Europe Big Data In E-commerce Market Forecast 2020-2028

Europe Big Data In E-commerce Market Forecast 2020-2028

The Europe big data in e-commerce market is set to register a CAGR of 12.13% during the forecasted period, 2020-2028. The factors impacting the growth of the market are the increasing number of big data initiatives, the growing number of internet users, and the rising online sales. The appropriate infrastructure development initiatives by the EU commission for supplementing the big data growth is beneficial for the overall market growth.

The Europe big data in e-commerce market growth analysis is conducted across the countries of Italy, Germany, Russia, Belgium, Poland, the United Kingdom, France, and the rest of Europe. Big data is one of the innovative sectors in Germany, driven by the internet, advertising sectors, and e-commerce. The organizations of the country are focusing on big data technology. Italy is considered to be one of the fastest-growing e-commerce markets in Western Europe. With considerable internet penetration, the country has a huge volume of online shoppers in the country. Several initiatives for digitalization are estimated to propel the adoption of big data, thereby driving the overall market growth. In Russia, the e-commerce sector is booming, and big online retailers are recording considerable revenue growth. The retail companies in the country are implementing big data in offline and online sales to increase the competitiveness of brands. Lenta chain was one of the first retailers to use big data analysis, for monitoring customer information from register receipts.

Some of the well-known companies establishing their presence in the market are Sap SE, Oracle Corporation, Splunk Inc, SAS Institute, Palantir Technologies, etc.

Our report offerings include:
• Explore key findings of the overall market
• Strategic breakdown of market dynamics (Drivers, Restraints, Opportunities, Challenges)
• Market forecasts for a minimum of 9 years, along with 3 years of historical data for all segments, sub-segments, and regions
• Market Segmentation cater to a thorough assessment of key segments with their market estimations
• Geographical Analysis: Assessments of the mentioned regions and country-level segments with their market share
• Key analytics: Porter's Five Forces Analysis, Vendor Landscape, Opportunity Matrix, Key Buying Criteria, etc.
• Competitive landscape is the theoretical explanation of the key companies based on factors, market share, etc.
• Company profiling: A detailed company overview, product/services offered, SCOT analysis, and recent strategic developments

1. Research Scope & Methodology
1.1. Study Objectives
1.2. Scope Of Study
1.3. Methodology
1.4. Assumptions & Limitations
2. Executive Summary
2.1. Market Size & Estimates
2.2. Market Overview
3. Market Dynamics
3.1. Parent Market Analysis: Big Data Market
3.2. Development Of Big Data
3.3. Market Definition
3.4. Key Drivers
3.4.1. Utilization Of Big Data For Improving Sales & Customer Satisfaction
3.4.2. Use Of Big Data By E-commerce Companies To Drive Product Customizations
3.4.3. Growing Inclination Towards Various Online Payment Methods
3.5. Key Restraints
3.5.1. Shortage Of Trained Big Data Experts
3.5.2. Concerns Related To Data Accuracy & Privacy
4. Key Analytics
4.1. Key Investment Insights
4.2. Porter’s Five Force Analysis
4.2.1. Buyer Power
4.2.2. Supplier Power
4.2.3. Substitution
4.2.4. New Entrants
4.2.5. Industry Rivalry
4.3. Opportunity Matrix
4.4. Vendor Landscape
4.5. Value Chain Analysis
5. Market By Component
5.1. Software
5.2. Hardware
6. Market By Deployment Model
6.1. Cloud Based
6.1.1. Private Cloud
6.1.2. Public Cloud
6.2. On-premises
7. Market By Type
7.1. Structured
7.2. Unstructured
7.3. Semi-structured
8. Market By Solution
8.1. Content Analytics
8.2. Customer Analytics
8.3. Fraud Detection
8.4. Risk Management
9. Market By End-user
9.1. Online Classified
9.2. Online Education
9.3. Online Financial
9.3.1. Banking Services/Wallets
9.3.2. Financial Services
9.4. Online Retail
9.5. Online Travel And Leisure
9.6. Other End-users
10. Geographical Analysis
10.1. Europe
10.1.1. United Kingdom
10.1.2. Germany
10.1.3. France
10.1.4. Italy
10.1.5. Russia
10.1.6. Belgium
10.1.7. Poland
10.1.8. Rest Of Europe
11. Company Profiles
11.1. Amazon Web Services Inc
11.2. Cloudera Inc
11.3. Data Usa
11.4. Dell Inc
11.5. Guavus Inc
11.6. Hewlett Packard Enterprise Company
11.7. Hitachi Ltd
11.8. International Business Machines Corporation (Ibm)
11.9. Microsoft Corporation
11.10. Oracle Corporation
11.11. Palantir Technologies
11.12. Sap Se
11.13. Sas Institute
11.14. Splunk Inc
11.15. Teradata Corporation

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