Recommendation Engine Market [2028] by Trends, Demands, Forecast

Summary

Report Description

Forecast Period	2024-2028
Market Size (2022)	USD 4.71 Billion
Market Size (2028)	USD 26.23 Billion
CAGR (2023-2028)	33.22%
Fastest Growing Segment	Cloud
Largest Market	North America

Global recommendation engine market is anticipated to grow at a steady pace in the forecast period, 2024-2028. The increased desire to enhance the customer experience is fueling the need for recommendation engines. For instance, IBM Corporation expanded its IBM Watson Advertising Accelerator for OTT and video in May 2021. This tool was created to assist advertisers in moving beyond contextual relevance. Instead of relying on conventional advertising IDs, The amplifier uses artificial intelligence to constantly optimize OTT ad copy for better campaign outcomes at scale.

A recommendation engine is a system that recognizes employees and offers them relevant material. One example of how other technical developments continue to alter customer interest and utilize the available data is mobile applications. The advice engine is recognized as a key element of software and application products in the ICT sector. The two primary categories of recommendation engines are content-based filtering and collaborative filtering.

The recommendation system uses information analysis techniques to seek products that complement the user's preferences. For a variety of reasons, many advice engines are available. These include the picture recommendation engine, the product recommendation engine for online stores, the content recommendation engine, and the product suggestion engine. The increasing desire to enhance customer experience is satisfying the need for engines of recommendation.

Adoption of combine technology Fueling the Market Growth

Due to the increasing variety of industries and the subsequent growth in competition, many companies are attempting to combine technology, including computer science (AI), with their applications, businesses, analytics, and services. Around the world, quite a few firms are going through a digital transformation with an emphasis on using automation technologies to increase employee and customer knowledge. Due to the shift to digital, retailers can grow their client base, improve their customer connections, cut expenses, and raise employee morale. Increasing customer experience improvement methods and the growing scope of digital transformation are a few of the main factors driving the global recommendation engine market. For instance, in March 2021 SAP SE purchased Signavio. Signavio was a key player in the enterprise business process intelligence and process management arena. The solutions from Signavio were added to SAP's portfolio of business process intelligence and were designed to work with SAP's comprehensive process transformation portfolio. Owing to this the market is expected to grow in the forecast period.

Advantage To Record and Observe Customer Behavior Propelling the Market Growth

Due to the fact that customers usually make their purchasing decisions based on the position of the item in the shelf in brick and mortar businesses have a significant amount of ability to observe and shape customer behavior. The retail sector is adjusting to new and cutting-edge technologies as internet usage is increasing and new sales channels like e-commerce, mobile shopping, and smart technologies are emerging. With the help of latest technologies, such as self-checkout kiosks and smart point-of-sale systems, the market is growing rapidly. According to ZDNet, 70% of businesses have or are implementing a digital transformation plan. Since companies are moving towards digital transformation, the global recommendation engine market is expected to register a high CAGR in the forecast period.

Retailers may use digital transformation to increase customer acquisition, improve customer engagement, save operational costs, and boost staff morale. Along with other advantages, recommendation engine have a favorable effect on revenue and profits. Over the course of the predicted period, this positive influence will generate sizable prospects for the adoption of recommendation engines.

Moreover, the industry for recommendation engines is always concerned about the issue of inaccurate labeling brought by shifting user preferences. However, engineers are always trying to increase the precision and utility of suggestions. This fact is restraining the market growth in the forecast period.

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The Market is Expanding as a Result of Rising Demand for Customized Digital Commerce Experiences Across Mobile and the Web

Companies are looking for strategies and tools to take advantage of. Millions of unique consumers can benefit from these experiences by using private data. Execution determines the outcome. When properly implemented, personalized customer experience may help businesses stand out from the competition, win over customers' loyalty, and achieve a durable competitive advantage—all of which are crucial in the current market.

