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1689697

建議引擎：市場佔有率分析、產業趨勢與統計、成長預測（2025-2030）

Recommendation Engine - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 168 Pages | 商品交期: 2-3個工作天內

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簡介目錄

建議引擎市場規模預計在 2025 年為 91.5 億美元，預計到 2030 年將達到 381.8 億美元，預測期內（2025-2030 年）的複合年成長率為 33.06%。

推薦引擎-市場-IMG1

隨著企業數量的增加和企業之間競爭的加劇，許多企業都希望將人工智慧 (AI) 等技術融入他們的應用程式、業務、分析和服務中。全球大多數組織都在進行數位轉型，重點是改善客戶和員工體驗，而自動化解決方案可以實現這一點。

關鍵亮點

  • 新興國家日益數位化和電子商務市場的成長正在推動對建議引擎的需求。基於人工智慧的雲端平台上機器學習模型的整合正在推動多個終端用戶產業的自動化。
  • 消費者通常在商店做出購買決定，這使得實體零售商更有能力了解和影響消費者的行為和偏好。然而，隨著網路的普及率不斷提高，以及電子商務、行動購物和智慧技術帶來的新分銷管道的出現，零售業正在適應新興技術。這些技術，例如智慧 POS 解決方案和自助結帳系統亭，正在將傳統的實體店轉變為全通路商店。根據ZDNet報道，70%的公司已經或正在製定數位轉型策略。
  • 數位轉型為零售商提供了吸引新客戶、提高與現有客戶的互動、降低營運成本和提高員工積極性的機會。這些好處對收益和利潤率等產生了積極影響。預計這一正面影響將為預測期內建議引擎的採用創造重大機會。
  • 由於用戶偏好的變化而導致的錯誤標籤問題是建議引擎市場持續關注的問題。然而，開發人員正在不斷努力提高建議的準確性和相關性。隨著技術的進步，預計未來將出現更多有效的解決方案來應對這項挑戰。
  • 根據思科旗下 AppDynamics 最近發布的《轉型推動者報告》，95% 的組織在 COVID-19 疫情期間改變了其技術重點，其中 88% 的組織報告稱數位客戶體驗現在已成為其組織的優先事項。客戶開始使用聊天、通訊和對話機器人等自助服務工具。因此，這些工具使企業能夠提供卓越的客戶經驗，同時減少對社交距離店和實況活動的傳統依賴，而這在社交疏離時代根本不可行。因此，隨著這些公司擴大採用該技術，建議引擎所帶來的收益預計將進一步增加。

建議引擎市場趨勢

行動和網路對客製化數位商務體驗的需求不斷成長，推動了市場成長

  • 公司正在探索能夠透過提供高度個人化的客戶體驗為它們帶來競爭對手難以複製的優勢的方法和技術。這些體驗使用專有資料為數百萬個人客戶創造更好的體驗。成功取決於執行。如果做得好，個人化的客戶體驗可以讓公司脫穎而出，贏得客戶忠誠度和永續的競爭優勢。
  • 顧客不再在實體店做出決定，而是在網路上，在數位貨架前，使用網頁瀏覽器或行動電話做出決定。對於零售企業來說，產品的價格、位置和促銷不再只是與附近貨架上的產品進行比較，而是與來自世界各地的網路零售商的替代產品進行比較。在這方面，由人工智慧和機器學習驅動的建議引擎等技術足以確保滿足客戶需求，確保他們的需求和您的產品保持一致，並使您領先於競爭對手一步。
  • 在過去幾年中，由於客戶需求不斷增加，許多組織的負責人越來越注重提升客戶經驗。例如，根據 Adob​​e 的說法，擁有最強大的全通路客戶參與策略的公司可以實現 10% 的與前一年同期比較成長、10% 的平均訂單價值的成長以及 25% 的成交率的成長。此外，擁有強大的全通路客戶參與策略和消費者服務改善計畫的品牌平均可以保留 89% 的客戶，而全通路客戶參與策略較弱的品牌則只有 33%。
  • 隨著管道數量的增加，科技使品牌能夠在所有管道上傳遞一致的訊息。預計對更好的客戶服務的需求不斷成長將推動需求並在預測期內對市場產生積極影響。
  • 總體而言，對個人化數位商務體驗日益成長的需求正在推動建議引擎市場的發展。據泰雷茲集團稱，在消費者資訊安全方面，銀行和金融業被認為是值得信賴的。超過 40% 的全球消費者表示他們會將資料委託給數位銀行和金融服務部門。醫療保健提供者是數位服務領域中第二大最受信任的行業，37% 的受訪者表示該行業是最安全的行業之一。企業正在利用人工智慧技術提供有針對性的客戶建議、推動銷售並提高客戶滿意度。

