Automatic products categorisation with machine learning

Automatic products categorisation with machine learning

A common problem in large online stores is the need for a product to be present in several categories. With staff turnover, it takes a long time for new employees to study the entire category structure of hundreds of categories. The lack of a product in the right categories reduces the likelihood that it will be noticed by the consumer and purchased. My client's site has over 450 categories and often employees do not add products where they should or do not add them in enough categories. That's why I developed an automatic categorization of products for his online store with the help of machine learning. The system is self-learning, training a model loading the descriptions of all products of all categories and is easy to integrate. When an employee writes the product description, he is automatically offered categories in which to add it. He can remove one or add more. The categories have a written probability of coincidence - the higher the higher the categorization. If you are interested, I can send a link to test the automatic categorization.

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Automatic keyword extraction with machine learning for products filtering

Automatic keyword extraction with machine learning for products filtering

Filters are of great importance for online stores, the more closely the demand can be filtered by the consumer, the more likely he is to come across a product that interests him. Adding filters to products is often a time-consuming and tedious process, so store employees may not spend enough time on it or simply may not have it. In many stores the products are added quickly and there are no filters. My customer with a custom online store created by me with over 50,000 items is needed to add more filters so that users could better refine their searches and find products that interested them more easily. For this purpose, I developed with the help of machine learning keyword extraction from product descriptions in Bulgarian. These keywords were later used to automatically create filters. Site administrators have the option to remove filters from appearing if the filter does not seem appropriate to them or is duplicated by another. It took 15 minutes to generate the filters for 50,000 products. It would take the site staff an awful lot of time to do it by hand.

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How automatic product categorisation by product description with AI can help my e-commerce business

How automatic product categorisation by product description with AI can help my e-commerce business

As an e-commerce business, product categorisation can often be a tedious and time-consuming task. With the rapid advancement of technology, however, artificial intelligence (AI) can now help to automate product categorisation by automatically analysing product descriptions. AI-powered product categorisation can save time and money by reducing the amount of manual labour required to categorise products. This automated process can also provide more accurate and consistent product categorisation which can help to improve the user experience and increase customer satisfaction. AI can also help to improve the accuracy of product categorisation by suggesting more accurate categories based on the product description. This can help customers find the product they are looking for more quickly and easily. Additionally, AI-powered product categorisation can help to identify products that are not currently categorised, enabling e-commerce businesses to make more informed decisions about their product offerings. Overall, AI-powered product categorisation can be a powerful tool for e-commerce businesses. By automating the product categorisation process, businesses can save time and money, improve the accuracy of their product categorisation, and identify new products that can help to increase revenue. With AI, e-commerce businesses can ensure that their product offerings are optimised for the best user experience possible.

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The Benefits of Speech-to-Text Technology for Business Improvement

The Benefits of Speech-to-Text Technology for Business Improvement

One of the key benefits of speech to text technology is its ability to enable enhanced data analysis. By transcribing audio and video data into written text, businesses can more easily analyze and gain insights from their data. There are many ways that businesses can use speech to text to improve their data analysis efforts. Some examples include: Market research: By transcribing focus group sessions, customer interviews, and other market research data, businesses can more easily analyze and gain insights from their data. Product development: Speech to text can be used to transcribe customer feedback, user testing sessions, and other data related to product development. By analyzing this data, businesses can identify areas for improvement and make more informed decisions about product design and development. Customer insights: By transcribing customer calls and other interactions, businesses can gain a deeper understanding of customer needs and preferences. This can help them to tailor their products and services more effectively and improve customer satisfaction. Overall, speech to text technology can be a valuable tool for businesses looking to improve their data analysis efforts. By transcribing audio and video data into written text, businesses can more easily and accurately analyze their data and gain valuable insights. One way that speech to text can be used to improve customer service is by identifying common customer issues and complaints. By analyzing a large volume of transcribed customer calls, businesses can identify patterns in customer feedback and address common problems more effectively. In addition to identifying areas for improvement, speech to text can also help businesses to better understand the needs and preferences of their customers. By analyzing the language and tone of customer interactions, businesses can gain valuable insights into customer sentiment and tailor their service accordingly.

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