Global Hyper-Personalisation Banking market Insights 2024, Analysis and Forecast to 2031
- Published Date : 2024-05-31
- Pages : 100
- Report Id : 40400
- Categories : Services
This report describes the global market size of Hyper-Personalisation Banking from 2019 to 2022 and its CAGR from 2019 to 2023, and also forecasts its market size to the end of 2031 and its expected to grow with a CAGR of 15.1% from 2024 to 2031.
Due to the COVID-19 pandemic and Russia-Ukraine War Influence, the global market for Hyper-Personalisation Banking estimated at US 2.1$ billion in the year 2022, is projected to reach a revised size of US$ million by 2031, growing at a CAGR of 12% during the forecast period 2024-2031. North American market for Hyper-Personalisation Banking is estimated to increase from $500 million in 2023 to reach $1,416.67 million by 2031, at a CAGR of % during the forecast period of 2024 through 2031.
Hyper-personalization in banking refers to the practice of leveraging advanced data analytics and technology to provide highly tailored financial products, services, and experiences to individual customers. It involves understanding each customer's unique preferences, behaviors, and needs to deliver personalized recommendations, pricing,and communication strategies in real-time.
For geography segment, regional supply, demand, major players, price is presented from 2019 to 2031. This report cover following regions:
North America
Asia-Pacific
Europe
Middle East and Africa
South America
The key countries for each regions are also included such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.
For competitor segment, the report include global key players of Hyper-Personalisation Banking as well as some small players. The information for each competitor include:
Company Profile
Main Business Information
SWOT Analysis
Sales Volume, Revenue, Price and Gross Margin
Market Share
By Component
Solutions
Customer Analytics
Predictive Analytics
Personalization Engines
Data Management Platforms
Services
Professional Services
Managed Services
In 2023, Hyper-Personalization Banking solutions encompass a suite of components designed to enhance customer experiences and optimize financial services.
Customer Analytics enables banks to gather insights into customer behavior and preferences, while Predictive Analytics utilizes data to forecast future trends and anticipate customer needs. Personalization Engines leverage advanced algorithms to tailor banking services and offerings to individual customers, fostering deeper engagement and loyalty. Data Management Platforms provide the infrastructure necessary to collect, store, and analyze vast amounts of customer data securely. Additionally, Professional Services and Managed Services offer support for the implementation, optimization, and ongoing management of hyper-personalized banking solutions, ensuring seamless integration and maximum efficiency.
Deployment Mode
On-premises
Cloud-based
In 2023, the realm of Hyper-Personalization Banking, deployment modes play a crucial role in determining accessibility, scalability, and security. On-premises deployment involves hosting software and infrastructure within the bank's premises, allowing for greater control over data and customization but requiring significant upfront investment and maintenance. Alternatively, cloud-based deployment offers flexibility, scalability, and cost-efficiency by leveraging cloud infrastructure, enabling banks to access services remotely, scale resources as needed, and benefit from continuous updates and enhancements while ensuring data security and compliance with regulatory standards.
Applications
Customer Experience Management
Personalized Customer Interaction
Contextualized Marketing
Fraud Detection and Prevention
Real-time Risk Analysis
Sales and Marketing Optimization
Cross-selling and Up-selling
Product and Service Personalization
Tailored Financial Products
In 2023, Hyper-Personalisation Banking, applications are diverse, aiming to enhance every aspect of the customer journey. From Customer Experience Management to Personalized Customer Interaction, banks utilize data-driven insights to deliver tailored experiences, ensuring customer satisfaction and loyalty. Contextualized Marketing enables targeted campaigns, while Fraud Detection and Prevention, Real-time Risk Analysis, and Sales and Marketing Optimization leverage advanced analytics to mitigate risks and maximize opportunities, ultimately leading to improved cross-selling, up-selling, and the delivery of tailored financial products that meet individual customer needs and preferences
End-user-
Retail Banking
Corporate Banking
Investment Banking
In 2023, the Hyper-Personalisation Banking market, end-users span across various sectors, including Retail Banking, Corporate Banking, and Investment Banking.