Due to the increasing demand from consumers, many marketing professionals across organizations have shifted their attention to improving customer experience over time. A 10% boost in year-over-year growth, a 10% rise in average order value, and a 25% increase in closure rates, for instance, according to Adobe company, can be observed by businesses with the strongest omnichannel customer engagement strategy. In addition, companies with strong omnichannel customer interaction strategies and consumer service improvement programs retain 89% of their consumers on average, as opposed to 33% for those with weaker strategies. Technologies make sure that the brands provide a consistent message about their services across all channels in light of the expanding number of channels in operation. During the projected period, the market is anticipated to benefit from the rising need for enhanced customer service.

Recent Developments

One of the most significant countries in the Asia-Pacific region with increasing technological adoption is China. One of the fastest internet networks and powerful e-commerce businesses, like Alibaba, are found in the nation. In addition, China is the world's second-largest OTT market after the United States. There were 68 memberships for every 100 houses in China, and the number of people watching internet videos is steadily rising. However, the nation has strong laws governing the sector, the data utilized, and the types of information that are permitted to broadcast there.
Alibaba, a major player in the e-commerce sector, employs AI and machine learning to power its recommendations. For instance, the Alibaba search engineering team created the online platform AI OS, which combines personalized search, recommendation, and advertising. The AI OS engine system supports a wide range of business situations, including product suggestions on the Taobao homepage, personalized recommendations, and product selection by category and industry. Taobao Mobile information flow venues for significant promotion events are also supported.
January 2023 - New Coveo Merchandising Hub's debut was announced by Coveo. With the support of The Hub's extensive feature set, businesses offer customers a highly relevant purchasing experience that encourages loyalty and increases profitability. It is intended to enable merchandisers to construct custom experiences that increase conversion.
In October 2021, Coveo purchased Qubit, a London-based start-up that provides fashion shops and companies with AI-powered personalization technologies.
In October 2022, Algonomy released two key links for Shopify and commerce tools. which enabled automated and seamless data exchange between Algonomy's products and online shops. Online stores may easily be integrated with Shopify or Commerce tools using Algonomy Connectors, allowing for the collection of real-time product data. Connectors boost control and visibility over the catalogue integration process and eliminate the need for depending on outside groups and resources to maintain the catalog's data on a regular basis.

Market Segmentation

The global recommendation engine market is divided based on type, deployment model, enterprise size, application, end user and region. Based on type, the market is divided into collaborative filtering, content-based filtering, and hybrid recommendation. Based on deployment model, the market is divided into on-premises and cloud, Based on enterprise size, the market is divided into large enterprises, small & medium enterprises. Based on application, the market is divided into Personalized Campaigns & Customer Delivery, Strategy Operations & Planning, Product Planning, and Proactive Asset Management. Based on end user, the market is segmented into retail & consumer goods, IT & telecom, healthcare & life science, BFSI, media & entertainment, and others. Based on region, the market is divided into North America, Asia-Pacific, Europe, South America, and Middle East & Africa.

Market Players

Major market players in the global recommendation engine market are IBM Corporation, Hewlett Packard Enterprise Development LP, Intel Corporation, Amazon Web Services, Adobe, Salesforce, Inc, Microsoft Corporation, Oracle Corporation, Google LLC, and SAP SE.

Attribute	Details
Base Year	2022
Historic Data	2018 – 2022
Estimated Year	2023
Forecast Period	2024 – 2028
Quantitative Units	Revenue in USD Million, and CAGR for 2018-2022 and 2024-2028
Report coverage	Revenue forecast, company share, growth factors, and trends
Segments covered	Type Deployment Model Enterprise Size Application End User Region
Regional scope	North America, Asia-Pacific, Europe, South America, Middle East & Africa
Country scope	United States, Canada, Mexico, China, India, Japan, South Korea, Indonesia, Germany, United Kingdom, France, Russia, Spain, Brazil, Argentina, Saudi Arabia, South Africa, UAE, Egypt, Israel
Key companies profiled	IBM Corporation, Hewlett Packard Enterprise Development LP, Intel Corporation, Amazon Web Services, Adobe, Salesforce, Inc, Microsoft Corporation, Oracle Corporation, Google LLC, SAP SE
Customization scope	10% free report customization with purchase. Addition or alteration to country, regional & segment scope.
Pricing and purchase options	Avail customized purchase options to meet your exact research needs. Explore purchase options
Delivery Format	PDF and Excel through Email (We can also provide the editable version of the report in PPT/Word format on special request)

