查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1445475

推薦引擎：市場佔有率分析、產業趨勢與統計、成長預測（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個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

簡介目錄

推薦引擎市場規模預計到 2024 年為 68.8 億美元，預計到 2029 年將達到 287 億美元，在預測期內（2024-2029 年）年複合成長率為 33.06%。

推薦引擎-市場

隨著企業數量的增加和企業之間競爭的加劇，許多公司正在尋求將人工智慧 (AI) 等技術與其應用程式、業務、分析和服務整合。世界各地的大多數組織都在追求數位轉型，重點是透過自動化解決方案改善客戶和員工體驗。

主要亮點

  • 由於新興國家數位化的進步和電子商務市場的成長，對推薦引擎的需求不斷增加。跨基於人工智慧的雲端平台整合機器學習模型可推動多個最終用戶產業的自動化。
  • 傳統上，消費者在商店貨架上做出購買決定，這為實體零售商提供了強大的平台來了解和影響消費者的行為和偏好。然而，隨著網路普及的提高以及電子商務、行動購物和智慧技術等新銷售管道的出現，零售業正在適應新技術和先進技術。這些技術，例如智慧 POS 解決方案和自助結帳系統亭，將傳統實體店轉變為全通路商店。據 ZDNet 稱，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獲取新客戶、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。除此之外，這些好處對收益和利潤有正面影響。這種正面影響為預測期內採用推薦引擎創造了巨大的機會。
  • 由於用戶偏好變化而導致標籤不準確的挑戰是推薦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其建議的準確性和相關性。隨著科技的進步，我們希望未來能找到更有效的解決方案來應對這項挑戰。
  • 根據思科旗下 AppDynamics 最近發布的轉型代理報告，在 COVID-19 大流行期間，95% 的組織中的技術優先事項發生了變化，88% 的組織表示數位客戶經驗在其組織中發生了變化。我將其報告為優先事項。客戶轉向自助服務工具，例如聊天、通訊和對話機器人。因此，企業現在可以使用這些工具提供卓越的客戶體驗，同時減少對實體店和實況活動的傳統依賴，這在社交距離時代是不可能的。隨著這些公司採用該技術，預計這將進一步增加推薦引擎所提供的好處。

推薦引擎市場趨勢

跨行動和網路的數位商務體驗客製化需求的不斷成長推動了市場成長

  • 企業正在尋找方法和技術，透過提供高度個人化的客戶體驗來利用競爭對手難以模仿的優勢。這些體驗使用專有資料為數百萬個人客戶提供更好的體驗。結果取決於執行。如果執行得當，個人化的客戶體驗可以幫助企業脫穎而出，獲得客戶忠誠度和永續的競爭優勢，這在當前情況下是非常需要的。
  • 客戶不再在實體店做出決定，而是使用網頁瀏覽器或行動電話在數位貨架前在線上做出決定。對於從事零售業經營的公司來說，其產品的價格、地點和促銷不再僅僅與附近貨架上的產品進行比較，而是與擁有世界各地網站的零售商的替代產品進行比較。在這方面，使用AI和ML的推薦引擎等技術可確保滿足客戶的要求，確保客戶的需求和產品處於同一水平，並確保您可以領先競爭對手一步。
  • 多年來，由於客戶需求不斷增加，許多組織的行銷專業人員都致力於改善客戶體驗。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%與前一年同期比較成長，平均訂單價值增加 10%，獲勝率增加 25%。這是有性別的。此外，擁有強大全通路客戶參與策略和消費者服務改善計畫的品牌平均保留了 89% 的客戶，而全通路客戶參與策略較弱的品牌則客戶維繫。
  • 隨著使用管道數量的增加，技術使品牌能夠確保在所有管道上訊息有關其產品的一致訊息。對更好的客戶服務的需求不斷成長預計將在預測期內推動需求並對市場產生積極影響。
  • 總體而言，對個人化數位商務體驗不斷成長的需求正在推動推薦引擎市場。據泰雷茲集團稱，銀行和金融部門在消費者資訊安全方面被認為是值得信賴的。全球超過 40% 的消費者表示，他們信任數位銀行業務和金融服務部門的資料。醫療保健提供者是數位服務中第二值得信賴的行業，37% 的受訪者表示該行業是最安全的。公司正在利用人工智慧技術為客戶提供有針對性的建議、推動銷售並提高客戶滿意度。

