Global Recommendation Engine Market By Type (Collaborative Filtering, Content-based Filtering and Hybrid Recommendation), By Application (Personalized Campaigns & Customer Delivery, Product Planning & Proactive Asset Management and Strategy Operations & P

Global Recommendation Engine Market By Type (Collaborative Filtering, Content-based Filtering and Hybrid Recommendation), By Application (Personalized Campaigns & Customer Delivery, Product Planning & Proactive Asset Management and Strategy Operations & Planning), By Deployment Type (Cloud and On-premise), By Organization Size (Large Enterprises and Small & Medium Enterprises), By End Use (Retail, BFSI, Healthcare, Media & Entertainment, Information Technology and Others), By Regional Outlook, Industry Analysis Report and Forecast, 2021 - 2027

The Global Recommendation Engine Market size is expected to reach $11.4 billion by 2027, rising at a market growth of 32.5 % CAGR during the forecast period.

Recommendation engines are data filtering technologies that use a variety of algorithms and data to suggest the most relevant results to a certain client. It begins by capturing a customer's prior behavior and then offers products that the customers are likely to purchase based on that information. The integrated software system evaluates the available data to provide recommendations for things (products/services) that a website user might be interested in, among other things. E-commerce, social media, and content-based websites all use recommendation engines systems.

Many firms are attempting to integrate technology such as artificial intelligence (AI) with their apps, businesses, analytics, and services due to the growing competition in their respective markets. The majority of companies across the world are pursuing digital transformation, concentrating on improving customer and employee experiences through automation technologies.

Retailers may use digital transformation to connect with new customers, better engage with existing customers, save operating costs, and increase employee motivation. Along with that, the rising digitalization and high adoption of smart devices by the consumers would fuel the demand and growth of the recommendation engine market over the forecast period.

COVID-19 Impact Analysis

The outbreak of the COVID-19 pandemic has significantly impacted various companies across the business domain. Several businesses have taken precautionary in response to the COVID-19 pandemic, which has resulted in the closure of some establishments. As a result, businesses all over the world are experiencing short-term difficulties in areas such as sustained revenues, health and safety, supply chain management, labour shortages, and pricing, to mention a few.

Additionally, suppliers can use digital transformation to gain new consumers, communicate with existing customers, lower the cost of corporate operations, and boost employee enthusiasm. These benefits have a favourable effect on earnings and surpluses. This would positively impact the demand for recommendation engines among the enterprises in the coming years.

Market Growth Factors:

Rising focus on enhancing customer satisfaction

The increased focus on improving customer experience in the digital space is a primary factor driving the demand for recommendation engines by the companies. Furthermore, it is critical to improve customer experience in order to increase customer engagement and retention, as well as to boost revenue and return on investment (RoI). Upselling and cross-selling opportunities arise naturally as a result of smart product suggestions made by using recommendation engine.

Rapid pace of digitalization

Online buying has increased as a result of the rise in digitization across the world and the emergence of new e-commerce platforms. These recommendation engines enable easy browsing and display products or information based on the customer's past search. Furthermore, mobile phone ownership is highly contributing to e-commerce growth and encouraging e-commerce companies to use recommendation engines.

Market Restraining Factors:

Security and privacy concerns

Consumers can obtain more credible feedback if a recommendation engine generates more personal data. The recommender may acquire information such as the user's identification, demographic profile, behavioural data and purchase history, ranking history, and more. These details could be particularly sensitive in terms of privacy. Providing this information to the companies can increase the risks of privacy and security breaches. The data could be sold to a third party without the client's authorization, or it could be hacked by the attackers.

Type Outlook

Based on type, the recommendation engine market is classified into Collaborative Filtering, Content-based Filtering and Hybrid Recommendation. The collaborative filtering segment dominated the recommendation engine market with the maximum revenue share in 2020 and is estimated to maintain its dominance over the forecast period. This is due to increasing demand for dependable recommendation engines by the e-commerce companies to improve their consumers' shopping experiences by proposing products based on their tastes and preferences. For example, Spotify employs collaborative filtering to suggest “Discover Weekly” and other playlists to listeners based on their previous listening habits.

Application Outlook

By application, the recommendation engine market is classified into Personalized Campaigns and Customer Delivery, Strategy Operations & Planning and Product Planning and Proactive Asset Management. The personalized campaigns and customer delivery segment acquired the largest revenue share in the recommendation engine market and is estimated to maintain its dominance during the forecast period. It is owing to the increase in the requirement to provide better customer experience and services by various companies across different industrial verticals.

Deployment Type Outlook

On the basis of deployment type, the recommendation engine market is bifurcated into cloud and on-premise. The cloud segment procured the highest revenue share in the recommendation engine market in 2020 and is anticipated to continue this trend over the forecast period. This is due to an increase in demand for such solutions by companies that are using cloud technologies to integrate recommendation engines into their web-based services. Several companies in the media and entertainment and retail industries are highly using Cloud since most of their data is stored on cloud storage.

Organization Size Outlook

Based on organization size, the recommendation engine market is segmented into large enterprises and small & medium enterprises. The large enterprise segment garnered the highest revenue share in the recommendation engine market in 2020 and is projected to maintain this trend during the forecast period. It is owing to the high adoption of recommendation engines by the large enterprises to make better business decisions, manage their company portfolio more efficiently, and gain a competitive advantage in the market.

End Use Outlook

Depending on the end-use, the recommendation engine market is divided into Information Technology, Healthcare, Retail, BFSI, Media & Entertainment and Others. The retail segment procured the maximum revenue share in the recommendation engine market in 2020 and is anticipated to continue this trend during the forecast period. It is due to increased competition in the market, along with that, e-commerce and retail firms are increasingly adopting recommendation systems to give better and faster services to their customers.

