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人工智慧驅動的個人化零食推薦市場預測至2032年：全球分析（按個人化類型、零食類型、訂閱模式、最終用戶和地區分類）

AI-Personalized Snack Curation Market Forecasts to 2032 - Global Analysis By Personalization Type (Taste-Based, Nutrient-Based, Activity-Based, and Allergy-Based), Snack Type, Subscription Model, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計 2025 年全球人工智慧驅動的個人化零食推薦市場規模將達到 41 億美元，到 2032 年將達到 133 億美元，預測期內複合年成長率將達到 18%。

人工智慧驅動的個人化零食推薦系統利用人工智慧技術，根據使用者的偏好、飲食需求和健康目標客製化零食選擇。透過分析購買記錄和健康應用程式數據，演算法會推薦或提供量身定做的選項，從而提升便利性和滿意度。這種技術主導的方法在確保營養的同時，也能契合使用者的生活方式和不斷變化的偏好。它將物流與智慧物流相結合，透過提供反映用戶習慣、渴望和健康優先事項的即時零食解決方案，將日常零食體驗轉變為健康、精心策劃的享受。

根據 CB Insights 稱，這款由人工智慧主導的零食平台將利用行為數據和口味分析來打造個人化零食盒，從而在競爭激烈的健康零食生態系統中提升用戶發現度和留存率。

個人化營養的普及程度日益提高

個人化營養概念的日益普及正推動人工智慧驅動的個人化零食推薦領域蓬勃發展。消費者越來越需要根據自身飲食偏好、健康目標和食物習慣量身訂製的零食選擇。在人工智慧技術的進步推動下，各大平台正透過分析用戶數據來推薦最佳化後的零食組合。此外，注重健康的千禧世代和其他精通科技的消費者日益成長的影響力也進一步推動了這一趨勢，他們重視日常飲食中的便利性、個人化和功能性營養。

資料隱私和演算法問題

對資料隱私和演算法的擔憂仍然是阻礙消費者信任和接受度的重要因素。收集個人健康和消費數據引發了透明度、知情同意和數據濫用等問題。此外，人工智慧演算法中的偏見可能導致不準確的推薦，並降低用戶滿意度。因此，各公司正優先考慮安全的資料管理框架和透明的演算法運行，以確保在個人化營養生態系統中負責任地使用消費者數據，同時恪守道德標準。

用於客製化零食的預測分析

針對個人化零食的預測分析為市場參與企業提供了一條充滿前景的成長路徑。透過利用機器學習和即時行為數據，品牌可以精準預測消費者的喜好和營養需求。這種方法能夠實現精準的產品推薦、最佳化庫存並減少食物浪費。此外，將預測系統與穿戴式健康設備整合，可實現動態的膳食調整，使人工智慧主導的零食推薦成為不斷發展的數位健康和​​營養領域的基石。

對數位基礎設施準確性的依賴

對數位基礎設施精準性的過度依賴對市場持續營運構成重大威脅。技術故障、演算法錯誤和平台宕機都可能擾亂個人化建議，導致糟糕的使用者體驗。此外，對第三方資料供應商和網際網路絡的依賴也增加了系統漏洞。為了緩解這個問題，市場領導者正在投資於基於雲端基礎的冗餘、區塊鏈可追溯性和即時系統審核，以確保其人工智慧驅動的零食平台性能穩定，並贏得消費者信任。

新冠疫情的影響：

新冠疫情加速了消費者對人工智慧驅動的個人化零食平台的需求，因為消費者開始更多地居家管理營養。隨著健康意識的增強和實體店購物受限，數位零食推薦服務的註冊量激增。在消費者對增強免疫力和提供舒適感的零食的需求驅動下，各公司利用人工智慧技術改進建議引擎，以適應不斷變化的偏好。疫情過後，人們對便利性和預防性健康的持續關注將繼續推動數位營養生態系統的長期發展。

預計在預測期內，基於偏好的細分市場規模最大。

由於消費者對口味客主導和健康益處的需求日益成長，預計在預測期內，以口味為導向的細分市場將佔據最大的市場佔有率。人工智慧演算法分析口味特徵和感官偏好，從而打造兼具美味和營養的零食。此外，先進的口味預測模型和區域口味映射功能使品牌能夠創建高度本地化的零食組合，從而提高用戶滿意度和復購率。

預計在預測期內，蛋白質棒細分市場將實現最高的複合年成長率。

受健身趨勢上升和人們對便捷營養需求日益成長的推動，預計蛋白質棒市場在預測期內將實現最高成長率。透過人工智慧推薦系統的應用，各大品牌正根據個人的新陳代謝、運動模式和飲食目標來客製化蛋白質配方。此外，消費者對高蛋白、低糖零食的需求不斷成長，也使蛋白質棒成為個人化零食經濟的關鍵驅動力。