Report Scope:

In this report, the global recommendation engine market has been segmented into following categories, in addition to the industry trends which have also been detailed below:

Recommendation Engine Market, By Type:

Collaborative Filtering
Content-based Filtering
Hybrid recommendation

Recommendation Engine Market, By Deployment Model:

On-Premises
Cloud

Recommendation Engine Market, By Application:

Personalized Campaigns & Customer Delivery
Strategy Operations & Planning
Product Planning
Proactive Asset Management

Recommendation Engine Market, By Enterprise Size:

Large Enterprises
Small & Medium Enterprises

Recommendation Engine Market, By End User:

Retail & Consumer Goods
IT & Telecom
Healthcare & Life Science
BFSI
Media
Entertainment
Others

Recommendation Engine Market, By Region:

North America

United States
Canada
Mexico

Asia-Pacific

China
India
Japan
South Korea
Indonesia

Europe

Germany
United Kingdom
France
Russia
Spain

South America

Brazil
Argentina

Middle East & Africa

Saudi Arabia
South Africa
Egypt
UAE
Israel

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Recommendation Engine Market.

Available Customizations:

Global recommendation engine market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

Detailed analysis and profiling of additional market players (up to five).

Global recommendation engine market is an upcoming report to be released soon. If you wish an early delivery of this report or want to confirm the date of release, please contact us at [email protected]

Table of content

1. Product Overview

1.1. Market Definition

1.2. Scope of the Market

1.3. Markets Covered

1.4. Years Considered for Study

1.5. Key Market Segmentations

2. Research Methodology

2.1. Objective of the Study

2.2. Baseline Methodology

2.3. Key Industry Partners

2.4. Major Association and Secondary Sources

2.5. Forecasting Methodology

2.6. Data Triangulation & Validation

2.7. Assumptions and Limitations

3. Executive Summary

4. Voice of Customers

5. Global Recommendation Engine Market Outlook

5.1. Market Size & Forecast

5.1.1. By Value

5.2. Market Share & Forecast

5.2.1. By Type (Collaborative Filtering, Content-based Filtering, Hybrid recommendation)

5.2.2. By Deployment Model (On-Premises, Cloud)

5.2.3. By Enterprise Size (Large Enterprises, Small and Medium Enterprises)

5.2.4. By Application (Personalized Campaigns and Customer Delivery, Strategy Operations and Planning, Product Planning and Proactive Asset Management)

5.2.5. By End User (Retail and Consumer Goods, IT and Telecom, Healthcare and Life Science, BFSI, Media and Entertainment, Others)

