Gartner: Top strategic technology trends for 2024

Generative AI involves using machine learning algorithms to create realistic and coherent outputs based on raw data and training data. Generative AI models use large language models (LLMs) and NLP to generate unique outputs for users. NLP is broadly defined as the automatic manipulation of natural language, either in speech or text form, by software. NLP-enabled systems aim to understand human speech and typed language, interpret it in a form that machines can process, and respond back using human language forms rather than code. AI systems have greatly improved the accuracy and flexibility of NLP systems, enabling machines to communicate in hundreds of languages and across different application domains. In conclusion, NLP is a powerful tool that can help e-commerce businesses improve their customer experience and gain a competitive advantage.

As the amount of electronically available text in education is increasing rapidly, NLP can be effective in organising the relevant text for teaching. For, it can be a very onerous task for teachers to identify appropriate materials for effective input during lectures. That is, teaching with the most up to date course materials is beneficial for both students in learning and teachers in teaching the subjects more effectively and efficiently [41]. In addition, NLP can be effective in research, especially in formulating meaningful extractions from bodies of literature (systematic reviews, scoping reviews, meta-analyses etc.). In addition, NLP can be effective in processing qualitative data, such as those collected from interviews, in various formats including audio, video and text. Moreover, it can be used for converting audio translations from one language to other or into text, thereby assisting with data analysis [60, 61].

Technology innovations in communication and interactions

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). The use of NLP in e-commerce apps makes search functionality smooth and customer-friendly. It helps users to search for products by giving voice commands and making their custom search faster and easier. There are great benefits to integrating NLP capabilities into e-Commerce applications. Online retailers can implement NLP applications and improve customer experiences.

By analysing customer data, NLP algorithms can suggest products that are likely to be of interest to each individual customer, improving the overall shopping experience. NLP can be used to automatically generate product descriptions, which can save businesses time and effort. By analysing product features and customer reviews, NLP algorithms can generate high-quality descriptions that are both informative and engaging. Semantic search, powered by NLP allows for larger search queries, overlooking typos and even identifying synonyms.

Customer Sentiment Analysis: Leveraging Conversational Data for Better Outcomes

They will at least have an idea or a product in mind that they are searching for. If someone does not know what they are looking for, they will not use the search option. If a customer is looking for an item with more than one meaning, we can show all of the results. However, showing all of those results can lead to the customer seeing a lot of irrelevant items. If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

Liang et al.7 propose a SenticNet-based graph convolutional network to leverage the affective dependencies of the sentence based on the specific aspect. Specifically, the authors build graph neural networks by integrating SenticNet’s affective natural language processing examples knowledge to improve sentence dependency graphs. Automation makes the process of changing call centers without the need for a human agent simple. They’ve established themselves as a valuable asset in their customer service department.

Artificial Intelligence

In this article, we will explore the use cases of NLP in retail, eCommerce, and marketing and discuss the benefits of using this technology. Companies are now deploying NLP in customer service through sentiment analysis tools that automatically monitor written text, such as reviews and social media posts, to track sentiment in real time. This helps companies proactively respond to negative comments and complaints from users. It also helps companies improve product recommendations based on previous reviews written by customers and better understand their preferred items. Without AI-powered NLP tools, companies would have to rely on bucketing similar customers together or sticking to recommending popular items. The polarity determination of text in sentiment analysis is one of the significant tasks of NLP-based techniques.

• Some promising NLP systems were developed in the 1960s, which have been updated since through various techniques and new conceptualisations.
• On the other hand, NLP can take in more factors, such as previous search data and context.
• Predictive analytics with eCommerce intelligence can assist you in the correct inventory management so that your online shop has just the right amount of certain products.
• Honest customer feedback provides valuable data points for companies, but customers don’t often respond to surveys or give Net Promoter Score-type ratings.
• As such, custom tokenization helps identify and process the idiosyncrasies of each language so that the NLP can understand multilingual queries better.

Table 4 shows the overall result of all the models that has been used, including accuracy, loss, validation accuracy, and validation loss. GloVe32 is a distributed word representation model derived from Global Vectors. The GloVe model is an excellent tool for discovering associations between cities, countries, synonyms, and complementary products.

Reinvent What Your Business Could Be

It also estimates that home robots or domestic robots will contribute $11 billion in revenue by 2020. Additional techniques like custom tokenization can specify how NLP should break each language down into discrete units. In most Western languages, we break language units down into words separated by spaces. But in Chinese, Japanese, and Korean languages, spaces don’t divide words or concepts.

This makes it more critical to ensure that the use of IT becomes more efficient, circular and sustainable,” Gartner stated. “Generative AI (GenAI) is becoming democratized by the confluence of massively pretrained models, cloud computing and open source, making these models accessible to workers worldwide,” Gartner stated. By 2026, more than 80% of enterprises will have used GenAI APIs or models or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023, according to the firm. We, humans, understand the word in the context of the sentence spoken or written and we do it more efficiently and effortlessly. But to teach the computer the context in which the sentence is spoken or written is quite a task.

How Natural Language Processing Can Help Product Discovery

With the growing amount of transactions, returns, complaints, and other kinds of customer inquiries, the retailers started to reach out for advanced automation in order to gain a competitive edge. NLP is one of the techniques that enables them to cope with the dynamically changing market requirements and provide the best experience to their clients. Streamlining the processing of inquiries, automating customer service, and many other ways are covered further in this article. E-commerce uses social media for monitoring, customer interviews, and reviews to get feedback on their products. Give your customers the right answer every time and provide a better customer experience. Now more than ever, they want to fully self-serve on your websites and mobile apps — virtual agents and intelligent search allow your customers to achieve independence.

Sometimes reviewers write a lot about their lifestyle and the use case they have found for the product as well. This can provide insights into things like the product-market fit or the value proposition for the product. We can also find opportunities or gaps in a category and hence get the “voice of the customer” to create a new product or even start a new business (Sri 2021). Bots are learning faster and faster, and that’s good news for retailers which spend a lot of money on customer service due to processing refunds, returns, and other e-commerce-related matters. With chatbots trained with the content of the solved queries, they can reduce the tasks their customer agents have to deal with in their daily work.

Why Is Natural Language Processing Important?

As the voice search is gaining popularity, they also need to rethink their approach to optimization. But NLP also creates opportunities for the digital marketing team – here are some of the most interesting examples of such. When it comes to virtual assistants that we use in everyday life, it’s essential for the retailers to optimize their stores for speech search which we have described above. And the bots are more and more commonly becoming a sort of virtual assistants as well, being able to solve complex problems of the customers, provide them with suggestions, or guide them through the purchase process.