Keywords: convolutional neural network (CNN), ecommerce, customer experience, visual data, image analysis
In this article, we'll take a more detailed look into image recognition and understand:
how convolutional neural networks (CNNs) work
how the technology is used in ecommerce
how Finite's API uses CNN to help beauty brands streamline their sales and better manage their customer experience
A convolutional neural network (CNN) is a type of deep learning algorithm that is specifically designed to process and analyze data from images and videos. In an ecommerce context, a CNN like the one used to build our AI models, identifies and classifies skin type in images to give a predictive response based on the user's unique skin conditions.
The core concept behind a CNN is the idea of convolution, which involves applying a set of filters to an input image in order to extract features and patterns from the data. These filters are trained using a large dataset, and are able to detect specific characteristics of an image, such as edges, textures, and shapes. Once the filters have extracted the relevant features from an image, the data is then passed through several layers of neural networks, where it is processed and analyzed to make predictions and decisions.
Value of AI for ecommerce and beauty brands
Convolutional neural networks can be trained to digest massive amounts of visual data from your brand's audience and can make predictions with about 90% accuracy. Whether a visitor is searching for a particular product or browsing the site, using the AI technology can allow brands to capture their customers unique needs and market their products based on it. This can help increase sales conversions and average order value.
Overall, a convolutional neural network is a powerful tool for analyzing and interpreting visual data in an ecommerce context, and can provide deeper insights for improving customer experience that driving sales.
To see the technology in action. Please request custom demo for your brand below.