5.2.6. By Region

5.3. By Company (2022)

5.4. Market Map

6. North America Recommendation Engine Market Outlook

6.1. Market Size & Forecast

6.1.1. By Value

6.2. Market Share & Forecast

6.2.1. By Type

6.2.2. By Deployment Model

6.2.3. By Enterprise Size

6.2.4. By Application

6.2.5. By End User

6.3. North America: Country Analysis

6.3.1. United States Recommendation Engine Market Outlook

6.3.1.1. Market Size & Forecast

6.3.1.1.1. By Value

6.3.1.2. Market Share & Forecast

6.3.1.2.1. By Type

6.3.1.2.2. By Deployment Model

6.3.1.2.3. By Enterprise Size

6.3.1.2.4. By Application

6.3.1.2.5. By End User

6.3.2. Canada Recommendation Engine Market Outlook

6.3.2.1. Market Size & Forecast

6.3.2.1.1. By Value

6.3.2.2. Market Share & Forecast

6.3.2.2.1. By Type

6.3.2.2.2. By Deployment Model

6.3.2.2.3. By Enterprise Size

6.3.2.2.4. By Application

6.3.2.2.5. By End User

6.3.3. Mexico Recommendation Engine Market Outlook

6.3.3.1. Market Size & Forecast

6.3.3.1.1. By Value

6.3.3.2. Market Share & Forecast

6.3.3.2.1. By Type

6.3.3.2.2. By Deployment Model

6.3.3.2.3. By Enterprise Size

6.3.3.2.4. By Application

6.3.3.2.5. By End User

7. Asia-Pacific Recommendation Engine Market Outlook

7.1. Market Size & Forecast

7.1.1. By Value

7.2. Market Share & Forecast

7.2.1. By Type

7.2.2. By Deployment Model

7.2.3. By Enterprise Size

7.2.4. By Application

7.2.5. By End User

7.3. Asia-Pacific: Country Analysis

7.3.1. China Recommendation Engine Market Outlook

7.3.1.1. Market Size & Forecast

7.3.1.1.1. By Value

7.3.1.2. Market Share & Forecast

7.3.1.2.1. By Type

7.3.1.2.2. By Deployment Model

7.3.1.2.3. By Enterprise Size

7.3.1.2.4. By Application

7.3.1.2.5. By End User

7.3.2. India Recommendation Engine Market Outlook

7.3.2.1. Market Size & Forecast

7.3.2.1.1. By Value

7.3.2.2. Market Share & Forecast

7.3.2.2.1. By Type

7.3.2.2.2. By Deployment Model

7.3.2.2.3. By Enterprise Size

7.3.2.2.4. By Application

7.3.2.2.5. By End User

7.3.3. Japan Recommendation Engine Market Outlook

7.3.3.1. Market Size & Forecast

7.3.3.1.1. By Value

7.3.3.2. Market Share & Forecast

7.3.3.2.1. By Type

7.3.3.2.2. By Deployment Model

7.3.3.2.3. By Enterprise Size

7.3.3.2.4. By Application

7.3.3.2.5. By End User

7.3.4. South Korea Recommendation Engine Market Outlook

7.3.4.1. Market Size & Forecast

7.3.4.1.1. By Value

7.3.4.2. Market Share & Forecast

7.3.4.2.1. By Type

7.3.4.2.2. By Deployment Model

7.3.4.2.3. By Enterprise Size

7.3.4.2.4. By Application

7.3.4.2.5. By End User

7.3.5. Indonesia Recommendation Engine Market Outlook

7.3.5.1. Market Size & Forecast

7.3.5.1.1. By Value

7.3.5.2. Market Share & Forecast

7.3.5.2.1. By Type

7.3.5.2.2. By Deployment Model

7.3.5.2.3. By Enterprise Size

7.3.5.2.4. By Application

7.3.5.2.5. By End User

8. Europe Recommendation Engine Market Outlook

8.1. Market Size & Forecast

8.1.1. By Value

8.2. Market Share & Forecast

8.2.1. By Type

8.2.2. By Deployment Model

8.2.3. By Enterprise Size

8.2.4. By Application

8.2.5. By End User

8.3. Europe: Country Analysis

8.3.1. Germany Recommendation Engine Market Outlook

8.3.1.1. Market Size & Forecast

8.3.1.1.1. By Value

8.3.1.2. Market Share & Forecast

8.3.1.2.1. By Type

8.3.1.2.2. By Deployment Model

8.3.1.2.3. By Enterprise Size

8.3.1.2.4. By Application

8.3.1.2.5. By End User

8.3.2. United Kingdom Recommendation Engine Market Outlook

8.3.2.1. Market Size & Forecast

8.3.2.1.1. By Value

8.3.2.2. Market Share & Forecast

8.3.2.2.1. By Type

8.3.2.2.2. By Deployment Model