佔比最大的地區：

亞太地區預計將在預測期內佔據最大的市場佔有率，這主要得益於其龐大且技術普及率高的人口，以及對個人化健康解決方案日益成長的需求。中國、日本和印度等國家正加速採用人工智慧主導的食品推薦平台。行動健康應用的普及，以及零售業的快速數位化，進一步鞏固了該地區在客製化營養和智慧零食解決方案領域的領先地位。

複合年成長率最高的地區：

在預測期內，北美預計將呈現最高的複合年成長率，這主要得益於人工智慧膳食分析和消費者數據整合領域的強勁創新。在領先科技公司和專注於健康的新興企業的支持下，該地區在開發先進的個人化演算法方面處於領先地位。可支配收入的成長以及機能性食品日益普及，預計將推動美國和加拿大數位營養生態系統的市場擴張。

免費提供客製化服務：

訂閱本報告的用戶可享有以下免費客製化服務之一：

  • 公司簡介
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要企業進行SWOT分析（最多3家公司）
  • 區域細分
    • 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
  • 競爭基準化分析
    • 基於產品系列、地域覆蓋和策略聯盟對主要企業基準化分析

目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究資訊來源
    • 初級研究資訊來源
    • 次級研究資訊來源
    • 先決條件

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球人工智慧賦能的個人化零食推薦市場（按個人化類型分類）

  • 基於口味
  • 營養基礎
  • 基於活動的
  • 基於過敏

6. 全球人工智慧驅動的個人化零食推薦市場（按零食類型分類）

  • 蛋白質棒
  • 堅果
  • 晶片
  • 軟糖
  • 其他零食類型

7. 全球人工智慧賦能的個人化零食推薦市場（以訂閱模式分類）

  • 每月
  • 一經請求
  • 免費增值
  • 試用裝

8. 全球人工智慧賦能的個人化零食推薦市場（依最終用戶分類）

  • 城市專業人士
  • 學生
  • 健身愛好者
  • 其他最終用戶

9. 全球人工智慧賦能的個人化零食推薦市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第10章：重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與併購
  • 新產品上市
  • 業務拓展
  • 其他關鍵策略

第11章 企業概況

  • PepsiCo
  • Mondelez International
  • Nestle
  • Kellogg Company
  • General Mills
  • Conagra Brands
  • Campbell Soup Company
  • Hershey Company
  • Mars Incorporated
  • Danone
  • TreeHouse Foods
  • Hain Celestial Group
  • B&G Foods
  • Utz Brands
  • Post Consumer Brands
  • Hostess Brands
  • The Kraft Heinz Company
Product Code: SMRC31678

According to Stratistics MRC, the Global AI-Personalized Snack Curation Market is accounted for $4.1 billion in 2025 and is expected to reach $13.3 billion by 2032 growing at a CAGR of 18% during the forecast period. AI-Personalized Snack Curation leverages artificial intelligence to customize snack choices based on taste, dietary needs, and health goals. By analyzing data from purchase history or health apps, algorithms recommend or deliver tailored options that enhance convenience and satisfaction. This tech-driven approach ensures nutrition while aligning with individual lifestyles and evolving preferences. It merges personalization with smart logistics, offering real-time snack solutions that reflect user habits, cravings, and wellness priorities-transforming everyday snacking into a curated, health-conscious experience.

According to CB Insights, AI-driven snack platforms use behavioral data and taste profiling to curate personalized boxes, enhancing discovery and retention in the competitive healthy snacking ecosystem.

Market Dynamics:

Driver:

Rising adoption of personalized nutrition

Rising adoption of personalized nutrition is fueling strong growth across the AI-personalized snack curation landscape. Consumers are increasingly seeking customized snacking options tailored to dietary preferences, health goals, and taste patterns. Fueled by advancements in artificial intelligence, platforms now analyze user data to recommend optimized snack assortments. This trend is further strengthened by the growing influence of health-conscious millennials and tech-savvy consumers who value convenience, personalization, and functional nutrition in everyday food consumption.

Restraint:

Data privacy and algorithm concerns

Data privacy and algorithm concerns remain major restraints, affecting consumer trust and adoption rates. The collection of personal health and consumption data raises issues around transparency, consent, and data misuse. Additionally, biases in AI algorithms can lead to inaccurate recommendations, undermining user satisfaction. Consequently, companies are prioritizing secure data management frameworks and transparent algorithmic operations to maintain ethical standards while ensuring responsible use of consumer data within the personalized nutrition ecosystem.