亞太地區發展迅速

  • 預計亞太地區將見證建議引擎市場最快的成長，其中以澳洲、印度、中國和韓國等國家為主導。
  • 中國是亞太地區領先的技術採用者之一。該國擁有最快的網際網路頻寬，並且是阿里巴巴等強大電子商務參與企業的所在地。
  • 而且中國是繼美國之後全球第二大OTT市場。根據墨西哥聯邦電信實驗室（Instituto Federal 通訊）統計，中國每100戶家庭就有68戶訂閱網路影片，有效提升了網路影片用戶的比例。然而，圍繞該行業、所使用的資料以及允許在國內傳播的內容的規定非常嚴格。
  • 中國嚴格的法規環境進一步鞏固了三巨頭（愛奇藝、騰訊和優酷）的主導地位，阻止了 FAANG（Facebook、亞馬遜、蘋果、Netflix 和谷歌）等國際參與企業在中國營運。這些國際參與企業大規模使用建議引擎，透過廣告推動其他業務。這為該地區的國內參與企業留下了充足的機會，但與美國相比，其成長速度有所放緩。
  • 此外，電子商務巨頭阿里巴巴使用人工智慧和機器學習來推動建議。例如，AI·OS是阿里巴巴搜尋工程團隊開發的集個人化搜尋、建議和廣告於一體的線上平台。 AI·OS引擎體系支援各種業務場景，包括手機淘寶全搜尋頁面、手機淘寶大促活動資訊流會場、淘寶首頁商品建議、個人化建議、按類目和行業選品等。

建議引擎行業概覽

建議引擎市場較為分散，主要企業包括 IBM 公司、Google有限責任公司 (Alphabet Inc.)、亞馬遜網路服務公司 (Amazon.com Inc.)、微軟公司和 Salesforce Inc.。該市場的參與企業正在採用聯盟、合併和收購等策略來增強其產品供應並獲得永續的競爭優勢。

  • 2023 年 1 月－Coveo 宣布推出全新的 Coveo 商品中心。中心提供了豐富的功能，可協助您提供相關的購物旅程、建立忠誠度並提高盈利。該中心旨在幫助商家創造可轉換的客製化體驗。 Qubit 是一家總部位於倫敦的新興企業，為時尚公司和零售商提供人工智慧客製化技術，於 2021 年 10 月被 Coveo 收購。
  • 2022 年 10 月 - Algonomy 宣布推出適用於 Shopify 和 Commerceetools 的兩個重要連接器。 Algonomy Connectors 提供了一種將網路商店與 Shopify 或 Commercetools 整合的簡單方法，從而實現即時產品資料收集。連接器提供了對目錄整合流程的改進控制和洞察，無需依賴外部組織或資源來定期更新目錄資料。

其他福利

  • Excel 格式的市場預測 (ME) 表
  • 3個月的分析師支持

目錄

第1章 引言

  • 研究假設和市場定義
  • 研究範圍

第2章調查方法

第3章執行摘要

第4章 市場洞察

  • 市場概覽
  • 產業吸引力-波特五力分析
    • 供應商的議價能力
    • 買家/消費者的議價能力
    • 新進入者的威脅
    • 競爭對手之間的競爭
    • 替代品的威脅
  • COVID-19 市場影響評估
  • 技術簡介
    • 空間意識
    • 情境感知（機器學習與深度學習、自然語言處理）
  • 新興使用案例（主要使用案例跨多個最終用戶利用建議引擎）

第5章市場動態

  • 市場促進因素
    • 對行動和網路客製化數位商務體驗的需求不斷成長
    • 零售商擴大採用管理商品行銷和庫存規則
  • 市場限制
    • 由於使用者偏好的變化導致錯誤標記的複雜性

第6章市場區隔

  • 依實施類型
    • 本地
    • 雲
  • 按類型
    • 協同過濾
    • 基於內容的過濾
    • 混合推薦系統
    • 其他
  • 按最終用戶產業
    • 資訊科技/通訊
    • BFSI
    • 零售
    • 媒體娛樂
    • 醫療保健
    • 其他
  • 按地區
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東和非洲

第7章競爭格局

  • 公司簡介
    • 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8章投資分析

第9章：市場的未來

簡介目錄
Product Code: 67378

The Recommendation Engine Market size is estimated at USD 9.15 billion in 2025, and is expected to reach USD 38.18 billion by 2030, at a CAGR of 33.06% during the forecast period (2025-2030).

Recommendation Engine - Market - IMG1

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