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市场调查报告书
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1856806

人工智慧驱动的个人化零食推荐市场预测至2032年：全球分析（按个人化类型、零食类型、订阅模式、最终用户和地区划分）

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个工作天内

价格

根据 Stratistics MRC 的数据，预计 2025 年全球人工智慧驱动的个人化零食推荐市场规模将达到 41 亿美元，到 2032 年将达到 133 亿美元，预测期内复合年增长率将达到 18%。

人工智慧驱动的个人化零食推荐系统利用人工智慧技术，根据使用者的偏好、饮食需求和健康目标客製化零食选择。透过分析购买记录和健康应用程式数据，演算法会推荐或提供量身定制的选项，从而提升便利性和满意度。这种技术主导的方法在确保营养的同时，也能契合使用者的生活方式和不断变化的偏好。它将物流与智慧物流相结合，透过提供反映用户习惯、渴望和健康优先事项的即时零食解决方案，将日常零食体验转变为健康、精心策划的享受。

根据 CB Insights 称，这款由人工智慧主导的零食平台将利用行为数据和口味分析来打造个人化零食盒，从而在竞争激烈的健康零食生态系统中提升用户发现度和留存率。

个人化营养的普及程度日益提高

个人化营养概念的日益普及正推动人工智慧驱动的个人化零食推荐领域蓬勃发展。消费者越来越需要根据自身饮食偏好、健康目标和食物习惯量身订製的零食选择。在人工智慧技术的进步推动下，各大平台正透过分析用户数据来推荐优化后的零食组合。此外，注重健康的千禧世代和其他精通科技的消费者日益增长的影响力也进一步推动了这一趋势，他们重视日常饮食中的便利性、个人化和功能性营养。

资料隐私和演算法问题

对资料隐私和演算法的担忧仍然是阻碍消费者信任和接受度的重要因素。收集个人健康和消费数据引发了透明度、知情同意和数据滥用等问题。此外，人工智慧演算法中的偏见可能导致不准确的推荐，并降低用户满意度。因此，各公司正优先考虑安全的资料管理框架和透明的演算法运行，以确保在个人化营养生态系统中负责任地使用消费者数据，同时恪守道德标准。

用于客製化零食的预测分析

针对个人化零食的预测分析为市场参与企业提供了一条充满前景的成长路径。透过利用机器学习和即时行为数据，品牌可以精准预测消费者的喜好和营养需求。这种方法能够实现精准的产品推荐、优化库存并减少食物浪费。此外，将预测系统与穿戴式健康设备整合，可实现动态的膳食调整，使人工智慧主导的零食推荐成为不断发展的数位健康和​​营养领域的基石。

对数位基础设施准确性的依赖

对数位基础设施精准性的过度依赖对市场持续营运构成重大威胁。技术故障、演算法错误和平台宕机都可能扰乱个人化建议，导致糟糕的使用者体验。此外，对第三方资料供应商和互联网络的依赖也增加了系统漏洞。为了缓解这个问题，市场领导者正在投资于基于云端基础的冗余、区块链可追溯性和即时系统审核，以确保其人工智慧驱动的零食平台性能稳定，并赢得消费者信任。

新冠疫情的影响：

新冠疫情加速了消费者对人工智慧驱动的个人化零食平台的需求，因为消费者开始更多地居家管理营养。随着健康意识的增强和实体店购物受限，数位零食推荐服务的註册量激增。在消费者对增强免疫力和提供舒适感的零食的需求驱动下，各公司利用人工智慧技术改进建议引擎，以适应不断变化的偏好。疫情过后，人们对便利性和预防性健康的持续关注将继续推动数位营养生态系统的长期发展。

预计在预测期内，基于偏好的细分市场规模最大。

由于消费者对口味客主导和健康益处的需求日益增长，预计在预测期内，以口味为导向的细分市场将占据最大的市场份额。人工智慧演算法分析口味特征和感官偏好，从而打造兼具美味和营养的零食。此外，先进的口味预测模型和区域口味映射功能使品牌能够创建高度本地化的零食组合，从而提高用户满意度和復购率。

预计在预测期内，蛋白质棒细分市场将实现最高的复合年增长率。

受健身趋势上升和人们对便捷营养需求日益增长的推动，预计蛋白质棒市场在预测期内将实现最高增长率。透过人工智慧推荐系统的应用，各大品牌正根据个人的新陈代谢、运动模式和饮食目标来客製化蛋白质配方。此外，消费者对高蛋白、低糖零食的需求不断增长，也使蛋白质棒成为个人化零食经济的关键驱动力。

占比最大的地区：

亚太地区预计将在预测期内占据最大的市场份额，这主要得益于其庞大且技术普及率高的人口，以及对个人化健康解决方案日益增长的需求。中国、日本和印度等国家正加速采用人工智慧主导的食品推荐平台。行动健康应用的普及，以及零售业的快速数位化，进一步巩固了该地区在客製化营养和智慧零食解决方案领域的领先地位。

复合年增长率最高的地区：

在预测期内，北美预计将呈现最高的复合年增长率，这主要得益于人工智慧膳食分析和消费者数据整合领域的强劲创新。在领先科技公司和专注于健康的新兴企业的支持下，该地区在开发先进的个人化演算法方面处于领先地位。可支配收入的成长以及机能性食品日益普及，预计将推动美国和加拿大数位营养生态系统的市场扩张。

免费提供客製化服务：

订阅本报告的用户可享有以下免费客製化服务之一：

  • 公司简介
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究资讯来源
    • 初级研究资讯来源
    • 次级研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球人工智慧赋能的个人化零食推荐市场（按个人化类型划分）

  • 基于口味
  • 营养基础
  • 基于活动的
  • 基于过敏

6. 全球人工智慧驱动的个人化零食推荐市场（按零食类型划分）

  • 蛋白质棒
  • 坚果
  • 晶片
  • 软糖
  • 其他零食类型

7. 全球人工智慧赋能的个人化零食推荐市场（以订阅模式划分）

  • 每月
  • 一经请求
  • 免费增值
  • 试用装

8. 全球人工智慧赋能的个人化零食推荐市场（依最终用户划分）

  • 城市专业人士
  • 学生
  • 健身爱好者
  • 其他最终用户

9. 全球人工智慧赋能的个人化零食推荐市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十章：重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与併购
  • 新产品上市
  • 业务拓展
  • 其他关键策略

第十一章 企业概况

  • 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.