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市场调查报告书
商品编码
1445475

推荐引擎：市场占有率分析、产业趋势与统计、成长预测（2024-2029）

Recommendation Engine - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
出版日期: | 出版商: Mordor Intelligence | 英文 168 Pages | 商品交期: 2-3个工作天内

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简介目录

推荐引擎市场规模预计到 2024 年为 68.8 亿美元，预计到 2029 年将达到 287 亿美元，在预测期内（2024-2029 年）复合年增长率为 33.06%。

推荐引擎-市场

随着企业数量的增加和企业之间竞争的加剧，许多公司正在寻求将人工智慧 (AI) 等技术与其应用程式、业务、分析和服务整合。世界各地的大多数组织都在追求数位转型，重点是透过自动化解决方案改善客户和员工体验。

主要亮点

  • 由于新兴国家数位化的进步和电子商务市场的成长，对推荐引擎的需求不断增加。跨基于人工智慧的云端平台整合机器学习模型可推动多个最终用户产业的自动化。
  • 传统上，消费者在商店货架上做出购买决定，这为实体零售商提供了强大的平台来了解和影响消费者的行为和偏好。然而，随着网路普及的提高以及电子商务、行动购物和智慧技术等新销售管道的出现，零售业正在适应新技术和先进技术。这些技术，例如智慧 POS 解决方案和自助结帐系统亭，将传统实体店转变为全通路商店。据 ZDNet 称，70% 的公司已经或正在製定数位转型策略。
  • 数位转型为零售商提供了获取新客户、提高与现有客户的互动、降低营运成本和提高员工积极性的机会。除此之外，这些好处对收益和利润有正面影响。这种正面影响为预测期内采用推荐引擎创造了巨大的机会。
  • 由于用户偏好变化而导致标籤不准确的挑战是推荐引擎市场持续关注的问题。然而，开发人员正在不断努力提高其建议的准确性和相关性。随着科技的进步，我们希望未来能找到更有效的解决方案来应对这项挑战。
  • 根据思科旗下 AppDynamics 最近发布的转型代理报告，在 COVID-19 大流行期间，95% 的组织中的技术优先事项发生了变化，88% 的组织表示数位客户经验在其组织中发生了变化。我将其报告为优先事项。客户转向自助服务工具，例如聊天、通讯和对话机器人。因此，企业现在可以使用这些工具提供卓越的客户体验，同时减少对实体店和实况活动的传统依赖，这在社交距离时代是不可能的。随着这些公司采用该技术，预计这将进一步增加推荐引擎所提供的好处。

推荐引擎市场趋势

跨行动和网路的数位商务体验客製化需求的不断增长推动了市场成长

  • 企业正在寻找方法和技术，透过提供高度个人化的客户体验来利用竞争对手难以模仿的优势。这些体验使用专有资料为数百万个人客户提供更好的体验。结果取决于执行。如果执行得当，个人化的客户体验可以帮助企业脱颖而出，获得客户忠诚度和永续的竞争优势，这在当前情况下是非常需要的。
  • 客户不再在实体店做出决定，而是使用网页浏览器或行动电话在数位货架前在线上做出决定。对于从事零售业经营的公司来说，其产品的价格、地点和促销不再仅仅与附近货架上的产品进行比较，而是与拥有世界各地网站的零售商的替代产品进行比较。在这方面，使用AI和ML的推荐引擎等技术可确保满足客户的要求，确保客户的需求和产品处于同一水平，并确保您可以领先竞争对手一步。
  • 多年来，由于客户需求不断增加，许多组织的行销专业人员都致力于改善客户体验。例如，根据 Adob​​e 的说法，拥有最强大的全通路客户参与策略的公司可以实现 10%与前一年同期比较增长，平均订单价值增加 10%，获胜率增加 25%。这是有性别的。此外，拥有强大全通路客户参与策略和消费者服务改善计画的品牌平均保留了 89% 的客户，而全通路客户参与策略较弱的品牌则客户维繫。
  • 随着使用管道数量的增加，技术使品牌能够确保在所有管道上讯息有关其产品的一致讯息。对更好的客户服务的需求不断增长预计将在预测期内推动需求并对市场产生积极影响。
  • 总体而言，对个人化数位商务体验不断增长的需求正在推动推荐引擎市场。据泰雷兹集团称，银行和金融部门在消费者资讯安全方面被认为是值得信赖的。全球超过 40% 的消费者表示，他们信任数位银行业务和金融服务部门的资料。医疗保健提供者是数位服务中第二值得信赖的行业，37% 的受访者表示该行业是最安全的。公司正在利用人工智慧技术为客户提供有针对性的建议、推动销售并提高客户满意度。