Regional Outlook

Region-wise, the recommendation engine market is analyzed across North America, Europe, Asia Pacific and LAMEA. North America emerged as the leading region in the recommendation engine market with the largest revenue share in 2020 and is projected to continue this trend over the forecast period. This is due to the growing acceptance of modern technology as well as increased government support for developing technologies in this region.

The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Microsoft Corporation and Google LLC are the forerunners in the Recommendation Engine Market. Companies such as Amazon.com, Inc., SAP SE and Intel Corporation are some of the key innovators in the Market.

The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Oracle Corporation, Microsoft Corporation, SAP SE, Salesforce.com, Inc., Adobe, Inc., Google LLC, Intel Corporation, Hewlett-Packard Enterprise Company, and Amazon.com, Inc.

Recent Strategies deployed in Recommendation Engine Market

Partnerships, Collaborations and Agreements:

Nov-2021: Google Cloud formed a partnership with Pegasystems, a US-based tech company. This partnership aimed to assist enhance experiences in healthcare with better data insights and personalization. Together, the companies would bring the capabilities of Google Cloud’s Healthcare Data Engine and Pega’s suite of intelligent healthcare solutions.

Mar-2021: Adobe came into a partnership with government agencies in all 50 states. This partnership aimed to empower their digital modernization via Adobe Experience Cloud and Adobe Document Cloud. Utilizing Adobe Experience Cloud, governments are rebuilding their online presence, which makes their websites & apps easier to determine and make sure that the content is customized and updated in real-time, and developing intuitive forms that work on any device.

Jul-2020: Intel partnered with Burger King, an American multinational chain of hamburger fast-food restaurants. This partnership aimed to integrate a recommendation engine for the touchscreen ordering of Burger King.

Product Launches and Product Expansions:

Apr-2021: Adobe launched new capabilities for Adobe Commerce powered by Magento, which extend the intelligence of features such as Product Recommendations and Live Search. Adobe Commerce merchants would soon be able to set up recommendations with different rules differences in pricing depending on the customer, variance in the catalog of goods, which are provided up that also takes into account the nuances of a B2B buyer’s buying behavior.

Apr-2021: Adobe released the next generation of its Real-time Customer Data Platform (CDP). Adobe Real-time CDP enables brands to activate known & unknown customer data to manage the whole customer profile and journey effortlessly in one system, without the requirement for third-party cookies.

Mar-2021: HP Enterprise launched the Software Defined Opportunity Engine (SDOE), a cloud-based machine-learning platform. This platform takes a snapshot of the customer’s workloads, configuration, and use patterns to make a quote for the best-suited solution for the customer within a minute.

Feb-2021: Microsoft unveiled Microsoft Viva, the first employee experience platform. This platform aimed to bring tools for learning, employee engagement, wellbeing, and knowledge discovery, directly into the flow of people’s work.

Dec-2020: Amazon Web Services unveiled Amazon Monitron, Amazon Lookout for Equipment, the AWS Panorama SDK, the AWS Panorama Appliance, and Amazon Lookout for Vision. These new machine learning services would assist industrial and manufacturing customers with inbuilt intelligence in their production processes to enhance operational efficiency, security, quality control, and workplace safety.

Nov-2020: Adobe introduced new features to its Adobe CDP Platform. These features would make workflows for marketing teams and connect to popular B2B marketing platforms. By combining the Adobe Experience Platform CDP with Marketo, B2B sales & marketing teams would able to link their data that include complicated relationships between buying & selling teams, with the analytics, marketing, targeting, and segmenting applications in the Adobe Experience Cloud.

Jul-2020: Google released the public beta of Recommendations AI, a fully-managed service. Recommendations AI removes the requirement for retailers to manually curate rules or operate recommendation models in-house and features prevailing integration with Merchant Center, Google Analytics 360, Google Tag Manager, Cloud Storage, and Big Query.

Jun-2020: Intel released its 3rd-gen Intel Xeon Scalable processors. These processors would allow customers to boost the development and usage of artificial intelligence (AI) and analytics workloads running in data centers.

Acquisitions and Mergers:

Oct-2020: SAP signed into an agreement to acquire Emarsys, an omnichannel customer engagement platform provider. This acquisition aimed to assist SAP's commerce offering, and help customers provide omnichannel engagements in real-time.

Jul-2020: IBM took over WDG Automation, a Brazilian-based software provider. This acquisition aimed to enable IBM to extend their automation portfolio with RPA software and offer effortless integrations into IBM Cloud Pak for Automation & IBM Digital Business for Automation platform, providing their customer base fully end-to-end automation platform.

Scope of the Study

Market Segments covered in the Report:

By Type

• Collaborative Filtering
• Content-based Filtering and
• Hybrid Recommendation

By Application

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

By Deployment Type

• Cloud and
• On-premise

By Organization Size

• Large Enterprises and
• Small & Medium Enterprises

By End Use

• Retail
• BFSI
• Healthcare
• Media & Entertainment
• Information Technology and
• Others

By Geography

• North America
• US
• Canada
• Mexico
• Rest of North America
• Europe
• Germany
• UK
• France
• Russia
• Spain
• Italy
• Rest of Europe
• Asia Pacific
• China
• Japan
• India
• South Korea
• Singapore
• Malaysia
• Rest of Asia Pacific
• LAMEA
• Brazil
• Argentina
• UAE
• Saudi Arabia
• South Africa
• Nigeria
• Rest of LAMEA

Companies Profiled

• IBM Corporation
• Oracle Corporation
• Microsoft Corporation
• SAP SE
• Salesforce.com, Inc.
• Adobe, Inc.
• Google LLC
• Intel Corporation
• Hewlett-Packard Enterprise Company
• Amazon.com, Inc.

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