8.3.2.2.3. By Enterprise Size

8.3.2.2.4. By Application

8.3.2.2.5. By End User

8.3.3. France Recommendation Engine Market Outlook

8.3.3.1. Market Size & Forecast

8.3.3.1.1. By Value

8.3.3.2. Market Share & Forecast

8.3.3.2.1. By Type

8.3.3.2.2. By Deployment Model

8.3.3.2.3. By Enterprise Size

8.3.3.2.4. By Application

8.3.3.2.5. By End User

8.3.4. Russia Recommendation Engine Market Outlook

8.3.4.1. Market Size & Forecast

8.3.4.1.1. By Value

8.3.4.2. Market Share & Forecast

8.3.4.2.1. By Type

8.3.4.2.2. By Deployment Model

8.3.4.2.3. By Enterprise Size

8.3.4.2.4. By Application

8.3.4.2.5. By End User

8.3.5. Spain Recommendation Engine Market Outlook

8.3.5.1. Market Size & Forecast

8.3.5.1.1. By Value

8.3.5.2. Market Share & Forecast

8.3.5.2.1. By Type

8.3.5.2.2. By Deployment Model

8.3.5.2.3. By Enterprise Size

8.3.5.2.4. By Application

8.3.5.2.5. By End User

9. South America Recommendation Engine Market Outlook

9.1. Market Size & Forecast

9.1.1. By Value

9.2. Market Share & Forecast

9.2.1. By Type

9.2.2. By Deployment Model

9.2.3. By Enterprise Size

9.2.4. By Application

9.2.5. By End User

9.3. South America: Country Analysis

9.3.1. Brazil Recommendation Engine Market Outlook

9.3.1.1. Market Size & Forecast

9.3.1.1.1. By Value

9.3.1.2. Market Share & Forecast

9.3.1.2.1. By Type

9.3.1.2.2. By Deployment Model

9.3.1.2.3. By Enterprise Size

9.3.1.2.4. By Application

9.3.1.2.5. By End User

9.3.2. Argentina Recommendation Engine Market Outlook

9.3.2.1. Market Size & Forecast

9.3.2.1.1. By Value

9.3.2.2. Market Share & Forecast

9.3.2.2.1. By Type

9.3.2.2.2. By Deployment Model

9.3.2.2.3. By Enterprise Size

9.3.2.2.4. By Application

9.3.2.2.5. By End User

10. Middle East & Africa Recommendation Engine Market Outlook

10.1. Market Size & Forecast

10.1.1. By Value

10.2. Market Share & Forecast

10.2.1. By Type

10.2.2. By Deployment Model

10.2.3. By Enterprise Size

10.2.4. By Application

10.2.5. By End User

10.3. Middle East & Africa: Country Analysis

10.3.1. Saudi Arabia Recommendation Engine Market Outlook

10.3.1.1. Market Size & Forecast

10.3.1.1.1. By Value

10.3.1.2. Market Share & Forecast

10.3.1.2.1. By Type

10.3.1.2.2. By Deployment Model

10.3.1.2.3. By Enterprise Size

10.3.1.2.4. By Application

10.3.1.2.5. By End User

10.3.2. South Africa Recommendation Engine Market Outlook

10.3.2.1. Market Size & Forecast

10.3.2.1.1. By Value

10.3.2.2. Market Share & Forecast

10.3.2.2.1. By Type

10.3.2.2.2. By Deployment Model

10.3.2.2.3. By Enterprise Size

10.3.2.2.4. By Application

10.3.2.2.5. By End User

10.3.3. UAE Recommendation Engine Market Outlook

10.3.3.1. Market Size & Forecast

10.3.3.1.1. By Value

10.3.3.2. Market Share & Forecast

10.3.3.2.1. By Type

10.3.3.2.2. By Deployment Model

10.3.3.2.3. By Enterprise Size

10.3.3.2.4. By Application

10.3.3.2.5. By End User

10.3.4. Israel Recommendation Engine Market Outlook

10.3.4.1. Market Size & Forecast

10.3.4.1.1. By Value

10.3.4.2. Market Share & Forecast

10.3.4.2.1. By Type

10.3.4.2.2. By Deployment Model

10.3.4.2.3. By Enterprise Size

10.3.4.2.4. By Application

10.3.4.2.5. By End User

10.3.5. Egypt Recommendation Engine Market Outlook

10.3.5.1. Market Size & Forecast

10.3.5.1.1. By Value

10.3.5.2. Market Share & Forecast

10.3.5.2.1. By Type

10.3.5.2.2. By Deployment Model

10.3.5.2.3. By Enterprise Size

10.3.5.2.4. By Application

10.3.5.2.5. By End User

11. Market Dynamics

11.1. Drivers

11.2. Challenges

12. Market Trends & Developments

13. Company Profiles

13.1. IBM Corporation

