Content Recommendation Engine Market - Growth, Trends, Forecasts (2022 - 2027)

The content recommendation engine market is expected to reach a CAGR of 25 % during the forecast period 2020-2025. Content recommendation engines have been around for some time now and are being continuously improved and upgraded for delivering services as per individual user preferences. It uses Artificial Intelligence to identify and categorize content by topics. However, despite the considerable amount of researches done in the context of recommender systems, the specific problem of integrating tags into standard recommender system algorithms, especially content-based ones, is less explored than the problem of recommending tags. Folksonomies provide new opportunities in the field of recommender systems which can cater towards the significant growth.

Key Highlights

• The advancement of digitalization across emerging economies drives the market. The number of people around the world using the internet has grown to around 4.54 billion, which is an increase of 7 % (298 million new users) compared to January 2019 (source: Global Web Index). Further, there are 3.8 billion social media users in January 2020, and this number is increasing by more than 9 % annually (321 million new users). Also, online via mobile device e-commerce purchase in the third quarter of 2019, Indonesia, Thailand, and the Philippines were having the highest user with 80%, 69%, and 66%, respectively. Such trends are focusing the players for the adoption of a content recommendation engine to increase the revenue, retention, and traffic.
• Further, the advantage in functionality over collaborative based filtering drives the market. Content-based recommenders exploit only ratings provided by the active user to build her/his own profile. Instead, collaborative filtering methods need ratings from other users in order to find the “nearest neighbors” of the active user. Also, Content-based recommenders are capable of recommending items that are not yet rated by any of the users. As a consequence, they do not suffer from the first-rater problem, which affects collaborative recommenders, which rely only on users' preferences to make recommendations.
• However, Limited Content Analysis is a major challenge for market growth. Content-based techniques have a natural limit in the number and type of features associated, whether automatically or manually, with the objects they recommend. The domain knowledge is needed for it. No content-based recommendation system can provide the suitable suggestions if the analyzed content does not contain enough data to discriminate items the user likes from items the user does not like. To sum up, both automatic and manually assignment of features to items could not be sufficient to define distinguishing aspects of items that turn out to be necessary for the elicitation of user interest.
• Further, in the COVID-19 pandemic, the market has not slowed down as the retention rate for the e-commerce sector, media, and entertainment segment has risen sharply, which caters to the adoption of content recommendation engine platform. Accenture says they expect a 160% increase in e-commerce purchases from new and low-frequency buyers. Also, the rise in the penetration of the OTT platform has boosted the market. In India, most users are more likely to switch towards paid OTT audio subscription, only if the charges are approximately Rs 25 per month, adding that 62 percent of consumers surveyed are willing to switch to paid subscription models in the pandemic period.

Key Market Trends

E-Commerce to Witness Significant Market Growth

• The biggest challenge for e-commerce businesses is ensuring superior customer service to shoppers. The massive adoption of the Web as an e-commerce platform has led to the fundamental change in a way that businesses of all sizes interact with their customers. The use of content recommender systems in an e-commerce environment can impact financial performance as well as the intensity of the dialogue with customers through increasing Cross-sell and building loyalty.
• According to Aspect Software Inc., in the United States in 2018, the churn rate for retail was 27%, and for online retail, it was 22%. Further, Recurly analyzed that more than 900 e-commerce sites that use their subscription management platform over the 24 months (January 2017 to December 2018) found that there was a 10.65% churn rate.
• With increasing churn rate percentage, e-commerce players are more focused on customer purchase activity and based on it, the recommending products are shown to the customers through their content recommendation platform.
• By mapping certain keywords from the product text, the content recommendation engine enables eCommerce businesses to make precise and accurate recommendations based on a single customer’s purchase history, to scale the recommendation engine to more users and thereby boost RO, to suggest new products by training the algorithms with selective keywords and demographic details of specific customers.
• This type of recommender engine is widely used in niche eCommerce stores (Discogs and Artsy use this approach). Further, Amazon Personalize blends real-time user activity data with user profile and product information to identify the optimal product or content recommendations. In the second quarter of 2020, Amazon's net revenue from the online sales segment amounted to almost USD 45.9 billion, and this revenue is majorly contributed through its content recommendation platform. According to Amazon, 35% of its sales are driven by its recommendation engine.
• Further, a player such as Episerver includes Commerce, Content Management, Search, Personalization, A/B Testing, Analytics, and Marketing Automation in one cloud subscription, which provides solutions to the e-commerce players catering to the market growth.

North America to Register the Highest Growth Rate During the Forecast Period

• North America is anticipated to be a significant revenue-generating region, thereby highly focusing on the growth of innovations across the US and Canada regions. These countries have the most competitive and rapidly changing market across the globe.
• Netflix remains the leading streaming platform of the United States, with Amazon Prime Video, Hulu, and HBO Now. Companies like Netflix collect thousands of data points from several places for making suggestions to users with the help of the tool known as a recommender engine.
• With over 7,000 movies and shows in the Netflix catalog, it is nearly impossible for users to find movies they will like on their own. The large platform needs a recommendation engine algorithm to automate the search process for users.
• Further, YouTube is the second most visited website in the United States, with around 400 hours of content uploaded per minute, with recommending fresh content. Google has switched to deep learning as a general framework for learning the problems. Since Google Brain has released Tensorflow, it became sufficiently easy to train, test, and deploy deep neural networks in a distributed fashion.
• Moreover, according to the US Bureau, e-commerce sales in 2018 were USD 524 billion, while in 2019, it increased to USD 602 billion. With increasing online sales, the adoption of content recommendation in such a segment is significantly catering to market growth.

Competitive Landscape

The content recommendation engine market is moderately competitive, consisting of few major players, and in terms of market share, few of the players are currently dominating the market. However, with the advancement in the analytics across AI-based platforms, new players are increasing their market presence, thereby expanding their business footprint across the emerging economies. Key players are Amazon Web Services (Amazon.com, Inc.), Taboola, Inc. (Outbrain, Inc.), Cxense ASA, and others. Recent developments in the market are -

• March 2020 - Aiclick united Tencent text travel officially launched a new product - text travel content recommendation management system. The product is jointly developed by aiclick.com and Tencent text travel, aiming to provide domestic scenic spot operators and relevant tourism enterprise customers with scenic spot popularity, audience trend, audience portrait, and regional comparison and other market insights analysis maps and professional content marketing ability.

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