Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) by Service/Product
Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) Based on Application
Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) Based on Technology
Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) Based on Distribution Channel
Global Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) Based on Region
Europe Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) by Country
North America Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) by Country
Asia Pacific Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) by Country
Latin America Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) by Country
Middle East & Africa Artificial Intelligence (AI) in Beauty and Cosmetics Market Revenue (US$ Mn) by Country
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Beauty and Cosmetics Market Snapshot
Chapter 4. Global AI in Beauty and Cosmetics Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Porter's Five Forces Analysis
4.7. Incremental Opportunity Analysis (US$ MN), 2024-2031
4.8. Global AI in Beauty and Cosmetics Market Penetration & Growth Prospect Mapping (US$ Mn), 2023-2031
4.9. Competitive Landscape & Market Share Analysis, By Key Player (2023)
4.10. Use/impact of AI on Radiopharma Industry Trends
Chapter 5. AI in Beauty and Cosmetics Market Segmentation 1: By Service/Product Types, Estimates & Trend Analysis
5.1. Market Share by Service/Product Types, 2023 & 2034
5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 & 2034 for the following Service/Product Types:
5.2.1. Personalized Recommendation Tools
5.2.2. Virtual Try-On and Augmented Reality (AR) Tools
5.2.3. Skin and Hair Analysis Platforms
5.2.4. Chatbots and Virtual Assistants
5.2.5. AI-Based Beauty Devices
5.2.6. Demand Forecasting and Supply Chain Tools
5.2.7. Performance Marketing Measurement Platforms
Chapter 6. AI in Beauty and Cosmetics Market Segmentation 2: By Applications, Estimates & Trend Analysis
6.1. Market Share by Applications, 2023 & 2034
6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 & 2034 for the following Applications:
6.2.1. Skincare
6.2.2. Makeup
6.2.3. Haircare
6.2.4. Fragrances
6.2.5. Nail Care
6.2.6. Body Care
6.2.7. Wellness Products
Chapter 7. AI in Beauty and Cosmetics Market Segmentation 3: By Technologies, Estimates & Trend Analysis
7.1. Market Share by Technologies 2023 & 2034
7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 & 2034 for the following Technologies:
7.2.1. Computer Vision
7.2.2. Machine Learning (ML)
7.2.3. Natural Language Processing (NLP)
7.2.4. Generative AI
Chapter 8. AI in Beauty and Cosmetics Market Segmentation 4: By Distribution Channels, Estimates & Trend Analysis
8.1. Market Share by Distribution Channels, 2023 & 2034
8.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2021 & 2034 for the following Distribution Channels:
8.2.1. E-Commerce Platforms
8.2.2. Mobile Apps
8.2.3. In-Store Technologies
8.2.4. Social Media
8.2.5. Specialty Retail
Chapter 9. AI in Beauty and Cosmetics Market Segmentation 5: Regional Estimates & Trend Analysis
9.1. Global AI in Beauty and Cosmetics Market, Regional Snapshot 2023 & 2034
9.2. North America
9.2.1. North America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
9.2.1.1. US
9.2.1.2. Canada
9.2.2. North America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Service/Product Types, 2021-2034
9.2.3. North America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Applications, 2021-2034
9.2.4. North America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Technologies 2021-2034
9.2.5. North America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Distribution Channels, 2021-2034
9.3. Europe
9.3.1. Europe AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
9.3.1.1. Denmark
9.3.1.2. Finland
9.3.1.3. Austria
9.3.1.4. France
9.3.1.5. Germany