13.1.1. Business Overview

13.1.2. Key Revenue and Financials (If Available)

13.1.3. Recent Developments

13.1.4. Key Personnel

13.1.5. Key Product/Services

13.2. Hewlett Packard Enterprise Development LP

13.2.1. Business Overview

13.2.2. Key Revenue and Financials

13.2.3. Recent Developments

13.2.4. Key Personnel

13.2.5. Key Product/Services

13.3. Intel Corporation

13.3.1. Business Overview

13.3.2. Key Revenue and Financials (If Available)

13.3.3. Recent Developments

13.3.4. Key Personnel

13.3.5. Key Product/Services

13.4. Amazon Web Services

13.4.1. Business Overview

13.4.2. Key Revenue and Financials (If Available)

13.4.3. Recent Developments

13.4.4. Key Personnel

13.4.5. Key Product/Services

13.5. Adobe

13.5.1. Business Overview

13.5.2. Key Revenue and Financials (If Available)

13.5.3. Recent Developments

13.5.4. Key Personnel

13.5.5. Key Product/Services

13.6. Salesforce, Inc.

13.6.1. Business Overview

13.6.2. Key Revenue and Financials (If Available)

13.6.3. Recent Developments

13.6.4. Key Personnel

13.6.5. Key Product/Services

13.7. Microsoft Corporation.

13.7.1. Business Overview

13.7.2. Key Revenue and Financials

13.7.3. Recent Developments

13.7.4. Key Personnel

13.7.5. Key Product/Services

13.8. Oracle Corporation,

13.8.1. Business Overview

13.8.2. Key Revenue and Financials (If Available)

13.8.3. Recent Developments

13.8.4. Key Personnel

13.8.5. Key Product/Services

13.9. Google LLC

13.9.1. Business Overview

13.9.2. Key Revenue and Financials (If Available)

13.9.3. Recent Developments

13.9.4. Key Personnel

13.9.5. Key Product/Services

13.10. SAP SE

13.10.1. Business Overview

13.10.2. Key Revenue and Financials (If Available)

13.10.3. Recent Developments

13.10.4. Key Personnel

13.10.5. Key Product/Services

14. Strategic Recommendations

15. About Us & Disclaimer

Figures and Tables

Frequently asked questions

Who are the leading players in global recommendation engine market?

Leading market players in the global recommendation engine market are IBM Corporation, Hewlett Packard Enterprise Development LP, Intel Corporation, Amazon Web Services, Adobe, Salesforce, Inc, Microsoft Corporation, Oracle Corporation, Google LLC, and SAP SE

What are the main drivers of the global recommendation engine market?

Increasing customer experience, improvement recommendation methods, and the expanding reach of digital transformation are among some of the main factors driving the global recommendation engine market.

Write about the benefits of using recommendation engines.

Recommendation engines offer personalized recommendation that aids in enhancing user engagement of consumers. Recommendation engines can help to reduce marketing costs by targeting ads and promotions to the users who are most likely to be interested in them. These benefits positively impact on the global recommendation engines market growth in the forecast period.

Which is the significant challenge faced by the global recommendation engine market?

The problem regarding erroneous labeling caused by changing user preferences is restraining the global recommendation engine market growth.

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Press Release

Recommendation Engine Market to be Dominated by Collaborative Filtering Segment

Aug, 2023

Increasing demand for personalized recommendations and growing internet usage in the industries are the major factors propelling the global recommendation engine market growth in the forecast period.

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