Retail Banking customers benefit from tailored financial solutions, personalized recommendations, and seamless digital experiences that enhance satisfaction and loyalty. Corporate Banking clients receive customized services, real-time insights, and efficient transaction management, while Investment Banking professionals leverage hyper-personalized analytics and advisory services to make informed investment decisions and optimize portfolio performance. Overall, Hyper-Personalisation Banking caters to the diverse needs of different end-users, empowering them with personalized financial solutions and superior user experiences.
By Technology
Artificial Intelligence (AI)
Machine Learning (ML)
Big Data Analytics
Blockchain
Robotic Process Automation (RPA)
In 2023, the Hyper-Personalisation Banking market, cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Big Data Analytics drive innovation. These technologies enable banks to analyze vast amounts of customer data, predict behaviors, and deliver personalized financial services and recommendations. Additionally, Blockchain enhances security and transparency in transactions, while Robotic Process Automation (RPA) streamlines operational processes, ensuring efficiency and accuracy in delivering hyper-personalized banking experiences.
Bank Size-
Large Banks
Medium-sized Banks
Small Banks
In 2023,the Hyper-Personalisation Banking market, Robotic Process Automation (RPA) offers transformative benefits across banks of all sizes. Large banks leverage RPA to automate routine tasks, streamline operations, and enhance scalability, allowing them to deliver hyper-personalized services efficiently at scale. Medium-sized and small banks adopt RPA to optimize resource utilization, improve operational efficiency, and remain competitive in providing personalized banking experiences tailored to their customer base, driving customer satisfaction and loyalty.
Channel-
Online Banking
Mobile Banking
Branch Banking
ATM
Others
In the Hyper-Personalisation Banking market, channels play a pivotal role in delivering tailored experiences to customers. Online Banking and Mobile Banking channels leverage data analytics to provide personalized services, such as customized dashboards and targeted offers, accessible anytime, anywhere. Branch Banking and ATM channels integrate hyper-personalized services seamlessly into physical interactions, offering personalized assistance and recommendations to customers in real-time. Additionally, emerging channels such as chatbots and voice assistants further enhance the hyper-personalization journey, providing convenient access to personalized banking services through innovative and intuitive interfaces.
Company-
IBM Corporation
Accenture
Capgemini
Oracle Corporation
Salesforce
Microsoft Corporation
Infosys
Adobe Systems
SAP SE
Please ask for sample pages for full companies list
Base Year: 2023
Historical Data: from 2019 to 2023
Forecast Data: from 2024 to 2031
Any special requirements about this report, please let us know and we can provide custom report."
