###Background
TikTok Shop has rapidly become one of the most important platforms for e-commerce growth, especially among Gen Z consumers. According to recent market data, over 40% of Gen Z shoppers prefer discovering and purchasing products directly on TikTok, surpassing traditional search engines or brand websites. 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 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.

###Objective
To create and evaluate a knowledge-graph–based framework that links TikTok Shop video attributes (hooks, camera shots, script phrasing, voiceovers), audience engagement, and gross merchandise value (GMV). We hypothesize that providing marketers with these model-driven insights will enable data-informed content optimization and yield a significant increase in per-video GMV.

###Methods
We will leverage TikTok Shop seller analytics to extract video-level data—hook types, camera shot categories, script phrasing patterns, and voiceover 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. Pattern Inc. manages over 200 e-commerce brands, with more than 20 actively selling through TikTok Shop. These include well-known names such as Philips, Optimum, and Thorne—all with verified GMV and TikTok sales data. Additionally, we will test our system with a smaller cohort of brands to validate performance across brands at different maturity levels.
We will evaluate pre- vs. post-system performance (including data extraction, modeling, insight delivery, and dashboard interaction) using paired t-tests and time-adjusted regression models.

###Results
Results are still pending. We expect the execution of this study to be complete by the end of June 2025. We anticipate that our LLM-powered, knowledge graph–driven feedback system will significantly improve the ability of brands to interpret TikTok video performance, adjust creative strategies, and increase GMV per video.