eCommerce opens up to artificial intelligence

eCommerce opens up to artificial intelligence

95% of all customer interactions

Today, intelligent machines are not only trying to start dominance in e-commerce-such a future is becoming the vision and wish of the end customers themselves. They are already looking forward to automated checkouts at the supermarket, automated answers to simple phone and online inquiries, and automated parking payments, and from year to year they will be looking forward to more and more of these features-from the key personalisation of product recommendations, to augmented reality, to holograms. Servion global solutions notes that by 2025, artificial intelligence will power 95% of all customer interactions, including phone calls so professionally automated that it will be almost impossible for a customer to recognize a bot.

A new level of personalization

Research conducted for the next level of personalization in retail confirms that consumers want one thing: to get offers that match their needs. It turns out that as many as 40% of the surveyed people who received personalized shopping offers, spent more on shopping than planned.  AI is the future, but not far off-best-in-class eCommerce businesses are already using personalisation to make shopping easy, fast and intuitive at all points of contact. Their success is based on skillfully building customization capabilities, but this does not necessarily require creating a unique experience for each customer at every stage-rather, the goal of leaders is to use technology to personalize critical points of contact in a way that best delivers value for the customer and the seller. When implementing a tool currently being developed that uses artificial intelligence to support ecommerce, we want to be able to group our customers, keeping in mind that they are not one large group that behaves the same way.

On the other hand, we cannot treat each client individually – says Adam Wasilewski, Integration Architect at Fast white cat, the agency Magento, which is currently implementing artificial intelligence into its projects-so we need to find appropriate criteria for allocation to representative groups, look for similarities between individual clients and the features that distinguish them. The fast white cat team is currently working on a special project, co-financed by the ncbr, which will ultimately affect the conversion in the e-shops of our business partners. We will be able to provide the final consumer with a more user-friendly interface, adapted to his behavior in the store. To do this, we will know the behavior of these users in advance and create appropriate groups based on them, which will receive a dedicated user interface, that is, the most adapted to their behavior appearance of the e-store.

One shop-many faces

As a result of such actions, the same store on the desktop or smartphone screen will be noticeably different in the case of two separate consumers-the search engine may be in a different place, favorite filters be bold, and less used-hidden. For the buyer, AI in this case are the same advantages-he will be able to move around the store faster and not waste attention on items that he would never have been interested in anyway. At the moment, our project is at the research stage, we are preparing it for specific partners and on this basis we will create universal groups of consumers – adds Jakub Nadolny – it should be emphasized here that machine learning is an important element of consumer grouping. Categorization of clients is not carried out by a human, but follows from an algorithm. The effect of its action is the allocation of buyers to a specific group . We will examine the effectiveness of machine learning algorithms by comparing store conversion rates before and after personalization implementation.

AI based on machine learning is already used today, for example, by ecommerce giant Amazon-it is believed that they contribute to a 35% increase in total sales. So we have a clear goal: to make friends with AI and sell intelligently.

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