亚太地区将经历最快的成长

  • 以澳洲、印度、中国和韩国等国家主导的亚太地区预计将成为推荐引擎市场成长最快的地区。
  • 中国是亚太地区主要国家之一，也是技术采用者。该国拥有最快的互联网乐队之一和阿里巴巴等强大的电子商务公司。
  • 此外，中国是仅次于美国的全球第二大OTT市场。根据墨西哥联邦电讯统计，中国每100户家庭就有68户订阅，网路视讯用户比例大幅上升。然而，该国对所使用的行业和资料以及允许在国内传播的内容有非常严格的规定。
  • 中国严格的法规环境进一步确保了三方（爱奇艺、腾讯、优酷）的主导地位，这阻止了 FAANG（Facebook、亚马逊、苹果、Netflix、Google）等国际玩家在中国运作。这些国际参与者大规模使用推荐引擎，并透过广告推广其他业务。这为该地区的国内企业留下了充足的机会，导致其成长速度低于美国。
  • 此外，电子商务巨头阿里巴巴利用人工智慧和机器学习来推动推荐。例如，AI OS是阿里巴巴搜寻工程团队开发的集个人化搜寻、推荐和广告于一体的线上平台。 AI OS引擎系统提供淘宝全网行动搜寻页面、淘宝各大促销活动行动资讯流地、淘宝首页商品推荐、个人化推荐、按品类、业界选购等多种功能，支援业务场景。

推荐引擎行业概况

推荐引擎市场呈现分散化，主要参与者包括 IBM 公司、Google LLC (Alphabet Inc.)、Amazon Web Services Inc. (Amazon.com Inc.)、Microsoft Corporation 和 Salesforce Inc.。市场参与者正在采取联盟、併购等策略来增强其产品供应并获得永续的竞争优势。

  • 2023 年 1 月 - Coveo 宣布新的 Coveo 商品行销 Hub 首次亮相。该中心提供了一系列丰富的功能，使企业能够提供相关的购物旅程，有助于提高忠诚度并提高盈利。它旨在帮助商家创造可转换的客製化体验。 Qubit 是一家总部位于伦敦的Start-Ups，为时尚公司和零售商提供人工智慧驱动的客製化技术，于 2021 年 10 月被 Coveo 收购。
  • 2022 年 10 月 - Algonomy 宣布推出适用于 Shopify 和 Commercetools 的两个重要连接器。这使得 Algonomy 产品和电子商店之间能够自动、顺畅地进行资料交换。 Algonomy 连接器提供了一种将网路商店与 Shopify 或 Commercetools 整合的简单方法，并支援即时产品资料收集。连接器可让您更好地控制和深入了解目录整合流程，从而无需依赖外部组织或资源来定期更新目录资料。

其他福利

  • Excel 格式的市场预测 (ME) 表
  • 3 个月分析师支持

目录

第一章简介

  • 研究假设和市场定义
  • 调查范围

第二章调查方法

第三章执行摘要

第四章市场洞察

  • 市场概况
  • 产业吸引力－波特五力分析
    • 供应商的议价能力
    • 买方议价能力
    • 新进入者的威胁
    • 竞争公司之间的敌意强度
    • 替代产品的威胁
  • 评估 COVID-19 对市场的影响
  • 技术简介
    • 地理空间意识
    • 情境辨识（机器学习与深度学习、自然语言处理）
  • 新用例（跨多个最终用户利用推荐引擎的关键用例）

