The Europe Recommendation Engine Market is expected to witness market growth of 31.6% CAGR during the forecast period (2021-2027).
Recommendation engines can rely on the properties of the items that a user likes, which are analyzed to determine what else the user might like; or it can rely on the likes and dislikes of other users. Afterward, the recommendation engine then uses this data to compute a similarity index between users and recommend items. It is also feasible to combine both of these approaches to create an even more powerful recommendation engine. However, just like all other information-related challenges, it is important to select an algorithm that is appropriate for the task.
Another type of recommendation engine is content-based filtering that operates on the assumption that if a user liked one item, they is expected to like similar content or item as well. Algorithms employ cosine and Euclidean distances to calculate the similarity of objects based on a profile of the customer's interests and a description of the item.
In Germany, over three-quarters of Internet users buy goods and services through online mediums. According to the Federal Statistical Office, there are 50 million internet shoppers present in Germany. As per the Bundesverband E-Commerce und Versandhandel Deutschland (German Federal Association of E-Commerce and Mail-Order Trade), gross e-commerce sales increased by 11.4 percent to EUR 65.1 billion in 2018. Owing to this expansion in e-commerce shoppers, several market players is expected to update themselves with new technologies like recommendation engines to serve better.
The Germany market dominated the Europe Recommendation Engine Market by Country 2020, and is expected to continue to be a dominant market till 2027; thereby, achieving a market value of $775.1 million by 2027. The UK market is expected to witness a CAGR of 30.6% during (2021 - 2027). Additionally, The France market is expected to witness a CAGR of 32.5% during (2021 - 2027).
Based on Type, the market is segmented into Collaborative Filtering, Content-based Filtering and Hybrid Recommendation. Based on Application, the market is segmented into Personalized Campaigns & Customer Delivery, Product Planning & Proactive Asset Management and Strategy Operations & Planning. Based on Deployment Type, the market is segmented into Cloud and On-premise. Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises. Based on End Use, the market is segmented into Retail, BFSI, Healthcare, Media & Entertainment, Information Technology and Others. Based on countries, the market is segmented into Germany, UK, France, Russia, Spain, Italy, and Rest of Europe.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Oracle Corporation, Microsoft Corporation, SAP SE, Salesforce.com, Inc., Adobe, Inc., Google LLC, Intel Corporation, Hewlett-Packard Enterprise Company, and Amazon.com, Inc.
Recommendation engines can rely on the properties of the items that a user likes, which are analyzed to determine what else the user might like; or it can rely on the likes and dislikes of other users. Afterward, the recommendation engine then uses this data to compute a similarity index between users and recommend items. It is also feasible to combine both of these approaches to create an even more powerful recommendation engine. However, just like all other information-related challenges, it is important to select an algorithm that is appropriate for the task.
Another type of recommendation engine is content-based filtering that operates on the assumption that if a user liked one item, they is expected to like similar content or item as well. Algorithms employ cosine and Euclidean distances to calculate the similarity of objects based on a profile of the customer's interests and a description of the item.
In Germany, over three-quarters of Internet users buy goods and services through online mediums. According to the Federal Statistical Office, there are 50 million internet shoppers present in Germany. As per the Bundesverband E-Commerce und Versandhandel Deutschland (German Federal Association of E-Commerce and Mail-Order Trade), gross e-commerce sales increased by 11.4 percent to EUR 65.1 billion in 2018. Owing to this expansion in e-commerce shoppers, several market players is expected to update themselves with new technologies like recommendation engines to serve better.
The Germany market dominated the Europe Recommendation Engine Market by Country 2020, and is expected to continue to be a dominant market till 2027; thereby, achieving a market value of $775.1 million by 2027. The UK market is expected to witness a CAGR of 30.6% during (2021 - 2027). Additionally, The France market is expected to witness a CAGR of 32.5% during (2021 - 2027).
Based on Type, the market is segmented into Collaborative Filtering, Content-based Filtering and Hybrid Recommendation. Based on Application, the market is segmented into Personalized Campaigns & Customer Delivery, Product Planning & Proactive Asset Management and Strategy Operations & Planning. Based on Deployment Type, the market is segmented into Cloud and On-premise. Based on Organization Size, the market is segmented into Large Enterprises and Small & Medium Enterprises. Based on End Use, the market is segmented into Retail, BFSI, Healthcare, Media & Entertainment, Information Technology and Others. Based on countries, the market is segmented into Germany, UK, France, Russia, Spain, Italy, and Rest of Europe.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Oracle Corporation, Microsoft Corporation, SAP SE, Salesforce.com, Inc., Adobe, Inc., Google LLC, Intel Corporation, Hewlett-Packard Enterprise Company, and Amazon.com, Inc.
Scope of the Study
Market Segments Covered in the Report:
By Type
- Collaborative Filtering
- Content-based Filtering and
- Hybrid Recommendation
By Application
- Personalized Campaigns & Customer Delivery
- Product Planning & Proactive Asset Management and
- Strategy Operations & Planning
By Deployment Type
- Cloud and
- On-premise
By Organization Size
- Large Enterprises and
- Small & Medium Enterprises
By End Use
- Retail
- BFSI
- Healthcare
- Media & Entertainment
- Information Technology and
- Others
By Country
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
Key Market Players
List of Companies Profiled in the Report:
- IBM Corporation
- Oracle Corporation
- Microsoft Corporation
- SAP SE
- Salesforce.com, Inc.
- Adobe, Inc.
- Google LLC
- Intel Corporation
- Hewlett-Packard Enterprise Company
- Amazon.com, Inc.
Unique Offerings from the Publisher
- Exhaustive coverage
- The highest number of market tables and figures
- Subscription-based model available
- Guaranteed best price
- Assured post sales research support with 10% customization free
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market Overview
Chapter 3. Competition Analysis - Global
Chapter 4. Europe Recommendation Engine Market by Type
Chapter 5. Europe Recommendation Engine Market by Application
Chapter 6. Europe Recommendation Engine Market by Deployment Type
Chapter 7. Europe Recommendation Engine Market by Organization Size
Chapter 8. Europe Recommendation Engine Market by End Use
Chapter 9. Europe Recommendation Engine Market by Country
Chapter 10. Company Profiles
Companies Mentioned
- IBM Corporation
- Oracle Corporation
- Microsoft Corporation
- SAP SE
- Salesforce.com, Inc.
- Adobe, Inc.
- Google LLC
- Intel Corporation
- Hewlett-Packard Enterprise Company
- Amazon.com, Inc.
Methodology
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