Opportunity:

Predictive analytics for custom snacking

Predictive analytics for custom snacking offers a promising growth avenue for market participants. Leveraging machine learning and real-time behavioral data, brands can anticipate consumer cravings and nutritional needs with precision. This approach enables targeted product recommendations, inventory optimization, and reduced food waste. Moreover, integrating predictive systems with wearable health devices allows for dynamic dietary adjustments, positioning AI-driven snack curation as a cornerstone in the evolving digital health and nutrition landscape.

Threat:

Reliance on digital infrastructure accuracy

Reliance on digital infrastructure accuracy poses a significant threat to market continuity. Technical glitches, algorithmic errors, or platform downtimes can disrupt personalized recommendations, leading to poor user experiences. Furthermore, dependence on third-party data providers and connectivity networks increases system vulnerability. To mitigate this, market leaders are investing in cloud-based redundancies, blockchain traceability, and real-time system audits to ensure consistent performance and consumer trust in AI-powered snacking platforms.

Covid-19 Impact:

The COVID-19 pandemic accelerated demand for AI-personalized snack platforms as consumers shifted toward home-based nutrition management. With heightened health awareness and limited retail access, digital snack curation services experienced a surge in subscriptions. Spurred by the desire for immune-supportive and comfort-driven snacks, companies leveraged AI to refine recommendation engines and cater to evolving taste preferences. Post-pandemic, the sustained focus on convenience and preventive wellness continues to drive long-term adoption across digital nutrition ecosystems.

The taste-based segment is expected to be the largest during the forecast period

The taste-based segment is expected to account for the largest market share during the forecast period, owing to consumers' increasing demand for flavor-driven customization alongside health benefits. AI algorithms analyze flavor profiles and sensory preferences to curate snacks that balance indulgence and nutrition. Additionally, the segment benefits from advanced flavor prediction models and regional taste mapping, allowing brands to create hyper-localized snack assortments that enhance user satisfaction and repeat engagement.

The protein bars segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the protein bars segment is predicted to witness the highest growth rate, reinforced by rising fitness trends and the growing need for on-the-go nutrition. Fueled by the integration of AI-based recommendation systems, brands are customizing protein formulations based on individual metabolism, workout patterns, and dietary goals. Furthermore, expanding consumer focus on high-protein, low-sugar snacking options positions this segment as a vital growth driver in the personalized snack economy.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, ascribed to its large tech-adaptive population and growing interest in personalized wellness solutions. Countries such as China, Japan, and India are witnessing increased adoption of AI-driven food recommendation platforms. The expansion of mobile health applications, coupled with rapid digitalization in retail, further strengthens the region's dominance in customized nutrition and smart snacking solutions.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR associated with strong innovation in AI-based dietary analytics and consumer data integration. Supported by leading technology firms and health-focused startups, the region is at the forefront of developing advanced personalization algorithms. Rising disposable incomes, coupled with the widespread acceptance of functional foods, are expected to accelerate market expansion across the U.S. and Canada's digital nutrition ecosystem.

Key players in the market

Some of the key players in AI-Personalized Snack Curation Market include PepsiCo, Mondelez International, Nestle, Kellogg Company, General Mills, Conagra Brands, Campbell Soup Company, Hershey Company, Mars Incorporated, Danone, TreeHouse Foods, Hain Celestial Group, B&G Foods, Utz Brands, Post Consumer Brands, Hostess Brands, and The Kraft Heinz Company.

Key Developments:

In September 2025, Nestle introduced the "NESTOLE Personalized Nutrition Hub," a smart countertop device for the home. Using AI and a user's health profile, it dispenses custom-portioned snacks from Nespresso-like capsules containing curated mixes of nuts, grains, and dark chocolate from brands like Gerber and Purina Pro Plan's new human-grade health line.

In August 2025, Mondelez International announced a major expansion of its "DunkSights AI" in-store partnership with convenience chains. The system analyzes time-of-day and local traffic data to optimize shelf layouts and suggest personalized Oreo, Chips Ahoy!, and Ritz Cracker pairings at the point of sale, increasing impulse buys by over 20% in pilot stores.