TABLE OF CONTENTS
Market Taxonomy
1. Executive Summary
1.1. Market Overview
1.2. Market Analysis and Recommendations
1.3. RA Analysis and Recommendations
2. Market Introduction
2.1. Market Definition
3. Market Background
3.1. Parent/Associated Market Overview
3.1.1.Hyper-Personalisation Banking Outlook
3.1.2.Hyper-Personalisation Banking Overview
3.2. Effect of Covid-19 Impact
3.3. Effect of Russia-Ukraine Impact
3.4. Macro-Economic Overview
3.4.1. GDP Growth
3.4.2. Retail Industry Growth
3.5. Pricing Analysis
3.6. Challenges related to adoption of Hyper-Personalisation Banking
3.7. Regulations ofHyper-Personalisation Banking
3.8. Forecast Factors: Relevance and Impact
3.9. Market Dynamics
3.9.1. Drivers
3.9.2. Restraints
3.9.3. Opportunity
3.9.4. Trends
3.9.5. SWOT Analysis
4. Market Forecast
4.1. Market Value Projections
4.2. Market Size Projections
4.3. Y-o-Y Projections
4.4. Absolute Opportunity Analysis
5. GlobalHyper-Personalisation BankingValue Chain Analysis
5.1.Hyper-Personalisation BankingValue Chain
5.1.1. List of Raw Material Suppliers
5.1.2. List ofHyper-Personalisation BankingManufacturer
5.1.3. List of Segment
6. GlobalHyper-Personalisation Banking Analysis By Component
6.1. Introduction
6.1.1. Market Value Share Analysis By Component
6.1.2. Y-o-Y Growth Analysis By Component
6.2. Historical Market Size (USD Mn) Analysis 2018-2022 By Component
6.3. Market Attractiveness Analysis By Component
7. Global Hyper-Personalisation Banking Analysis By By DeploymentMode
7.1. Introduction
7.1.1. Market Value Share Analysis By Deployment Mode
7.1.2. Y-o-Y Growth Analysis By DeploymentMode
7.2. Historical Market Size (USD Mn) Analysis 2018-2022 By Deployment Mode
7.3. Market Attractiveness Analysis By DeploymentMode
8. Market Structure Analysis
8.1. Market Analysis by Tier of Companies
8.1.1. By Large, Medium, and Small
8.2. Market Concentration
8.2.1. By Top 5 and by Top 10
8.3. Type ion Capacity Share Analysis
8.3.1. By Large, Medium, and Small
8.3.2. By Top 5 and Top 10
8.4. Technology Roadmap
9. Competition Analysis
9.1. Competition Dashboard
9.2. Company Profiles (15 Companies)
9.2.1.IBM Corporation
9.2.1.1. Overview
9.2.1.2. Type Portfolio
9.2.1.3. Financial Overview
9.2.2. Davidson's Organics
9.2.2.1. Accenture
9.2.2.2. Type Portfolio
9.2.2.3. Financial Overview
9.2.3. Capgemini
9.2.3.1. Overview
9.2.3.2. Type Portfolio
9.2.3.3. Financial Overview
9.2.4. Oracle Corporation
9.2.4.1. Overview
9.2.4.2. Type Portfolio
9.2.4.3. Financial Overview
9.2.5. Salesforce
9.2.5.1. Overview
9.2.5.2. Type Portfolio
9.2.5.3. Financial Overview
9.2.6. Microsoft Corporation
9.2.6.1. Overview
9.2.6.2. Type Portfolio
9.2.6.3. Financial Overview
9.2.7. Infosys
9.2.7.1. Overview
9.2.7.2. Type Portfolio
9.2.7.3. Financial Overview
9.2.8. Adobe Systems
9.2.8.1. Overview
9.2.8.2. Type Portfolio
9.2.8.3. Financial Overview
9.2.9. SAP SE
9.2.9.1. Overview
9.2.9.2. Type Portfolio
9.2.9.3. Financial Overview
9.2.10. COMPANY10
9.2.10.1. Overview
9.2.10.2. Type Portfolio
9.2.10.3. Financial Overview
9.2.11. COMPANY11
9.2.11.1. Overview
9.2.11.2. Type Portfolio
9.2.11.3. Financial Overview
9.2.12. COMPANY12
9.2.12.1. Overview
9.2.12.2. Type Portfolio
9.2.12.3. Financial Overview
9.2.13. COMPANY13
9.2.13.1. Overview
9.2.13.2. Type Portfolio
9.2.13.3. Financial Overview
9.2.14. COMPANY14
9.2.14.1. Overview
9.2.14.2. Type Portfolio
9.2.14.3. Financial Overview
9.2.15. COMPANY15
9.2.15.1. Overview
9.2.15.2. Type Portfolio
9.2.15.3. Financial Overview
10. Global Hyper-Personalisation Banking Analysis By Region
10.1. Introduction
10.1.1. Market Value Share Analysis By Region
