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How artificial intelligence benefits e-commerce?
Have you ever heard of artificial intelligence (AI)?

If for some it still evokes only science fiction and robotics, it still fits in all aspects of our daily lives. From automatic cash registers to advanced security controls in airports: nowadays, artificial intelligence is almost everywhere. And, little by little, she begins to interfere in e-commerce.

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Moreover, many companies are already taking advantage of the latest advances in AI and machine learning (ML) to provide a better shopping experience for their customers. As it improves, artificial intelligence could therefore change forever, and will most likely change the landscape of e-commerce in the years to come.

By definition, artificial intelligence is the ability of a machine to perform “smart” tasks, such as learning and decision-making, as a human being would do.

Machine learning is a current AI application based on the idea that we should be able to give machines access to data and let them learn on their own.

Applied to e-commerce and marketing, machine learning corresponds to the various methods of data analysis in which computers find information without being told exactly where to look for this information. ML algorithms, when exposed to massive amounts of data, can extract models and use them to generate ideas or predictions about future conditions.

Although still relatively new, artificial intelligence has already had a huge impact, in a short period of time, on industries such as finance or healthcare. And the benefits of AI are now starting to spread in e-commerce.

It is important to note that artificial intelligence by itself is not a product, but a powerful tool for creating better products that meet the needs of customers. Yes, even if it may seem paradoxical for a machine, the greatest strength of artificial intelligence is that it can help e-commerce to create a more humane customer experience by personalizing it!

Indeed, an online sales activity generates monumental volumes of data from dozens of channels. There is even too much data for a human being to know where to look for or even what he is looking for – the perfect conditions for machine learning.

As a result, many e-merchants are already trying to differentiate themselves by using forms of AI to better understand their customers, generate new leads and provide an improved customer experience.


Personalization in e-commerce is not new. Many businesses and e-merchants currently use a filtering system to provide customers with product recommendations. These filters usually base their results on bestseller data, consultation history, and other general aggregation parameters.

At best, the most successful referral systems can remember what your client likes.

But, you will agree, all this remains a bit impersonal. “People who bought this product also bought this product” is not the best way to personalize an offer.

This is where AI comes in.

While the word “artificial” connotes some dehumanization, artificial intelligence instead allows merchants to set up a more personalized customer experience by providing recommendations to subscribers according to their preferences.

How ? With the ability of AI to more effectively analyze than a human being from large data sets. This means that the technology can quickly analyze different aspects of the navigation behavior. Whenever a user examines a product, posts a message or even a tweet about it, the information can be used.

Artificial intelligence technology is also able to learn the interests, passions and triggers that make a consumer more likely to make a purchase.

In other words, millions of transactions and communications can be analyzed each day to target offers to a single customer.