9.3.1.6. Israel
9.3.1.7. Italy
9.3.1.8. Netherlands
9.3.1.9. Norway
9.3.1.10. Poland
9.3.2. Europe AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Service/Product Types, 2021-2034
9.3.3. Europe AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Applications, 2021-2034
9.3.4. Europe AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Technologies 2021-2034
9.3.5. Europe AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Distribution Channels, 2021-2034
9.4. Asia Pacific
9.4.1. Asia Pacific AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
9.4.1.1. Australia
9.4.1.2. China
9.4.1.3. India
9.4.1.4. Indonesia
9.4.1.5. Japan
9.4.1.6. Malaysia
9.4.1.7. Philippines
9.4.1.8. Singapore
9.4.1.9. South Korea
9.4.1.10. Taiwan
9.4.1.11. Thailand
9.4.1.12. Rest of APAC
9.4.2. Asia Pacific AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Service/Product Types, 2021-2034
9.4.3. Asia Pacific AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Applications, 2021-2034
9.4.4. Asia Pacific AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts By Technologies 2021-2034
9.4.5. Asia Pacific AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Distribution Channels, 2021-2034
9.5. Latin America
9.5.1. Latin America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Country, 2021-2034
9.5.1.1. Argentina
9.5.1.2. Brazil
9.5.1.3. Mexico
9.5.1.4. Rest of LATAM
9.5.2. Latin America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Service/Product Types, 2021-2034
9.5.3. Latin America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Applications, 2021-2034
9.5.4. Latin America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Technologies 2021-2034
9.5.5. Latin America AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Distribution Channels, 2021-2034
9.6. Middle East & Africa
9.6.1. Middle East & Africa AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
9.6.1.1. Egypt
9.6.1.2. Israel
9.6.1.3. Nigeria
9.6.1.4. Saudi Arabia
9.6.1.5. Qatar
9.6.1.6. United Arab Emirates
9.6.1.7. Rest of MEA
9.6.2. Middle East & Africa AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Service/Product Types, 2021-2034
9.6.3. Middle East & Africa AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Applications, 2021-2034
9.6.4. Middle East & Africa AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Technologies 2021-2034
9.6.5. Middle East & Africa AI in Beauty and Cosmetics Market Revenue (US$ Million) Estimates and Forecasts by Distribution Channels, 2021-2034
Chapter 10. Competitive Landscape
10.1. Major Mergers and Acquisitions/Strategic Alliances
10.2. Company Profiles
10.2.1. AmorePacific
10.2.1.1. Business Overview
10.2.1.2. Key Product/Service
10.2.1.3. Financial Performance
10.2.1.4. Modeance
10.2.1.5. Geographical Presence
10.2.1.6. Recent Developments with Business Strategy
10.2.2. Anua
10.2.3. Beautymix
10.2.4. Beiersdorf (NIVEA SKiN GUiDE)
10.2.5. Blank Beauty
10.2.6. Clarins
10.2.7. Cosmax
10.2.8. Coty Inc. (Rimmel)
10.2.9. DCYPHER
10.2.10. EpigenCare Inc.
10.2.11. Estée Lauder
10.2.12. FaceGym
10.2.13. FTI Foodtech
10.2.14. Function of Beauty LLC
10.2.15. Givaudan
10.2.16. Haut.AI
10.2.17. L’Oréal (ModiFace, Hair Coach)
10.2.18. Lilac St.
10.2.19. Luna Fofo (FOREO)
10.2.20. Lush
10.2.21. My Beauty Matches
10.2.22. New Kinpo Group
10.2.23. Nioxin
10.2.24. Olay (Skin Care App)
10.2.25. Orbis
10.2.26. Perfect Corp
10.2.27. Symrise (Philyra)
10.2.28. Procter & Gamble (Opte Wand)
10.2.29. Proven Skincare
10.2.30. Pure & Mine
10.2.31. Revieve
10.2.32. Sephora USA, Inc. (Virtual Artist)
10.2.33. Shiseido (Optune System)
10.2.34. Skin Analytics
10.2.35. Skin Match
10.2.36. SkinGenix
10.2.37. Spruce Beauty
10.2.38. The Lip Bar
10.2.39. Toun28
10.2.40. Unilever
10.2.41. YouCam Makeup
10.2.42. Yours Skincare
10.2.43. Youth Laboratories
10.2.44. Youthforia
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.