10.1.2. Y-o-Y Growth Analysis By Region
10.2. Historical Market Size (USD Mn) Analysis 2018-2022 By Region
10.2.1. North America
10.2.2. Middle East and Africa
10.2.3. South America
10.2.4. Asia-Pacific
10.2.5. Others
10.3. Current Market Size (USD Mn) Forecast 2023-2030 By Region
10.3.1. North America
10.3.2. Middle East and Africa
10.3.3. South America
10.3.4. Asia-Pacific
10.3.5. Others
10.4. Market Attractiveness Analysis By Region
11. North America Hyper-Personalisation Banking Analysis
11.1. Introduction
11.2. Historical Market Size (USD Mn) Analysis 2018-2022 By Country
11.2.1. U.S.
11.2.2. Canada
11.2.3. Mexico
11.2.4. Rest of North America
11.3. Current Market Size (USD Mn) Forecast 2023-2030 By Country
11.3.1. U.S.
11.3.2. Canada
11.3.3. Mexico
11.3.4. Rest of North America
11.4. Historical Market Size (USD Mn) Analysis 2018-2022 By Component
11.5. Current Market Size (USD Mn) Forecast 2023-2030 By Component
11.6. Historical Market Size (USD Mn) Analysis 2018-2022 By DeploymentMode
11.7. Current Market Size (USD Mn) Forecast 2023-2030 By DeploymentMode
12. South America Hyper-Personalisation Banking Analysis
12.1. Introduction
12.2. Regional Pricing Analysis
12.3. Historical Market Size (USD Mn) Analysis 2018-2022 By Country
12.3.1. Brazil
12.3.2. Argentina
12.3.3. Rest of South America
12.4. Current Market Size (USD Mn) Forecast 2023-2030 By Country
12.4.1. Brazil
12.4.2. Argentina
12.4.3. Rest of South America
12.5. Historical Market Size (USD Mn) Analysis 2018-2022 By Component
12.6. Current Market Size (USD Mn) Forecast 2023-2030 By Component
12.7. Historical Market Size (USD Mn) Analysis 2018-2022 By DeploymentMode
12.8. Current Market Size (USD Mn) Forecast 2023-2030 By DeploymentMode
13. Europe Hyper-Personalisation Banking Analysis
13.1. Introduction
13.2. Regional Pricing Analysis
13.3. Historical Market Size (USD Mn) Analysis 2018-2022 By Country
13.3.1. Germany
13.3.2. Italy
13.3.3. France
13.3.4. Spain
13.3.5. U.K.
13.3.6. Rest of Europe
13.4. Current Market Size (USD Mn) Forecast 2023-2030 By Country
13.4.1. Germany
13.4.2. Italy
13.4.3. France
13.4.4. Spain
13.4.5. U.K.
13.4.6. Rest of Europe
13.5. Historical Market Size (USD Mn) Analysis 2018-2022 By Component
13.6. Current Market Size (USD Mn) Forecast 2023-2030 By Component
13.7. Historical Market Size (USD Mn) Analysis 2018-2022 By DeploymentMode
13.8. Current Market Size (USD Mn) Forecast 2023-2030 By DeploymentMode
14. Middle East and Africa Hyper-Personalisation Banking Analysis
14.1. Introduction
14.2. Regional Pricing Analysis
14.3. Historical Market Size (USD Mn) Analysis 2018-2022 By Country
14.3.1. Saudi Arabia
14.3.2. U.A.E.
14.3.3. South Africa
14.3.4. Turkey
14.3.5. Rest of Middle East and Africa
14.4. Current Market Size (USD Mn) Forecast 2023-2030 By Country
14.4.1. Saudi Arabia
14.4.2. U.A.E.
14.4.3. South Africa
14.4.4. Turkey
14.4.5. Rest of Middle East and Africa
15. Asia-Pacific Hyper-Personalisation Banking Analysis
15.1. Introduction
15.2. Regional Pricing Analysis
15.3. Historical Market Size (USD Mn) Analysis 2018-2022 By Country
15.3.1. China
15.3.2. India
15.3.3. Japan
15.3.4. South Korea
15.3.5. Singapore
15.3.6. Australia and New Zealand
15.3.7. Rest of Asia-Pacific
15.4. Current Market Size (USD Mn) Forecast 2023-2030 By Country
15.4.1. China
15.4.2. India
15.4.3. Japan
15.4.4. South Korea
15.4.5. Singapore
15.4.6. Australia and New Zealand
15.4.7. Rest of Asia-Pacific
i. Research Methodology
ii. Assumptions & Acronyms
LIST OF FIGURES
Figure 01: Global Hyper-Personalisation Banking, BPS Analysis, By Component, 2018(H), 2023(E) & 2030(F)
