Large D2C brands wanted to experiment with trending skincare concepts in the US for their content strategy

Case Study • US Skincare Trends

Summary

A leading personal care brand sought to identify high-engagement content strategies and emerging trends in the US skincare market to inform their 2025 influencer marketing roadmap. Using Convotrack's advanced AI-powered influencer intelligence system, we analyzed 7.7K+ videos from 150+ influencers across Instagram, TikTok, and YouTube, processing a collective audience of 6.75 billion non-unique followers. Using Convotrack’s trend spotter, they identified key trending concepts categorized by engagement metrics, content type distributions, and influencer mix.

Digital Landscape Analyzed

9.9K
videos analyzed across TT, YT and IG
👤
150+
creator accounts analyzed across 3 platforms
👏
1 billion
impressions
💡
8 billion
trending concept vectors identified

Details

Approach

Convotrack's Gen-AI engine used proprietary machine learning algorithms to extract multi-dimensional insights from unstructured video content across major social platforms. Our technology architecture performed natural language processing on captions, analyzed visual content patterns, and mapped engagement metrics against content taxonomies to generate statistically significant trend vectors. The system's computational framework classified content into distinct concept categories (Routine, Aesthetic, Proof-based, and Viral), providing quantifiable performance metrics for each content typology based on engagement rates and view accumulation patterns.

Analysis

The AI system performed deep computational analysis across multiple dimensions of the skincare content ecosystem:

Trending Concept Quantification: The algorithm identified nine statistically significant content concepts showing above-average engagement rates, including "Unwind with me" (16% ER), "Skin Cycling" (14% ER), and "Vanilla Girl" (11% ER). Machine learning classification revealed that these concepts cluster into four distinct content categories.
Engagement-to-Views Correlation Analysis: Advanced pattern recognition detected inverse correlation patterns between engagement rates and total views for certain concepts, suggesting specific content types optimize for either deep engagement or broad reach—critical intelligence for targeted campaign planning.
Platform-Specific Behavioral Mapping: The system's cross-platform analysis revealed distinct content consumption patterns: Instagram optimized for aesthetics (60% of top-performing content), YouTube for educational content (50%), and TikTok for rapid-cycle viral concepts.
Product and Ingredient Sentiment Classification: Natural language processing detected emerging ingredient trends, including polyglutamic acid (54 mentions), beta glucan (43 mentions), and peptides (65 mentions), revealing consumer interest shifts toward science-backed formulations.
Consumer Decision Factor Correlation: AI-driven factor analysis revealed clinical results (42 mentions) and user reviews (38 mentions) as primary purchase consideration drivers, providing quantifiable metrics for messaging optimization.