Recommendation Engine Market by Type (Collaborative Filtering, Content-Based Filtering, and Hybrid Recommendation), Deployment Mode (Cloud and On-Premises), Technology, Application, End-User, and Region - Global Forecast to 2022

The recommendation engine market based on AI, is projected to grow at a CAGR of 40.7% during the forecast period

The market for recommendation engine based on AI, is expected to grow from USD 801.1 million in 2017 to USD 4414.8 million by 2022, at a Compound Annual Growth Rate (CAGR) of 40.7% during the forecast period. The growth in focus toward enhancing the customer experience is a major factor driving the growth of the recommendation engine market. Moreover, enhancing customer experience is important to achieve customer engagement and retention, thereby achieving higher sales and Return on Investment (RoI). However, designing of targeted campings, as well as relevant product and content recommendations, could help organizations engage more customers.

Hence, analysis of customer data here plays a vital role to understand the customer behavior and preferences. Furthermore, to analyze a large volume of data and automate the manual and tedious process of designing recommendations, enterprises need to design and lay out a plan of action. This could be accomplished by appropriate implementation of AI recommendation engine solutions into their operations.

Further, concerns related to infrastructure compatibility is expected to be a major restraint for the growth of recommendation engine market. As technological compatibility is linked to proper implementation of AI-based recommendation engines, improper implementation could lead to damages in the working mechanism of AI recommendation engine software and solutions.

The hybrid recommendation type is expected to grow at the fastest rate during the forecast period

Based on type, the recommendation engine market, include collaborative filtering, content-based filtering, and hybrid recommendation. The hybrid recommendation type helps various organizations combine 2 different data filtering types to achieve more accurate recommendations. Hence, this contributes to the adoption of hybrid recommendation type in the AI-powered recommendation systems.

The APAC region is expected to witness the highest growth rate during the forecast period

Asia Pacific (APAC) is expected to grow at the highest CAGR in the global recommendation engine market during the forecast period. Moreover, several factors, such as rapid expansion of local enterprises, increase in infrastructure developments, and growth in need to analyze customer data have driven the adoption of recommendation engines across different end-users. The North American region is expected to account for the largest market size during the forecast period. The major driving factors for the market are increase in need to understand the customer behavior and preferences and the need to achieve business insights from a large number of data to formulate various customer engagement strategies.

In the process to determine and verify the market size for several segments and subsegments gathered through secondary research, extensive primary interviews were conducted with key people.

• By Company Type - Tier 1 - 18%, Tier 2 - 47%, and Tier 3 - 35%
• By Designation - C-level - 22%, Director-level - 42%, and Others - 36%
• By Region - North America - 24%, Europe- 48%, APAC - 16%, and MEA - 12%

The major vendors in the global recommendation engine market based on AI, are IBM (US), SAP (Germany), Salesforce (US), HPE (US), Oracle (US), Google (US), Microsoft (US), Intel (US), AWS (US), and Sentient Technologies (US).

• AI recommendation engine software and platform providers
• Venture capitalists and angel investors
• Information Technology (IT) management directors/managers
• Government organizations
• Research organizations
• Consultants/advisory firms
• IT governance directors/managers
• AI system integrators
• Managed Service Providers (MSPs)
• Value-added Resellers (VARs)

Research Coverage

The recommendation engine market powered by AI, has been segmented on the basis of types (collaborative filtering, content-based filtering, and hybrid recommendation), deployment modes, technologies, applications, end-users, and regions. The recommendation solutions help AI recommendation software and platform providers; venture capitalists/angel investors; IT management directors/managers; and BFSI, healthcare, retail, media and entertainment, and government organizations to improve business operations, enhance decision-making, and reduce costs.

The deployment modes in the recommendation engine market are cloud and on-premises. Applications are segmented into personalized campaigns and customer discovery, product planning, strategy and operations planning, and proactive asset management. The technologies involved in the recommendation engine market are context aware and geospatial aware. The end-users segment includes BFSI, retail, healthcare, media and entertainment, transportation, and others (telecom, energy and utilities, manufacturing, and education). On the basis of regions, recommendation engine is segmented into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America.

The report is expected to help the market leaders and new entrants in the recommendation engine market based on AI, in the following ways:

1. The report segments the market into various subsegments, hence it covers the market comprehensively. The report provides the closest approximations of the revenue numbers for the overall market and subsegments. The market numbers are further split into different application areas and regions.

2. The report helps to understand the overall growth of the market. It provides information on the key market drivers, restraints, challenges, and opportunities.

3. The report helps to better understand competitors and gain more insights to strengthen organizations position in the market. In addition, the study presents the positioning of the key players based on their product offerings and business strategies.