亞太地區將經歷最快的成長

  • 以澳洲、印度、中國和韓國等國家主導的亞太地區預計將成為推薦引擎市場成長最快的地區。
  • 中國是亞太地區主要國家之一，也是技術採用者。該國擁有最快的網際網路樂隊之一和阿里巴巴等強大的電子商務公司。
  • 此外，中國是僅次於美國的全球第二大OTT市場。根據墨西哥聯邦電訊統計，中國每100戶家庭就有68戶訂閱，網路視訊用戶比例大幅上升。然而，該國對所使用的行業和資料以及允許在國內傳播的內容有非常嚴格的規定。
  • 中國嚴格的法規環境進一步確保了三方（愛奇藝、騰訊、優酷）的主導地位，這阻止了 FAANG（Facebook、亞馬遜、蘋果、Netflix、Google）等國際玩家在中國運作。這些國際參與者大規模使用推薦引擎，並透過廣告推廣其他業務。這為該地區的國內企業留下了充足的機會，導致其成長速度低於美國。
  • 此外，電子商務巨頭阿里巴巴利用人工智慧和機器學習來推動推薦。例如，AI OS是阿里巴巴搜尋工程團隊開發的集個人化搜尋、推薦和廣告於一體的線上平台。 AI OS引擎系統提供淘寶全網行動搜尋頁面、淘寶各大促銷活動行動資訊流地、淘寶首頁商品推薦、個人化推薦、按品類、業界選購等多種功能，支援業務場景。

推薦引擎行業概況

推薦引擎市場呈現分散化，主要參與者包括 IBM 公司、Google LLC (Alphabet Inc.)、Amazon Web Services Inc. (Amazon.com Inc.)、Microsoft Corporation 和 Salesforce Inc.。市場參與者正在採取聯盟、併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 宣布新的 Coveo 商品行銷 Hub 首次亮相。該中心提供了一系列豐富的功能，使企業能夠提供相關的購物旅程，有助於提高忠誠度並提高盈利。它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Start-Ups，為時尚公司和零售商提供人工智慧驅動的客製化技術，於 2021 年 10 月被 Coveo 收購。
  • 2022 年 10 月 - Algonomy 宣布推出適用於 Shopify 和 Commercetools 的兩個重要連接器。這使得 Algonomy 產品和電子商店之間能夠自動、順暢地進行資料交換。 Algonomy 連接器提供了一種將網路商店與 Shopify 或 Commercetools 整合的簡單方法，並支援即時產品資料收集。連接器可讓您更好地控制和深入了解目錄整合流程，從而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 個月分析師支持

目錄

第1章簡介

  • 研究假設和市場定義
  • 調查範圍

第2章調查方法

第3章執行摘要

第4章市場洞察

  • 市場概況
  • 產業吸引力－波特五力分析
    • 供應商的議價能力
    • 買方議價能力
    • 新進入者的威脅
    • 競爭公司之間的敵意強度
    • 替代產品的威脅
  • 評估 COVID-19 對市場的影響
  • 技術簡介
    • 地理空間意識
    • 情境辨識（機器學習與深度學習、自然語言處理）
  • 新用例（跨多個最終用戶利用推薦引擎的關鍵用例）

第5章市場動態

  • 市場促進因素
    • 對跨行動和網路的客製化數位商務體驗的需求不斷成長
    • 零售商更多採用產品管理和庫存規則
  • 市場限制因素
    • 由於使用者設定的變更而導致錯誤標籤的複雜性

第6章市場區隔

  • 依部署方式
    • 本地
    • 雲
  • 按類型
    • 協同過濾
    • 基於內容的過濾
    • 混合推薦系統
    • 其他類型
  • 按最終用戶產業
    • 資訊科技/通訊
    • BFSI
    • 零售
    • 媒體與娛樂
    • 衛生保健
    • 其他最終用戶產業
  • 按地區
    • 北美洲
    • 歐洲
    • 亞太地區
    • 拉丁美洲
    • 中東和非洲

第7章 競爭形勢

  • 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 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