Artificial Intelligence - A Real Prize for E-commerce

While we’re only now starting to experience the dawn of artificial intelligence, Salesforce expects this technology to generate $ 1 trillion in revenue across the entire CRM market by 2021. For experts, this is not much of an area for revolution, but for many, AI is still a mysterious, dark art. Here's what you need to know to get started.

Artificial intelligence in e-commerce is not just the history of chatbots! The basic principle of AI is self-learning through the content to which it is exposed. An example of this is image recognition software that can be trained to recognize very specific elements, such as the image of a cat across thousands of photos. Through exposure, the software will learn to reproduce this analysis on all new images. In this kind of process, there are two consecutive phases; first the learning process, and then the application and improvement of the software. Be aware that, regardless of whatever context, three conditions must be met for such a project to have a sound foundation- the software, great processing power (usually from the cloud), and a data source. It is the latter which is the most difficult to obtain, in both quantity and quality.

AI in E-commerce- What Results are to be Gained from This?

By understanding and anticipating customer behavior through sorting and categorization, artificial intelligence opens the way to increased personalization in the context of e-commerce activities. By using your site's sales history and traffic, for example, it is possible to create machine learning models to compare the behavior of internet users according to different scenarios (whether to push recommendations or not). This allows for the automatic identification of underlying behavioral patterns, for example, seeing an increase in revenue generated from a group to which a specific recommendation was pushed as well as being able to predict the likelihood of a user deciding whether to purchase a product or not. Salesforce Commerce Cloud now offers a "Product Recommendations" feature, which pushes the best offer for users based on their browsing behavior. "Predictive Sort" which has been available since July, uses machine learning to personalize the order in which products appear in search results. A predictive model is created for each buyer based on their purchase history and navigation behavior. These actions are automated by artificial intelligence, which frees up time for online retailers to do other tasks.