추천 엔진 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2024-2029년)

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).

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