第五章市场动态

  • 市场驱动因素
    • 对跨行动和网路的客製化数位商务体验的需求不断增长
    • 零售商更多采用产品管理和库存规则
  • 市场限制因素
    • 由于使用者设定的变更而导致错误标籤的复杂性

第六章市场区隔

  • 依部署方式
    • 本地
    • 云
  • 按类型
    • 协同过滤
    • 基于内容的过滤
    • 混合推荐系统
    • 其他类型
  • 按最终用户产业
    • 资讯科技/通讯
    • BFSI
    • 零售
    • 媒体与娱乐
    • 卫生保健
    • 其他最终用户产业
  • 按地区
    • 北美洲
    • 欧洲
    • 亚太地区
    • 拉丁美洲
    • 中东和非洲

第七章 竞争形势

  • 公司简介
    • IBM Corporation
    • Google LLC（Alphabet Inc.）
    • Amazon Web Services Inc.（Amazon.com, Inc.）
    • Microsoft Corporation
    • Salesforce Inc.
    • Unbxd Inc.
    • Oracle Corporation
    • Intel Corporation
    • SAP SE
    • Hewlett Packard Enterprise Development LP
    • Qubit Digital Ltd（COVEO）
    • Algonomy Software Pvt. Ltd
    • Recolize GmbH
    • Adobe Inc.
    • Dynamic Yield Inc.
    • Kibo Commerce
    • Netflix Inc.

第八章投资分析

第9章市场的未来

简介目录
Product Code: 67378

The Recommendation Engine Market size is estimated at USD 6.88 billion in 2024, and is expected to reach USD 28.70 billion by 2029, growing at a CAGR of 33.06% during the forecast period (2024-2029).

Recommendation Engine - Market

With the growing number of enterprises and the rising competition among them, many companies are trying to integrate technologies, like artificial intelligence (AI), with their applications, businesses, analytics, and services. Most organizations globally are pursuing digital transformation, focusing on improving the experience of customers and employees, which is being leveraged by automation solutions.

Key Highlights

  • The advancement of digitalization across emerging economies, coupled with the growth of the e-commerce market, has driven the demand for recommendation engines. Integrating the machine learning model across AI-based cloud platforms drives automation across multiple end-user industries.
  • Consumers traditionally make purchase decisions at the store shelf, providing institutional brick-and-mortar retailers a high-power level to learn about and influence consumers' behavior and preferences. However, with the rise of internet penetration and the emergence of new sales channels through e-commerce, mobile shopping, and smart technologies, the retail industry is adapting to new and advanced technologies. These technologies, such as smart point-of-sale solutions and self-checkout kiosks, transform traditional brick-and-mortar stores into omnichannel ones. According to ZDNet, 70% of the companies either have a digital transformation strategy or are working with one.
  • Digital transformation provides opportunities for retailers to acquire new customers, engage with existing customers better, reduce the cost of operations, and improve employee motivation. These benefits, among others, positively impact the revenue and margins. This positive impact will create significant opportunities for adopting recommendation engines over the forecast period.
  • The challenge of incorrect labeling due to changing user preferences is an ongoing concern for the recommendation engine market. However, developers are continually working to improve the accuracy and relevance of recommendations. As technology advances, we can expect to see more effective solutions to this challenge in the future.
  • According to the recent "Agents of Transformation Report" from AppDynamics, part of Cisco, technology priorities during the COVID-19 pandemic changed within 95% of organizations, and 88% reported that digital customer experience was the priority for their organization. Customers turned to self-service tools in the form of chats, messaging, and conversational bots. As a result, companies enabled these tools to deliver a great customer experience while reducing traditional dependencies on brick-and-mortar and live events, which were not feasible in a time of social distancing. This was further expected to increase the benefits achieved by recommendation engines due to the increased adoption of technologies in these companies.

