Recommendation Engine Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)

Market Overview

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.

Scope of the Report

Recommendation engines are basically data filtering tools that make use of 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 engines systems are common among e-commerce, social media, and content-based websites.

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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.

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