TikTok Shop has rapidly become one of the most important platforms for e-commerce growth, especially among Gen Z consumers. As a result, e-commerce brands are shifting significant attention and ad spend toward TikTok Shop, where short-form video content is directly tied to conversion outcomes.
Despite this, most social media marketers still rely on vanity metrics (likes, shares, and follower counts), which often fail to reflect true sales impact. With TikTok Shop now offering video-level sales and gross merchandise value (GMV) data, brands can, for the first time, create a closed-loop marketing system where creative choices can be evaluated and optimized based on actual revenue performance.
This session will show how we are creating and evaluating a knowledge-graph–based framework that links TikTok Shop video attributes (hooks, camera shots, script phrasing, voiceovers), audience engagement, and GMV.
We will leverage TikTok Shop seller analytics to extract video-level data (hook types, camera shot categories, script phrasing patterns, and voice-over styles) alongside engagement metrics (views, watch time) and sales outcomes (GMV, units sold). These elements will be represented in a Neo4j-based knowledge graph, linking videos, products, and audience segments.
To conduct a deep dive into each video’s creative and performance elements, we will use advanced LLMs and multimodal Vision models to analyze video content frame-by-frame. These models will automatically identify and categorize visual, audio, and narrative features, and convert them into structured GraphDB elements, enabling deeper semantic analysis and insight generation.
We will test this framework at scale using real TikTok Shop brand data and evaluate pre- vs. post-system performance (including data extraction, modeling, insight delivery, and dashboard interaction) using paired t-tests and time-adjusted regression models.
Speakers
Taeyang Kim
Machine Learning Engineer, Pattern Inc.
Taeyang Kim is a dynamic machine learning engineer and computer scientist with a passion for innovation and a track record of excellence in the field of data science and artificial intelligence. He began his Master of Science in Computer Science at the Georgia Institute of Technology in January 2024, building upon his strong foundation from Brigham Young University (BYU), where he graduated with a Bachelor of Science in Computer Science, emphasizing data science.
With a blend of academic excellence, professional expertise, and innovative project experience, Taeyang Kim brings a wealth of knowledge and a fresh perspective to the field of machine learning and data science. His contributions have not only advanced technologies within his organizations but have also set the stage for future innovations in the industry.
David Fagerburg
Software Engineer, Pattern Inc.
David Fagerburg is a highly motivated software engineer with a passion for learning and helping others grow.
While studying computer science at Brigham Young University, David worked at SkillStruck to help create flexible web-based computer science curricula. He graduated in 2022 with his bachelor's in computer science.
Since graduating, David has worked at Pattern as a software engineer and has led and contributed to the development of internal applications to streamline the e-commerce acceleration platform that Pattern provides.
Outside of work, David enjoys completely unplugging and going backpacking, fishing, and shooting archery, finding inspiration and relaxation in the great outdoors.
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