A common approach for executing product recommendations using machine learning, collaborative filtering looks at the collective set of preferences across users and items to learn from users that have similar behavior patterns. Recommendation systems collect both explicit data actively provided by users (such as numeric rating) and implicit data inferred by the system based on a user’s behavior (such as a preference for a certain product after viewing similar ones in the past). These massive datasets allow systems to craft predictions and serve relevant product recommendations tailored to users’ individual affinities and shopping behaviors.
A field of artificial intelligence (AI), computer vision enables digital systems to process, analyze, and make sense of visual data in the same way that humans do. Computer vision uses deep learning and convolutional neural networks (CNN) to derive meaningful information from digital images, videos, and other visual input, and take actions or make recommendations based on that information. A computer vision system uses large amounts of data that is analyzed over and over until it discerns distinctions and ultimately recognizes images. Computer vision is transforming ecommerce as it can identify products along with their characteristics such as color, size, shape, texture, print, and style.
The content-based filtering method uses intrinsic information about items and users to understand what features of an item a user might like. In contrast to collaborative filtering, content-based filtering does not require other users’ data to provide personalized recommendations to a user. This flexibility allows the system to provide highly specific recommendations to the current user, but it is also limited in the sense that it is unable to expand on known user interests.
A standard pillar of an effective marketing strategy, customer segmentation is simply the technique of dividing a company's customers into groups that share a similarity among customers in each group. The overarching goal of customer segmentation is to be able to make decisions about how to engage with customers in each of the segments in order to maximize both the value to the customer, and of each customer to the company. When carrying out customer segmentation, companies can separate consumers by personality traits, psychographic data, certain interests, and habits, as well as a host of other demographic factors, such as where they live, their income levels, and age bracket.