By exposing machine learning algorithms to truly massive amounts of data, marketers can build automated analytic models that are not This is not limited by the ability of humans to suggest why some people buy particular products. Such AI-based applications can uncover better ways to model user behavior. Finally, technology facilitates: the sales process, by identifying who is most likely to buy a product (based on history of past purchases, demographics, etc.) customizing the sales cycle , allowing you to engage the right prospects with the right message at the right time Example of using the AI for personalized recommendations: Starbucks recently launched “My Starbucks Barista”, which uses AI to allow customers to move voice or email commands. The algorithm relies on a variety of inputs, including account information, customer preferences, purchase history, third-party data, and contextual information. The coffee giant can provide more personalized messages and recommendations to its customers. FIND CUSTOMER POTENTIALS According to a recent study, at least one third of prospects are not followed by the sales team. Which means that pre-qualified potential buyers interested in your product or service are forgotten. In addition, many companies are overloaded with customer data that they operate little, if at all. However, it is a gold mine that can be used to improve the sales cycle. In the retail industry, for example, artificial intelligence is used with facial recognition to capture a customer’s behavior in a specific way. shop. Basically, if a consumer lingers for a while in front of a product – a coffee maker for example – this information will be stored for use on his next visit. As AI improves and develops, you can even start seeing special offers on your computer screen based on your wait time in the store or even your reaction to a product! Microsoft offers for example “Mall kiosk”, which recommends products through facial or voice recognition of reactions. CREATING AN EFFECTIVE SALES PROCESS WITH A VIRTUAL ASSISTANTThanks to virtual assistants, online businesses can leverage AI to appropriately select and recommend products that a buyer wants and need, while avoiding the need for a buyer. to have to do all the research work in the catalog. For example, the integration of artificial intelligence to your CRM will customize your solutions and create an effective sales message. Indeed, if your AI system allows learning natural language and voice input, like Siri or Alexa, your CRM will respond to customer requests, solve their problems and even identify new sales opportunities. Better yet? Some AI-managed CRM systems can be multitasked to handle all these functions and more. In this case, artificial intelligence helps users dive deeper into e-commerce product catalogs to find the perfect item that otherwise might not be discovered. There are also several virtual assistant technologies online. These robots use large sets of data, collected in real time, to “learn” the buying habits, interests and personal tastes of users. Example of an online virtual assistant: You may have heard of “Mona”, the virtual sales assistant developed by former employees of Amazon. It helps simplify mobile shopping and offers customers the best deals to suit their preferences. The more time users spend interacting with the Mona robot, the better they will know it. Virtual Assistant Usage Example: The North Face brand harnesses the power of virtual assistants to better understand their customers while offering recommendations on -measured. With the help of IBM’s intelligence solution called Watson, the company allows buyers to discover their ideal jacket. For this, several questions are asked to customers, such as: “where and when will you use your jacket? “. IBM’s software then scans hundreds of products to find the best matches based on correlated responses to other data, such as weather conditions. To get an idea, you can test the tool here. E-commerce north faceIre ecommerce north faceAI e-commerce north faceAI ecommerce north faceIntelligence artificial north faceThe North Face artificial intelligence demonstration BEST RESEARCH RESULTSAt least 30% of online shoppers use the search function of an e-commerce trade. However, it is often a tedious task for the consumer who is forced to choose, then refine a keyword accurately describing the product he is looking for.The scenario often takes place in the following way: a consumer between “smartphone with the best camera “in the search bar. While a human interlocutor would immediately understand the request, or ask questions to get more details about the client’s needs, the numerical results provided are often beside the plate. In short, in most cases, the search does not lead to the expected result. This is explained by the lack of context concerning the user, the rigid and irrelevant filters, and the problems related to the understanding of the keywords. . In fact, the algorithms of these e-commerce search engines have neither the practical intelligence nor the ability to understand a query with the nuances of the language. The key is to use the power of machine learning to ‘improve results for consumers who use research. The ML can also generate a search ranking, which allows the site to sort the search results by relevance, instead of matching a keyword. By doing so, e-commerce platforms will be able to transform a massive number of searches. research experiments fail in successful conversions.To replace text searches, a solution is also starting to be implemented: visual search – a technology that uses artificial intelligence to analyze a photo submitted by a customer, then find the product The visual search allows customers to take a picture of a product they like, and then download it. The AI software is then able to evaluate this specific product, its brand, its shape, its style, its fabric, its color, etc., and then to suggest similar products likely to interest the customer. Finally, in In addition to using images to search for products they want to buy, consumers will be able to use voice search – the ability to search for objects using speech. Voice search uses AI to understand what is being said and to improve the recognition of voices and phrases. Voice research has been popularized with voice assistants like Alexa and Siri, forcing e-merchants to re -optimize their web pages, including their FAQ, to respond to voice-based searches. To learn more, you can also read our article: how to adapt your e-commerce site to voice search? Example of using the AI for search results: A company that uses machine learning to provide better search results is eBay. With millions of items listed, the auction site harnesses the power of AI and data to predict and display the most relevant search results. Example of Using Visual Search: One of the Companies innovative in terms of visual search is Neiman Marcus. With its application “Snap. Find. Shop. The fashion and beauty brand allows users to take pictures of real-world objects and then find them in the catalog. IMPROVING CUSTOMER SERVICE If your business deals with customers on a daily basis and you encounter recurring issues or questions, creating a chatbot is a good way to provide customers with information faster and more efficiently than a service representative. For simplicity, chatbots are automated programs that can “converse” with people to answer questions and perform specific task queries. They have been around for quite some time now, but have made considerable progress in their ability to adapt to the customer through the machine learning process.Concretely, chatbots can help you reduce customer service costs and better dialogue. with consumers, 24 hours a day, 7 days a week. They also offer a good opportunity to customize consumer recommendations based on the conversation history and can actively take on some of the important responsibilities related to the execution of an online business, such as order process automation.

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