Figure 02: Global Hyper-Personalisation Banking, Y-O-Y Growth, By Component, 2020(A) - 2030(F)
Figure 03: Global Hyper-Personalisation Banking Attractiveness Analysis, By Component (2020)
Figure 04: Global Hyper-Personalisation Banking, BPS Analysis, By Deployment Mode, 2018(H), 2023(E) & 2030(F)
Figure 05: Global Hyper-Personalisation Banking, Y-O-Y Growth, By Deployment Mode, 2018(H) - 2030(F)
Figure 06: Global Hyper-Personalisation Banking Attractiveness Analysis, By DeploymentMode
Figure 07: Global Hyper-Personalisation Banking, Value Analysis, By Component, 2018(H)-2030(F)
Figure 08: Global Hyper-Personalisation Banking, Value Analysis, by Deployment Mode, 2018(H)-2030(F)
Figure 09: Global Hyper-Personalisation Banking, BPS Analysis, by Region, 2018(H), 2023(E) & 2030(F)
Figure 10: Global Hyper-Personalisation Banking, Y-O-Y Growth, by Region, 2018(H) - 2030(F)
Figure 11: Global Hyper-Personalisation Banking Attractiveness Analysis, by Region
Figure 12: North America Hyper-Personalisation Banking Value (USD Mn Forecast, 2018 – 2022
Figure 13: North America Hyper-Personalisation Banking Value (USD Mn Forecast, 2023 – 2030
Figure 14: South America Hyper-Personalisation Banking Value (USD Mn) Forecast, 2018 – 2022
Figure 15: South America Hyper-Personalisation Banking Value (USD Mn) Forecast, 2023 – 2030
Figure 16: Europe Hyper-Personalisation Banking Value (USD Mn) Forecast, 2018 – 2022
Figure 17: Europe Hyper-Personalisation Banking Value (USD Mn) Forecast, 2023 – 2030
Figure 18: Middle East and Africa Hyper-Personalisation Banking Value (USD Mn) Forecast, 2018 – 2022
LIST OF TABLES
Table 01: Global Hyper-Personalisation Banking Value (USD Mn) Forecast, By Component, 2018(H) – 2030(F)
Table 02: Global Hyper-Personalisation Banking Value (USD Mn) Forecast, By DeploymentMode, 2018(H) – 2030(F)
Table 03: Global Hyper-Personalisation Banking Value (USD Mn) Forecast, by Country, 2018(H) – 2030(F)
Table 04: North America Hyper-Personalisation Banking Value (USD Mn) Forecast, by Country, 2018(H) – 2030(F)
Table 05: North America Hyper-Personalisation Banking Value (USD Mn) Forecast, By Component, 2018(H) – 2030(F)
Table 06: North America Hyper-Personalisation Banking Value (USD Mn) Forecast, By DeploymentMode, 2018(H) – 2030(F)
Table 07: South America Hyper-Personalisation Banking Value (USD Mn) Forecast, by Country, 2018(H) – 2030(F)
Table 08: South America Hyper-Personalisation Banking Value (USD Mn) Forecast, By Component, 2018(H) – 2030(F)
Table 09: South America Hyper-Personalisation Banking Value (USD Mn) Forecast, By DeploymentMode, 2018(H) – 2030(F)
Table 10: Europe Hyper-Personalisation Banking Value (USD Mn) and Forecast, by Country, 2018(H) – 2030(F)
Table 11: Europe Hyper-Personalisation Banking Value (USD Mn) Forecast, By Component, 2018(H) – 2030(F)
Table 12: Europe Hyper-Personalisation Banking Value (USD Mn) Forecast, By DeploymentMode EndUser, 2018(H) – 2030(F)
Table 13: Middle East and Africa Hyper-Personalisation Banking Value (USD Mn) Forecast, by Country, 2018(H) – 2030(F)
Table 14: Middle East and Africa Hyper-Personalisation Banking Value (USD Mn) Forecast, By Component, 2018(H) – 2030(F)
Table 15: Middle East and Africa Hyper-Personalisation Banking Value (USD Mn) Forecast, By DeploymentMode, 2018(H) – 2030(F)
Table 16: Asia-Pacific Hyper-Personalisation Banking Value (USD Mn) Forecast, by Country, 2018(H) – 2030(F)
Table 17: Asia-Pacific Hyper-Personalisation Banking Value (USD Mn) Forecast, By Component, 2018(H) – 2030(F)
Table 18: Asia-Pacific Hyper-Personalisation Banking Value (USD Mn) Forecast, By DeploymentMode, 2018(H) – 2030(F)
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