, employees (and customers) would have to do things the old fashion way and search for the information they need without any help.

How Natural Language Search Can Boost Revenue in Ecommerce

According to Gartner 2019, Natural language product search wasn’t a driving element in the adoption of AI for eCommerce because of our unnatural search patterns. Users expect to be understood and given the information they require when they talk or type in this manner. They respond to the user, generate language, and are thereby controlled by NLG. Ecommerce product search and discovery that increases revenue, conversions, and profit.

Even though there is still a lot of work to do before NLP has the same abilities as humans, it is becoming a helpful tool that people can rely on. Usually, a business would list all of the possible outcomes based on their search query. The better upshot would be to understand customers’ intent and show them what they are looking for. By analyzing the search sessions, (and the products that the customer has bought in the past), it is easier to understand what the customer is looking for. The next time they search for something, they will most likely get the relevant products – based on their previous searches.

A Comprehensive Guide to Attention Mechanisms in NLP and AI for Developers

Read on as we explore the practical applications of NLP in e-commerce and how it can be used to improve efficiency and drive growth. Table 1 summarises several relevant articles and research papers on review analysis. Human communication is a reasonably chaotic ordeal and to understand it, one requires real-world knowledge as the way we communicate can change rapidly. For humans, this is effortless, as we can easily adapt according to the new rules of communication. However, it is not the same as computers as for them, it is a highly complex and computationally costly task.

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