The statistic represents to which extent French companies store and use their client data in 2019. The study compared data driven companies who already store their client information and use their data as a mean of transaction growth and non-data driven companies who do not yet orient themselves around client data. From the non-data driven companies, none of them tracked their users responsiveness to e-mail campaigns or other forms of advertisements and webpage visits. Of the data driven companies, 100 percent tracked their client contact information as opposed to 50 percent from the non-data driven companies. Client orders were tracked by 83 percent of the data driven companies compared to 67 percent of the non-data driven ones. The details of the purchased products played to 92 percent an important role for data driven companies who also fully tracked their website visits.

French companies use their client data in order to collect information about them. From the companies included in the sample of this study, 75 percent use AI to get to know their customers better, and 92 percent use data scientists, data miners and data analysts. All of them segment their clients for marketing and 92 percent use scoring to rank their customers.

Dataset to train and test a churn classifier model for a ecommerce company.

China Asset Mgt Business: Number of Product: Subsidiary of Fund Co.: One to One Client data was reported at 1,071.000 Unit in Dec 2024. This records a decrease from the previous number of 1,183.000 Unit for Sep 2024. China Asset Mgt Business: Number of Product: Subsidiary of Fund Co.: One to One Client data is updated quarterly, averaging 3,363.000 Unit from Dec 2014 (Median) to Dec 2024, with 41 observations. The data reached an all-time high of 9,004.000 Unit in Jun 2016 and a record low of 1,071.000 Unit in Dec 2024. China Asset Mgt Business: Number of Product: Subsidiary of Fund Co.: One to One Client data remains active status in CEIC and is reported by Asset Management Association of China. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Asset Management: Business: Number of Product: Quarterly.

subashdvorak/NDIS-Client-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

Surveys of working adults and IT security professionals worldwide conducted in 2021 and 2023 found that the share of organizations experiencing severe consequences due to a successful cyber attack had declined. In 2023, the share of enterprises experiencing a breach of customer or client data was 29 percent, down from 44 percent in 2022. Ransomware infections that occurred through e-mail were common for 32 percent of the respondents in 2023. Cases of a credential or account compromise occurred in 27 percent of the organizations in 2023, a decrease of 25 percent compared to the year prior.

China Asset Mgt Business: Number of Product: Fund Co.: One to One Client data was reported at 4,534.000 Unit in Dec 2024. This records a decrease from the previous number of 4,601.000 Unit for Sep 2024. China Asset Mgt Business: Number of Product: Fund Co.: One to One Client data is updated quarterly, averaging 4,190.000 Unit from Dec 2014 (Median) to Dec 2024, with 41 observations. The data reached an all-time high of 5,074.000 Unit in Jun 2023 and a record low of 1,110.000 Unit in Dec 2014. China Asset Mgt Business: Number of Product: Fund Co.: One to One Client data remains active status in CEIC and is reported by Asset Management Association of China. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Asset Management: Business: Number of Product: Quarterly.

Main components by cluster.

Data Set Information:

The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

There are four datasets: 1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014] 2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs. 3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). 4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). The smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM).

The classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).

Attribute Information:

Input variables:

bank client data:

1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown')

related with the last contact of the current campaign:

8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

other attributes:

12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

social and economic context attributes

16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric)

Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')

Citation Request:

This dataset is public available for research. The details are described in [Moro et al., 2014]. Please include this citation if you plan to use this database:

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

China Asset Mgt Business: Subsidiary of Fund Co.: Yield: One to One Client data was reported at 6.000 % in 2016. This records an increase from the previous number of 5.220 % for 2015. China Asset Mgt Business: Subsidiary of Fund Co.: Yield: One to One Client data is updated yearly, averaging 6.130 % from Dec 2013 (Median) to 2016, with 4 observations. The data reached an all-time high of 6.660 % in 2013 and a record low of 5.220 % in 2015. China Asset Mgt Business: Subsidiary of Fund Co.: Yield: One to One Client data remains active status in CEIC and is reported by Asset Management Association of China. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Asset Management: Business: Yield.

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Bring Your Own Device Market Size 2025-2029

The bring your own device (BYOD) market size is forecast to increase by USD 155.76 billion at a CAGR of 19.3% between 2024 and 2029.

