DYNAMIC SHORT-FORM VIDEO TRAVERSAL WITH MACHINE LEARNING IN AN ECOMMERCE ENVIRONMENT

Smart overview of the Invention

Techniques for dynamic short-form video traversal leverage machine learning within an ecommerce setting. A graph structure linked to a library of short-form videos is customized based on the products available on a website. Users are presented with these tailored videos, which include interactive overlays that enhance the shopping experience.

Video Consumption Behavior Analysis

As users engage with the videos, their viewing behaviors are tracked and analyzed by a machine learning model. This model identifies patterns in video consumption and user interactions to recommend subsequent videos that align with sales objectives. The goal is to optimize viewer engagement and drive product sales through targeted content.

Machine Learning Model Functionality

The machine learning model not only analyzes existing video data but can also synthesize new short-form videos when necessary. This functionality allows for the integration of fresh content into the graph structure, ensuring that users remain engaged and that their specific queries or interests are addressed effectively.

Interactive Elements for User Engagement

An interactive overlay is incorporated into the video experience, enabling viewers to provide feedback, ask questions, and interact with the content. This engagement helps to maintain viewer interest and can lead to higher sales conversions as the model learns which sequences of videos are most effective in prompting purchases.

Evolution of Ecommerce Video Strategies

The approach described emphasizes the importance of identifying successful patterns among short-form videos to enhance ecommerce strategies. By utilizing a decision-support model, businesses can better understand user preferences and behaviors, ultimately leading to improved marketing outcomes and customer satisfaction in the rapidly evolving ecommerce landscape.