October 7, 2020

Personalization Engines Market Size, Rising Demand, Status With Key Players & Forecast To 2026

In this report, our team offers a comprehensive analysis of Personalization Engines market, SWOT analysis of the most prominent players in this landscape. Along with an industrial chain, market statistics in terms of revenue, sales, price, capacity, regional market analysis, segment-wise data, and market forecast information are offered in the full study, etc.

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This report focuses on top manufacturers in global market, Involved the assessment of Sales, price, revenue and market share for each manufacturer, covering
• Acquia
• Adobe
• BloomReach
• Boxever
• Certona
• DynamicYield
• Emarsys
• Episerver
• Evergage
• IgnitionOne
• Monetate
• Oracle
• Qubit
• Reflektion
• RichRelevance
• Strands

On the basis of product, this report displays the Sales, revenue, price, market share and growth rate of each type, primarily split into
• Collaborative filtering
• Content-based filtering
• Hybrid recommendation

By Application, this report focuses on Sales, Market share and Growth Rate of each application, can be divided into
• Manufacturing
• Healthcare
• Media and entertainment
• Transportation
• Others (telecom, energy and utilities, manufacturing, and education)

By Regions, this report splits global market into several key regions, with Sales, Revenue, Price and Gross Margin market share of top players in these regions, from 2014 to 2026 (forecast), like
• China
• Europe
• Japan
• Korea
• India
• Southeast Asia
• South America

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Table of Contents

Global Personalization Engines Market Professional Survey Report 20191 Report Overview
1.1 Definition and Specification
1.2 Manufacturers and Region Overview
1.2.1 Manufacturers Overview
1.2.2 Regions Overview
1.3 Type Overview
1.3.1 Collaborative filtering
1.3.2 Content-based filtering
1.3.3 Hybrid recommendation
1.4 Application Overview
1.4.1 Manufacturing
1.4.2 Healthcare
1.4.3 BFSI
1.4.4 Media and entertainment
1.4.5 Transportation
1.4.6 Others (telecom, energy and utilities, manufacturing, and education)
1.5 Industrial Chain
1.5.1 Personalization Engines Industrial Chain
1.5.2 Upstream
1.5.3 Downstream

2.1 The Overall Market Performance(Volume)
2.1.1 Collaborative filtering
2.1.2 Content-based filtering
2.1.3 Hybrid recommendation
2.2 The Overall Market Performance(Value)
2.2.1 Collaborative filtering
2.2.2 Content-based filtering
2.2.3 Hybrid recommendation

3 Global Personalization Engines Market Assessment by Application
3.1 Overall Market Performance (Volume)
3.2 Manufacturing
3.3 Healthcare
3.4 BFSI
3.5 Media and entertainment
3.6 Transportation
3.7 Others (telecom, energy and utilities, manufacturing, and education)


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