The market is experiencing significant growth, driven by the reduction in hardware costs for enterprises and the increasing adoption in Small and Medium Enterprises (SMEs). This trend is transforming the corporate landscape, enabling employees to use their personal devices for work purposes, thereby enhancing productivity and flexibility. However, the market faces infrastructure constraints and connectivity issues as a challenge. With the proliferation of diverse devices and operating systems, ensuring seamless integration and security becomes a complex task for organizations. Moreover, regulatory hurdles impact adoption, as companies grapple with data privacy and compliance requirements. Another trend is the increasing adoption of BYOD in Small and Medium-sized Enterprises (SMEs), as they seek to remain competitive and provide their employees with the latest digital technology and software
To capitalize on the market opportunities and navigate these challenges effectively, businesses must invest in robust IT infrastructure, implement stringent security policies, and leverage cloud-based solutions for device management and data protection. However, infrastructure constraints and connectivity issues continue to pose challenges, requiring organizations to invest in strong solutions, including digital tools and software, to ensure seamless integration and security of these devices in the corporate network. By addressing these issues, organizations can successfully harness the benefits of the BYOD trend, including cost savings, improved employee satisfaction, and enhanced business agility.

What will be the Size of the Bring Your Own Device (BYOD) Market during the forecast period?

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The market is witnessing significant activity and trends as mobile device usage in businesses continues to expand. Companies are implementing mobile device policies to manage the integration of personal devices into their networks, prioritizing BYOD security solutions to mitigate risks. The mobile device lifecycle, including compliance, health, and performance, is a critical concern for businesses, necessitating the adoption of application usage tracking, mobile device analytics, and biometric authentication. Intellectual property, client data, and confidential business information are all potential targets for cybercriminals.
Mobile device insurance, device upgrades, repair, and training are essential components of effective BYOD program management. Zero trust security, device ownership, and deployment are key considerations for businesses embarking on BYOD initiatives. Mobile device replacement and device support are also vital aspects of maintaining employee productivity and ensuring seamless performance. The use of these devices for accessing essential company data is expected to increase during the forecast period, as more industries embrace digital transformation. The adoption of BYOD solutions in North America is anticipated to continue at a rapid pace due to the convenience and flexibility they offer. Employees can work from anywhere, at any time, using their preferred devices, while businesses can maintain data security and ensure business continuity.

How is this Bring Your Own Device (BYOD) Industry segmented?

The bring your own device (BYOD) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Retail
  Healthcare
  Government
  Energy and utilities
  Others


Deployment

  On-premises
  Cloud


Sector

  Large enterprises
  Small and medium enterprises (SMEs)


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth in the business sector, particularly in industries with a mobile workforce, such as retail. The trend towards a digital workplace and flexible work arrangements has led to an increase in the use of personal devices for work purposes. This shift is driven by the user experience benefits, including increased productivity and employee satisfaction. Retailers, for instance, are leveraging BYOD to create a more interactive in-store experience, collect customer data, and reduce device acquisition costs. BYOD enables employees to use their own devices for work-related tasks, promoting familiarity and saving on hardware deployment cost

Summary of definitions’ descriptive statistics.

In a March 2019 survey, 63 percent of respondents globally said that client data management was an investment area their company was currently focusing on for financial crime prevention. Meanwhile, only 35 percent focused on Blockchain.

In-Memory Database Market size was valued at USD 9.84 Billion in 2024 and is projected to reach USD 35.52 Billion by 2031, growing at a CAGR of 19.20% during the forecast period 2024-2031.

Global In-Memory Database Market Drivers

Demand for Real-Time Analytics: Companies are depending more and more on real-time data to make prompt, well-informed choices. Because they speed up data processing, in-memory databases are crucial for real-time analytics applications. Growth of Big Data and IoT: Large volumes of data are generated by the spread of big data and the Internet of Things (IoT), which must be quickly processed and analyzed. Large data volumes can be handled by in-memory databases more effectively than by conventional disk-based databases. Both Scalability and Performance Requirements: Databases that can scale to accommodate growing data loads without sacrificing performance are essential for growing enterprises. Growing businesses can benefit from the great scalability and performance of in-memory databases. Developments in Memory Technologies: As memory technologies like RAM and flash memory continue to progress, in-memory databases are becoming more widely available and reasonably priced for a greater variety of uses. Quicker Decision-Making Is Required: Businesses must act fast in the current competitive environment in order to stay ahead. Decision-making processes can go more quickly because to in-memory databases' faster data access and processing speeds. Demand for Real-Time Personalization: To improve consumer experiences, real-time personalization is becoming more and more necessary as e-commerce and online services expand in popularity. Large volumes of client data may be instantly analyzed by in-memory databases, allowing them to provide tailored content and recommendations.