Recommendation Engine Market Trends

Increasing Demand for Customization of Digital Commerce Experience Across Mobile and Web Drives the Market's Growth

  • Enterprises are looking for ways and technologies to leverage the advantage that could be difficult for their competitors to imitate by providing highly personalized customer experiences. Such experiences use proprietary data to offer a better experience to millions of individual customers. The results depend on the execution. When executed well, personalized customer experience can enable businesses to differentiate themselves and gain customer loyalty and sustainable competitive advantage, which is much needed in the present scenario.
  • Customers' decisions are no longer being made in a physical store but online on web browsers and mobile phones in front of the digital shelf. For the enterprises operating in the retail space, the price, place, and promotion of their products are no longer just being compared to products on neighboring shelves but to alternative products from retailers with websites worldwide. In this regard, technologies such as recommendation engines, using AI and ML, ensure customers' requirements are met and ensure that customers' needs and offerings are on the same level, enough to be one step ahead of their competitors.
  • Over the years, many marketing professionals across organizations have increased their focus on enhancing customer experience due to the customers' growing demand. For instance, according to Adobe, companies with the most robust omnichannel customer engagement strategies could witness a 10% Y-o-Y growth, a 10% increase in average order value, and a 25% increase in close rates. Also, brands that adopted robust omnichannel customer engagement strategies and consumer service enhancement programs retain, on average, 89% of their customers, compared to 33% for brands with weak omnichannel customer engagement strategies.
  • With a growing number of channels coming into play, technologies ensure that the brands provide a consistent message about their offerings across all channels. The growing demand for better customer service is expected to drive the demand and positively affect the market during the forecast period.
  • Overall, the growing demand for personalized digital commerce experiences drives the recommendation engine market. According to Thales Group, the banking and financial sector was considered trustworthy for the security of consumers' information. Over 40% of consumers globally stated they trusted the digital banking and financial services sector with their data. Healthcare providers were the second-most trusted industry in the digital services sector, with 37% of the respondents indicating this sector as among the most secure. Businesses seek to leverage AI technology to deliver targeted customer recommendations, drive sales, and improve customer satisfaction.

Asia-Pacific to Witness the Fastest Growth

  • Led by countries like Australia, India, China, and South Korea, the Asia-Pacific region is expected to witness the fastest growth in the recommendation engine market.
  • China is one of the major countries in Asia-Pacific with growing technological adoption. The country is home to one of the fastest internet bands and strong e-commerce players, like Alibaba.
  • Moreover, China is the second-largest OTT market in the world after the United States. According to Instituto Federal de Telecommunications (Mexico), there were 68 subscriptions per 100 homes in China, and the rate of online video users is increasing effectively. However, the country is very strict in terms of regulations surrounding the industry and the data used, as well as the content that is allowed to be circulated in the country.
  • The tripartite (iQiyi, Tencent, Youku) domination is further secured by the strict regulatory environment in China, which prevents international players, such as the FAANG (Facebook, Amazon, Apple, Netflix, and Google), from operating in the country. These international players use the recommendations engine at a large scale and drive other businesses through advertising. This leaves the region ample opportunities for domestic players, thus leading to moderate growth compared to the United States.
  • Furthermore, one e-commerce giant, Alibaba, uses AI and machine learning to drive its recommendations. For instance, AI OS is an online platform developed by the Alibaba search engineering team that integrates personalized search, recommendation, and advertising. The AI OS engine system supports various business scenarios, including all Taobao Mobile search pages, Taobao Mobile information flow venues for major promotion activities, product recommendations on the Taobao homepage, personalized recommendations, and product selection by category and industry.

