The Recommendation Engine market was valued at USD 2.12 billion in 2020, and it is expected to reach USD 15.13 billion by 2026, registering a CAGR of 37.46% during the period of 2021-2026. With the growing amount of information over the internet along with a significant rise in the number of users, it is becoming essential for companies to search, map, and provide them with the relevant chunk of information according to their preferences and tastes.
- 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. The majority of the organizations globally are pursuing digital transformation, focusing on improving the experience of customers and employees, which are being leveraged by automation solutions.
- 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, create a positive impact on the revenue and margins. This positive impact is expected to create significant opportunities for adopting recommendation engines over the forecast period.
- The advancement of digitalization across emerging economies, coupled with the growth of the e-commerce market, has driven the demand for recommendation engines. The integration of the machine learning model across AI-based cloud platforms drives automation across multiple end-user industries.
- The increasing need to consider all the user information to personalize and customize the best possible output is expected to impact the adoption of recommendation systems across industries. One of the major attributes adding to the consumer information is the content that the customer sees, i.e., the visual of the product.
- The COVID-19 pandemic has led businesses to take precautionary measures leading to the closures of several outlets. Owing to this, businesses across the globe are facing short-term challenges across sustained revenues, health, and safety, supply chain management, labor shortages, pricing, to name a few. Multiple studies during the period have identified that, amidst this outbreak, the use of advanced technologies, such as AI, ML, Analytics, and many more solutions, have assisted businesses to attain positive outcomes.
Key Market Trends
IT and Telecom industry is showing a promising growth for recommendation engine market.
- Advances in technology allow providers to collect massive amounts of information on geolocation. The challenge is effectively processing this data and combining it with existing customer intelligence to improve the success of marketing campaigns in near-real-time and offer convenient and relevant services and incentives for increased ROI.
- The IT industry is also witnessing the gradual adoption of recommendation engines to build product recommendation chatbots with the help of ML and AI algorithms. For example, gnani.ai offers a personalized recommendation chatbot based on user preferences and chat history. This drives more customers to the final stage of the sales funnel.
- Furthermore, vendors are rolling out new solutions in the recommendation engine market to have a strong foothold for the telecom industry. For instance, in January 2021, Envestnet Inc. announced the launch of a new version of its recommendation engine for enterprise organizations.
- The penetration of social media among people is also driving market growth. Companies use these recommendation platforms to gauge the sentiments of the users and feed their social media pages with their respective product choices through advertisements. These advertisements are chosen based on clicks, watch time, likes/dislikes, comments, freshness, and upload frequency, among other factors. For instance, Youtube is using an unsupervised machine learning algorithm to recommend similar content creators for any given channel.
- The IT and telecommunication industry is expected to witness growth during the forecast period. The increasing focus of businesses in this end-user industry to make investments and initiatives to enhance customer experience and increase customer retention, coupled with the high level of social media penetration, may propel market growth.
Asia Pacific is Expected to Hold Significant Market Share
- Led by countries such as Australia, India, China, and South Korea, among other 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.
- China is the second-largest OTT market in the world, after the United States. According to Instituto Federal de Telecomunicaciones (Mexico), as of January 2020, 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 they use, as well as the content that is allowed to be circulated in the country.
- Moreover, one of the e-commerce giants, Alibaba, uses AI and machine learning to drive its recommendations. For instance, AI OS is an online service 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, and personalized recommendations and product selection by category and industry.
- Additionally, the changing consumer behavior after the spread of COVID-19 across the region is expected to boost the adoption of recommendation engines by end-users, such as retail, hospitality, and BFSI. Furthermore, in January 2021, Google Cloud announced its plans to launch an AI recommendation engine for online retailers worldwide, including Asia. The cloud computing service's Product Discovery Solutions for Retail may allow retailers to implement search and recommendation capabilities that enhance customer engagement and improve conversions across their digital properties.
The recommendation engine market is competitive and consists of a number of major players. In terms of market share, some of the players are currently dominating the market. However, with the advancement in analytics across AI-based platforms, new players are increasing their market presence, thereby expanding their business footprint across the emerging economies. Hence the market concentration is low.
- May 2021 - IBM announced the expansion of IBM Watson Advertising Accelerator for OTT and video, designed to help marketers move beyond contextual relevance alone. The Accelerator aims to leverage artificial intelligence to dynamically optimize OTT ad creative for improved campaign outcomes at scale, not dependent on traditional advertising identifiers. While compatible with most streaming platforms, IBM is partnering closely with Xandr, an industry leader in programmatic and converged video solutions, to help scale the adoption of Accelerator.
- February 2021 - Microsoft Corporation launched Microsoft Viva, an employee experience platform that aims to deliver first- and third-party products across learning, wellness, insights, knowledge, recommendations, and engagement. As a part of this, the company debuted Viva Topics, Viva Connections, Viva Insights, and Viva Learning.
- January 2021 - Google Cloud launched an AI recommendation engine for online retailers with a new suite of solutions to strengthen personalized online shopping. Product Discovery Solutions for Retail includes Recommendations AI that can deliver highly personalized product recommendations at scale and across all channels.
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1.2 Scope of the Study
4.2 Industry Attractiveness - Porter's Five Forces Analysis
4.2.1 Bargaining Power of Suppliers
4.2.2 Bargaining Power of Buyers
4.2.3 Threat of New Entrants
4.2.4 Threat of Substitute Products
4.2.5 Intensity of Competitive Rivalry
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)
5.1.1 Increasing Demand for 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 Challenges
5.2.1 Complexity Regarding Incorrect Labeling Due to Changing User Preferences
6.2.1 Collaborative Filtering
6.2.2 Content-Based Filtering
6.2.3 Hybrid Recommendation Systems
6.2.4 Other Types
6.3 End-User Industry
6.3.1 It and Telecommunication
6.3.4 Media and Entertainment
6.3.6 Other End-User Industries
6.4.1 North America
6.4.4 Latin America
6.4.5 Middle East and Africa
7.1.1 IBM Corporation
7.1.2 Google LLC (Alphabet Inc.)
7.1.3 Amazon Web Services Inc.
7.1.4 Microsoft Corporation
7.1.5 Salesforce.Com 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 Co.
7.1.11 Qubit Digital Ltd.
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.
A selection of companies mentioned in this report includes:
- IBM Corporation
- Google LLC (Alphabet Inc.)
- Amazon Web Services Inc.
- Microsoft Corporation
- Salesforce.com Inc.
- Unbxd Inc.
- Oracle Corporation
- Intel Corporation
- SAP SE
- Hewlett Packard Enterprise Co.
- Qubit Digital Ltd.
- Algonomy Software Pvt Ltd
- Recolize GmbH
- Adobe Inc.
- Dynamic Yield Inc.
- Kibo Commerce
- Netflix Inc.