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Uncovering Untapped Skincare Opportunities
Ingredient Analytics for Aging, Texture, and Barrier Health
In a market flooded with similar skincare products, true innovation means finding what’s missing—not just repeating what’s popular. For this project, I helped a hypothetical skincare brand identify high-performing, underused ingredient combinations for aging, skin texture, and barrier health. Using webscraped product data and ingredient analysis, I revealed overlooked formulation opportunities and mapped out actionable paths for differentiation in an oversaturated industry.
Role
Data Analyst, Ingredient Researcher, Market Strategist
Tools Used
Webscraper.io & Google Sheets
Timeline
Started: 6/21/25
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Completion: 7/13/25
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Duration: ~3 weeks (part-time)
Status
Complete
Skills Showcased
Data scraping, market research, ingredient analysis, data visualization, business insight generation
Problem Statement
The skincare industry is oversaturated, especially in the anti-aging and barrier health segments, with countless products repeating the same ingredient stories (think retinol + hyaluronic acid serums). Brands struggle to stand out, and consumers face decision fatigue. The challenge: How can ingredient-level data reveal untapped product opportunities for aging, texture, and barrier health—without duplicating what’s already on the shelves?
Goals & Questions
Data Sources
Webscraped 634 skincare products from INCI Decoder, focusing on ingredient combinations for aging, texture, and barrier health; condensed to 343 products after cleaning and reducing overlap.
Ingredient and benefit data cross-referenced with a master skincare ingredient sheet compiled from reputable public ingredient databases.
Additional manual research on ingredient combinations was conducted using publicly available skincare resources and expert commentary from prior personal work.
Data Cleaning & Prep
Cleaning Steps
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Standardized tags and phrasing for consistency (e.g., unified “Barrier Repair” variants).
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Removed duplicates and consolidated overlapping benefits and concerns.
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Fixed delimiter and spacing issues to ensure formula accuracy in Google Sheets.
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Checked for missing or incomplete tags and corrected gaps.
Structural Transformation
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Used array formulas to break out multi-tag cells, linking each ingredient to individual benefit and concern tags (one tag per row).
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Reformatted data for easy pivot table analysis and accurate tag frequency counts.
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Standardized manually curated ingredient combos for consistent analysis.
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Created bar charts to visualize key findings
Tooling
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All data cleaning and prep performed in Google Sheets using advanced formulas (ARRAYFORMULA, SPLIT, FLATTEN, FILTER) for tag processing.
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Manual verification and controlled vocabulary lists ensured tag consistency.
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Used pivot tables for data validation and analysis.
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Created bar charts directly in Google Sheets for visualization
Exploratory Data Analysis
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Analyzed frequency of ingredient combos in products targeting aging, texture, and barrier health
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Mapped top 21 ingredient combos and identified 10 underrepresented or missing combinations
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Cross-checked for ingredient compatibility and formulation feasibility
Key Findings
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Reflection & Learning Outcomes
Innovation Insights
Ingredient-level analytics can reveal genuine innovation opportunities even in a crowded market.
Skill Growth
This project sharpened my skills in data scraping, ingredient mapping, and translating technical findings into business strategy.
Data Priorities
Data cleaning and focusing on current, in-market products is crucial for actionable insights. Next time, I’d prioritize scraping for availability and pricing first.
Future Vision
With more time, I’d build a tool or dashboard for real-time ingredient opportunity mapping, or expand the analysis to include consumer reviews and pricing for a more holistic view.