Recommendation Engine Market - Growth, Trends, COVID-19 Impact, and Forecasts (2023 - 2028)

Recommendation Engine Market Analysis

The Recommendation Engine market is expected to register a CAGR of 37.46% during the period. With the growing amount of information over the internet and a significant rise in the number of users, it is becoming essential for companies to search, map, and provide 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. Most organizations globally are pursuing digital transformation, focusing on improving customer and employee experience, 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, positively impact 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. Integrating 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 significant attributes adding to 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, companies across the globe are facing short-term challenges across sustained revenues, health and safety, supply chain management, labor shortages, and pricing, to name a few. Multiple studies have identified that, amidst this outbreak, the use of advanced technologies, such as AI, ML, Analytics, and many more solutions, have assisted businesses in attaining positive outcomes.

Recommendation Engine Industry Segments

Recommendation engines are data filtering tools that use various algorithms and data to recommend the most relevant items to a particular customer. It first captures the past behavior of a customer and, based on that, recommends products that the users might be likely to buy. The integrated software context analyzes available data to make suggestions for something (product/services) that a website user might be interested among other possibilities. Recommendation engine systems are common among e-commerce, social media, and content-based websites.

The Recommendation Engine Market report is segmented by Deployment Mode (Cloud, On-Premise), Type (Collaborative Filtering, Content-Based Filtering, Hybrid Recommendation Systems), End-user Industry (Retail, Media and Entertainment, IT and Communication, BFSI, and Healthcare), and Geography.

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Recommendation Engine Market Trends

This section covers the major market trends shaping the Recommendation Engine Market according to our research experts:

IT and Telecom industry is showing a promising growth for recommendation engine market.

• Advances in technology allow providers to collect massive amounts of geolocation information. 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 in the telecom industry. 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 users' sentiments 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 uses an unsupervised machine learning algorithm to recommend similar content creators for any channel.
• The IT and telecommunication industry is expected to grow 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 social media penetration, may propel market growth.

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