In July 2025, Kellogg Company (now Kellanova) unveiled its new "Bear Naked Custom Blend" service. Powered by an AI algorithm, it allows consumers to create their own perfectly balanced granola, trail mix, or cereal blend based on their specific fitness goals and taste preferences, with subscriptions offering monthly curated variations to prevent "palette fatigue."Personalization

Personalization Types Covered:

  • Taste-Based
  • Nutrient-Based
  • Activity-Based
  • Allergy-Based

Snack Types Covered:

  • Protein Bars
  • Nuts
  • Chips
  • Gummies
  • Other Snack Types

Subscription Models Covered:

  • Monthly
  • On-Demand
  • Freemium
  • Trial Pack

End Users Covered:

  • Urban Professionals
  • Students
  • Fitness Enthusiasts
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI-Personalized Snack Curation Market, By Personalization Type

  • 5.1 Introduction
  • 5.2 Taste-Based
  • 5.3 Nutrient-Based
  • 5.4 Activity-Based
  • 5.5 Allergy-Based

6 Global AI-Personalized Snack Curation Market, By Snack Type

  • 6.1 Introduction
  • 6.2 Protein Bars
  • 6.3 Nuts
  • 6.4 Chips
  • 6.5 Gummies
  • 6.6 Other Snack Types

7 Global AI-Personalized Snack Curation Market, By Subscription Model

  • 7.1 Introduction
  • 7.2 Monthly
  • 7.3 On-Demand
  • 7.4 Freemium
  • 7.5 Trial Pack

8 Global AI-Personalized Snack Curation Market, By End User

  • 8.1 Introduction
  • 8.2 Urban Professionals
  • 8.3 Students
  • 8.4 Fitness Enthusiasts
  • 8.5 Other End Users

9 Global AI-Personalized Snack Curation Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 PepsiCo
  • 11.2 Mondelez International
  • 11.3 Nestle
  • 11.4 Kellogg Company
  • 11.5 General Mills
  • 11.6 Conagra Brands
  • 11.7 Campbell Soup Company
  • 11.8 Hershey Company
  • 11.9 Mars Incorporated
  • 11.10 Danone
  • 11.11 TreeHouse Foods
  • 11.12 Hain Celestial Group
  • 11.13 B&G Foods
  • 11.14 Utz Brands
  • 11.15 Post Consumer Brands
  • 11.16 Hostess Brands
  • 11.17 The Kraft Heinz Company

List of Tables

  • Table 1 Global AI-Personalized Snack Curation Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI-Personalized Snack Curation Market Outlook, By Personalization Type (2024-2032) ($MN)
  • Table 3 Global AI-Personalized Snack Curation Market Outlook, By Taste-Based (2024-2032) ($MN)
  • Table 4 Global AI-Personalized Snack Curation Market Outlook, By Nutrient-Based (2024-2032) ($MN)
  • Table 5 Global AI-Personalized Snack Curation Market Outlook, By Activity-Based (2024-2032) ($MN)
  • Table 6 Global AI-Personalized Snack Curation Market Outlook, By Allergy-Based (2024-2032) ($MN)
  • Table 7 Global AI-Personalized Snack Curation Market Outlook, By Snack Type (2024-2032) ($MN)
  • Table 8 Global AI-Personalized Snack Curation Market Outlook, By Protein Bars (2024-2032) ($MN)
  • Table 9 Global AI-Personalized Snack Curation Market Outlook, By Nuts (2024-2032) ($MN)
  • Table 10 Global AI-Personalized Snack Curation Market Outlook, By Chips (2024-2032) ($MN)
  • Table 11 Global AI-Personalized Snack Curation Market Outlook, By Gummies (2024-2032) ($MN)
  • Table 12 Global AI-Personalized Snack Curation Market Outlook, By Other Snack Types (2024-2032) ($MN)
  • Table 13 Global AI-Personalized Snack Curation Market Outlook, By Subscription Model (2024-2032) ($MN)
  • Table 14 Global AI-Personalized Snack Curation Market Outlook, By Monthly (2024-2032) ($MN)
  • Table 15 Global AI-Personalized Snack Curation Market Outlook, By On-Demand (2024-2032) ($MN)
  • Table 16 Global AI-Personalized Snack Curation Market Outlook, By Freemium (2024-2032) ($MN)
  • Table 17 Global AI-Personalized Snack Curation Market Outlook, By Trial Pack (2024-2032) ($MN)
  • Table 18 Global AI-Personalized Snack Curation Market Outlook, By End User (2024-2032) ($MN)
  • Table 19 Global AI-Personalized Snack Curation Market Outlook, By Urban Professionals (2024-2032) ($MN)
  • Table 20 Global AI-Personalized Snack Curation Market Outlook, By Students (2024-2032) ($MN)
  • Table 21 Global AI-Personalized Snack Curation Market Outlook, By Fitness Enthusiasts (2024-2032) ($MN)
  • Table 22 Global AI-Personalized Snack Curation Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.