China Asset Mgt Business: Scale: Fund Co.: One to One Client data was reported at 3,049.460 RMB bn in Dec 2024. This records an increase from the previous number of 3,037.992 RMB bn for Sep 2024. China Asset Mgt Business: Scale: Fund Co.: One to One Client data is updated quarterly, averaging 3,049.460 RMB bn from Dec 2014 (Median) to Dec 2024, with 41 observations. The data reached an all-time high of 4,314.496 RMB bn in Dec 2017 and a record low of 873.657 RMB bn in Dec 2014. China Asset Mgt Business: Scale: Fund Co.: One to One Client data remains active status in CEIC and is reported by Asset Management Association of China. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Asset Management: Business: Scale: Quarterly.

Financial Sector BPO Market size was valued at USD 116.9 Billion in 2023 and is projected to reach USD 208.1 Billion by 2031, growing at a CAGR of 7.4% during the forecast period 2024-2031.

Global Financial Sector BPO Market Drivers

The market drivers for the Financial Sector BPO Market can be influenced by various factors. These may include:

Growing Need for Economical Solutions: Financial sector organizations are always looking for methods to save operating expenses. Financial institutions can assign non-core tasks, such as customer care and compliance monitoring, to specialist companies through business process outsourcing, or BPO. This strategy improves profitability by cutting labor and overhead expenditures. Financial institutions can also increase their competitive edge by concentrating their resources on more strategic endeavors. The financial sector will continue to use BPO services due to the ongoing economic challenges and the need for affordable solutions, which will enable businesses to function more effectively and react quickly to shifting market conditions.

Technological Progress: Innovations in technology have a significant influence on the Financial Sector BPO business. Advanced analytics, automation, and artificial intelligence (AI) increase productivity and raise the standard of services. Financial BPO providers may improve accuracy, expedite turnaround times, and streamline operations with the help of these technologies. Moreover, cloud computing makes it easier for financial institutions and their BPO partners to collaborate and access data seamlessly, resulting in a more cohesive operating framework. Financial institutions are more inclined to outsource procedures to take advantage of these technological improvements that continue to optimize BPO services, which is fueling the sector's expansion.

Global Financial Sector BPO Market Restraints

Several factors can act as restraints or challenges for the Financial Sector BPO Market. These may include:

Difficulties in Regulatory Compliance: Governmental organizations impose many rules and compliance requirements on the financial sector, which is highly regulated. Businesses that outsource their business processes need to make sure that their BPO partners follow these rules. Repercussions for non-compliance include heavy penalties, legal troubles, and reputational harm to an organization. Complicating matters further and increasing expenses is the changing nature of rules, which calls for ongoing training and updates for BPO employees. When procedures are outsourced, it can be difficult for organizations to manage compliance across jurisdictions, which can reduce operational effectiveness and raise risk management issues.

Privacy and Data Security Issues: Because sensitive financial data is involved, data security is a top priority in the financial sector BPO business. Contracting with outside suppliers raises the possibility of data breaches and illegal access to private data. To safeguard confidential information from online risks, financial institutions must make sure that BPO providers follow strict security procedures. Furthermore, neglecting to protect client data can result in harsh fines from authorities and erode consumer confidence, which will ultimately hurt a company's competitiveness and brand loyalty. Because of this worry, businesses are frequently reluctant to completely adopt BPO solutions, which restricts market expansion.

China Asset Mgt Business: Scale: Subsidiary of Fund Co.: One to Multi Client data was reported at 468.172 RMB bn in Dec 2024. This records an increase from the previous number of 454.006 RMB bn for Sep 2024. China Asset Mgt Business: Scale: Subsidiary of Fund Co.: One to Multi Client data is updated quarterly, averaging 615.523 RMB bn from Dec 2014 (Median) to Dec 2024, with 41 observations. The data reached an all-time high of 2,831.746 RMB bn in Jun 2016 and a record low of 450.580 RMB bn in Mar 2024. China Asset Mgt Business: Scale: Subsidiary of Fund Co.: One to Multi Client data remains active status in CEIC and is reported by Asset Management Association of China. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Asset Management: Business: Scale: Quarterly.

According to Cognitive Market Research, the global Insurance Analytics market size is USD 13.5 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 16.7% from 2024 to 2031. Market Dynamics of Insurance Analytics Market Key Drivers for the Insurance Analytics Market

The growing significance of making decisions based on data is driving the growth of the market.

One important market driver for insurance analytics is the growing importance of data in corporate strategy. Insurance firms that use data-driven insights to inform their decisions have a significant advantage over those that only use conventional techniques. Insurance companies may analyze enormous volumes of data using analytics to gain a deeper understanding of client behavior, preferences, and risk profiles. They can provide more accurate premium rates, more individualized policies, and faster claim processing as a result. Because advanced analytics algorithms can forecast future trends, insurers can proactively modify their plans to take advantage of new opportunities or prepare for impending obstacles. When it comes to spotting possible fraud schemes or figuring out which insurance yields the highest returns, data analytics offers the practical knowledge required to make wise choices. The market for insurance analytics solutions is being further driven by the increasing number of businesses realizing the benefits of being data-driven. The main driver of market growth is the expansion of government legislation requiring insurance coverage in developing nations.

Key Restraints for the Insurance Analytics Market

Fear of data security and privacy breach is a restraint for the market

One of the main obstacles to the insurance analytics market's revenue growth is worries about data security and privacy. Insurance firms handle sensitive client data, such as financial and personal information. A significant obstacle is the shortage of qualified personnel with data analytics experience in the insurance sector. To obtain insightful information, insurance businesses want experts who can efficiently evaluate and comprehend vast amounts of data.

Key Opportunity for the Insurance Analytics Market

The insurance policy market has a significant opportunity in Personalization through Data Analytics.

By leveraging data analytics and AI, insurers can create personalized policies tailored to individual customers' needs and risk profiles. This approach enables insurers to offer more relevant coverage, improve customer satisfaction, and increase policyholder retention. By harnessing data from various sources, insurers can gain a deeper understanding of their customers' behaviors, preferences, and risks. This insight can be used to develop targeted marketing campaigns, optimize policy pricing, and design more effective risk management strategies. Personalization can also help insurers to differentiate themselves in a competitive market, build stronger relationships with policyholders, and ultimately drive business growth. Introduction of the Insurance Analytics Market

The term "insurance analytics" describes the process of utilizing statistical models and data analysis tools to help the insurance business make well-informed decisions. Insurance firms can learn a great deal about their customers' behavior, risk assessment, and claims handling with the use of this strategy. Insurance companies can spot patterns and trends using data analysis that may not be visible using more conventional techniques. The increasing requirement for regulatory compliance primarily drives the global market. In order to guarantee consumer safety, financial stability, and data integrity in the insurance industry, governmental agencies and international organizations are always updating and enacting new legislation. This is pushing insurance businesses to use cutting-edge analytics tools in order to manage risk and guarantee compliance effectively. Consequently, this has a favorable impact on the market.

The Global Custom Software Development Market size is expected to reach $184.63 billion by 2031, rising at a market growth of 22.0% CAGR during the forecast period. Custom software is being used by financial institutions more and more to manage client data securely, expedite key banking activities,

As per Cognitive Market Research's latest published report, the Global Digital Commerce Platform market size was $5.51 Billion in 2022 and it is forecasted to reach $12.80 Billion by 2029. Digital Commerce Platform Industry's Compound Annual Growth Rate will be 13.1% from 2023 to 2030. What is Driving Digital Commerce Platform Market?

Over the last few years, there is a rapid increase in smart device adoption and internet penetration. Smart devices offer flexibility to customers on a smartphone or tablet. With a mobile device, users are simply able to access several food delivery apps and websites. With the introduction of mobile ordering applications, the landscape of the numerous sectors has been completely altered. This drives the market growth.

Despite the numerous benefits that digital commerce provides, data security is one of the primary issues that prevent organizations from using digital commerce solutions. The most typical security and privacy threats include phishing and social engineering, personal or card data theft or misuse, malware, and hacking.

In recent years, business and government bodies have used cloud-based services to meet a wide range of application and infrastructure requirements, including CRM, database, computing, and data storage. According to Gartner, the worldwide cloud computing industry will expand by $266.4 billion by 2020, up from $227.4 billion in 2019. Cloud computing in digital commerce fixes many industry problems, including fluctuating demands and lacklustre user experience. In addition to that, it also benefits the companies losing business due to a lack of mobile-friendly sites. This is expected to provide numerous opportunities for market growth.

Current Trends of the Digital Commerce Platform Market:

Technology is increasingly becoming an important component of a successful online delivery service. It is required at various stages of the order and delivery processes. Several technological advancements, such as artificial intelligence (AI), machine learning (ML), digital transactions, the use of drones for delivery, delivery robots, and others, are making online delivery easier. This will accelerate the growth of digital commerce platforms in the near future. What is Digital Commerce Platform?

Digital commerce is the act of making purchases online without the involvement of a human. When deployed with the appropriate tools, digital commerce can offer priceless customer data. By utilizing client data, it can deliver a more individualized experience across all channels. Customer data enables businesses to increase sales by attracting new clients, cultivating a sense of loyalty among current clients, and more.