The Use of AI on E-commerce System

Ecommerce systems have become an integral part of our lives recently. Whereas human being has also been witnessing the boom of Artificial Intelligence (AI), significantly impacted on every single aspect of human life. This article describes a couple use cases of applying AI on ecommerce systems, from business and user experience perspectives.

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Sales and Marketing

Machine learning, a branch of AI, has revolutionised the way businesses operate, particularly in the areas of sales and marketing. By leveraging machine learning, businesses can analyse large volumes of data to gain insights into customer behaviours and preferences and use this information to improve sales and marketing strategies. Following are some business cases by leveraging machine learning to support sales and marketing:

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Personalised Marketing

Machine learning can be used to analyse customer data and provide personalised marketing recommendations. By analysing customer behaviour and preferences, businesses can tailor their marketing messages to individual customers, increasing the likelihood of making a sale.

Predictive Analytics

Machine learning can be used to analyse historical data and make predictions about future customer behaviour. This information can be used to identify potential customers and target them with marketing messages, increasing the chance of sale.

Sales Forecasting

Machine learning can be used to analyse sales data and make predictions about future sales. This information can be used to optimise sales strategies, such as pricing and promotion, to maximize revenue.

Customer Segmentation

Machine learning can be used to segment customers based on their behaviour and preferences. This information can be used to target specific customer segments with marketing messages, increasing the likelihood of making a sale.

Lead Scoring

Machine learning can be used to analyse customer data and score leads based on their likelihood of making a purchase. This information can be used to prioritise sales efforts, focusing on leads that are most likely to convert.

Chatbots

Machine learning can be used to develop chatbots that can provide personalised customer service and support. Chatbots can analyse customer interaction and transaction information to provide relevant recommendations and support, increasing customer satisfaction, engagement, and loyalty. Chatbots can handle customer queries and complaints 24/7, reducing the need for human intervention. Some Chatbot systems even can use natural language processing (NLP) to understand customer queries and provide relevant responses. This has led to improved customer satisfaction and reduced response times. Chatbot can significantly reduce the cost of customer service. Chatbots can handle a large number of customer queries simultaneously, reducing the need for human customer service representatives.

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User Experience

With the rise of deep learning technology, ecommerce systems can now process images and voice commands to support searching, in addition to traditional text search. Using deep learning to process image and voice searches on ecommerce systems can bring a of user experience to end user.

Image Search

Traditionally, ecommerce systems have relied on text search to help customers find products. However, with the rise of deep learning technology, ecommerce systems can now process images to support searching. This means that customers can now upload an image of a product they are looking for, and the ecommerce system will use deep learning algorithms to identify similar products.

It provides customers with a more intuitive way to search for products. Instead of having to describe a product in words, customers can simply upload an image (taken by mobile phone, screen capture, or from Internet source…) of the product they are looking for. Moreover, it allows ecommerce systems to provide customers with more accurate search results. By using deep learning algorithms to analyse images, ecommerce systems can identify products that are similar to the one the customer is searching for, even if the customer doesn't know the name of the product.

Voice Search

Another way that deep learning is revolutionising ecommerce systems is through voice search. With voice search, customers can use their voice to search for products, instead of having to type in a search query. This is particularly useful for customers who are on the go or who have limited mobility.

Firstly, it provides customers with a more convenient way to search for products. Instead of having to type in a search query, customers can simply speak their search query out loud. Secondly, it allows ecommerce systems to provide customers with more personalised search results. By using deep learning algorithms to analyse voice commands, ecommerce systems can identify the customer's preferences and provide more relevant search results.

In conclusion, machine learning has transformed the way businesses approach sales and marketing. By leveraging machine learning, businesses can gain insights into customer behaviour and preferences, and use this information to improve sales and marketing strategies. Personalised marketing, predictive analytics, sales forecasting, customer segmentation, lead scoring, and chatbots are just some of the business cases for leveraging machine learning to support sales and marketing.

Furthermore, deep learning technology is revolutionising ecommerce systems by allowing them to process images and voice commands to support searching. Image search and voice search provide customers with more intuitive and convenient ways to search for products, while also allowing ecommerce systems to provide more accurate and personalised search results. As deep learning technology continues to evolve, we can expect ecommerce systems to become even more sophisticated, providing customers with an even better shopping experience.