Recommendation Engine Industry Overview

The recommendation engine market is fragmented with the presence of major players like IBM Corporation, Google LLC (Alphabet Inc.), Amazon Web Services Inc.(Amazon.com Inc.), Microsoft Corporation, and Salesforce Inc. Players in the market are adopting strategies such as partnerships, mergers, and acquisitions to enhance their product offerings and gain sustainable competitive advantage.

  • January 2023 - New Coveo Merchandising Hub's debut was announced by Coveo. The Hub offers a rich feature set that enables companies to deliver a highly relevant shopping journey that helps foster loyalty and boost profitability. It is designed to empower merchandisers to create tailored experiences that convert. Qubit, a London-based start-up that offers AI-powered customization technology for fashion companies and retailers, was acquired by Coveo in October 2021.
  • October 2022 - Algonomy announced the availability of two significant connectors for Shopify and Commercetools, which will enable automatic and smooth data interchange between Algonomy's products and e-stores. Algonomy Connectors offer a simple method for integrating online shops with Shopify or Commercetools, enabling real-time product data collecting. Connectors give improved control and insight over the catalog integration process and remove the need for relying on external organizations and resources to update catalog data regularly.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.2.1 Bargaining Power of Suppliers
    • 4.2.2 Bargaining Power of Buyers/Consumers
    • 4.2.3 Threat of New Entrants
    • 4.2.4 Intensity of Competitive Rivalry
    • 4.2.5 Threat of Substitute Products
  • 4.3 Assessment of the Impact of COVID-19 on the Market
  • 4.4 Technology Snapshot
    • 4.4.1 Geospatial Aware
    • 4.4.2 Context Aware (Machine Learning and Deep Learning, Natural Language Processing)
  • 4.5 Emerging Use-cases (Key Use-cases Pertaining to the Utilization of Recommendation Engine Across Multiple End Users)

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Demand for the Customization of Digital Commerce Experience Across Mobile and Web
    • 5.1.2 Growing Adoption by Retailers for Controlling Merchandising and Inventory Rules
  • 5.2 Market Restraints
    • 5.2.1 Complexity Regarding Incorrect Labeling Due to Changing User Preferences

6 MARKET SEGMENTATION

  • 6.1 By Deployment Mode
    • 6.1.1 On-premise
    • 6.1.2 Cloud
  • 6.2 By Types
    • 6.2.1 Collaborative Filtering
    • 6.2.2 Content-based Filtering
    • 6.2.3 Hybrid Recommendation Systems
    • 6.2.4 Other Types
  • 6.3 By End-user Industry
    • 6.3.1 IT and Telecommunication
    • 6.3.2 BFSI
    • 6.3.3 Retail
    • 6.3.4 Media and Entertainment
    • 6.3.5 Healthcare
    • 6.3.6 Other End-user Industries
  • 6.4 By Geography
    • 6.4.1 North America
    • 6.4.2 Europe
    • 6.4.3 Asia-Pacific
    • 6.4.4 Latin America
    • 6.4.5 Middle East and Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 IBM Corporation
    • 7.1.2 Google LLC (Alphabet Inc.)
    • 7.1.3 Amazon Web Services Inc. (Amazon.com, Inc.)
    • 7.1.4 Microsoft Corporation
    • 7.1.5 Salesforce Inc.
    • 7.1.6 Unbxd Inc.
    • 7.1.7 Oracle Corporation
    • 7.1.8 Intel Corporation
    • 7.1.9 SAP SE
    • 7.1.10 Hewlett Packard Enterprise Development LP
    • 7.1.11 Qubit Digital Ltd (COVEO)
    • 7.1.12 Algonomy Software Pvt. Ltd
    • 7.1.13 Recolize GmbH
    • 7.1.14 Adobe Inc.
    • 7.1.15 Dynamic Yield Inc.
    • 7.1.16 Kibo Commerce
    • 7.1.17 Netflix Inc.

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET