In 2022, the global contact center satisfaction index for the private sector fell to its lowest rating since 2019, whereas the government satisfaction index was at its highest since 2013. Regardless of this, government services had only one point higher on the index score than the private sector in the same year.
A service in decline? Despite still being one of the first points of contact for many public and private services, trends suggest that the call or contact center may be on the decline. In 2021 for example, there were over 100 fewer call centers that were opened or expanded in the United States than there were in 2016. Similarly, the number of jobs created in the call center industry in the Philippines fell dramatically within the same time period, with approximately 55,000 fewer jobs created in 2021 than in 2016. Revenue-wise, it was predicted that activities of call centers in the United Kingdom will bring in a similar annual turnover between 2020 and 2025, as the industry peaked in 2014.
An alternative workforce As numbers of new call centers and employees have been on the decline, the use of artificial intelligence (AI) has been increasing within this work setting. The chatbot market revenue was expected to grow exponentially between 2022 and 2027, and was predicted to create a market value of almost 454 million U.S. dollars in 2027. According to recent studies, the market size of all artificial intelligence was expected to more than double between 2022 and 2024, suggesting an industry on an upward trajectory.

In 2023, Latin America and Central America was the region or country with the highest number of call centers that were opened or expanded in that year. The majority of countries or regions featured in this survey including the United States decreased the number of call centers that were newly opened or expanded since 2020.

As the United States opened less call centers in 2023, the number of contact center employees accordingly decreased between 2020 and 2023.

This statistic shows the market size of call centers in China in 2017 with forecasts for 2020 and 2022. In 2017, call centers market size in China reached approximately 630 billion yuan and was estimated to grow up to almost 803 billion yuan by 2020.

Global Contact Center Software Market size was USD 26.5 Billion in 2022 and is growing to USD 148.1 Billion by 2030 with a CAGR of 24%.

The revenue of outsourced contact centers in Russia was forecast to reach approximately 27 billion Russian rubles in 2022. Compared to the previous year, the figure was expected to decrease by nearly 10 percent. The market size saw a continuous increase between 2015 and 2021.

The Data Center Services Market Report is Segmented by Service (Managed Hosting Service and Colocation Service), Data Center (Tier 1, Tier 2, Tier 3, and Tier 4), End-User Industry (BFSI, Healthcare, Retail, Manufacturing, IT and Telecom, and Other End-User Industries), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for the Above Segments.

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimates (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD program annually collects administrative and fiscal data about all public schools, school districts, and state education agencies in the United States. The data are supplied by state education agency officials and include basic directory and contact information for schools and school districts, as well as characteristics about student demographics, number of teachers, school grade span, and various other administrative conditions. CCD school and agency point locations are derived from reported information about the physical _location of schools and agency administrative offices. The point locations and administrative attributes in this data layer were developed from the 2022-2023 CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. For more information about these CCD attributes, as well as additional attributes not included, see: https://nces.ed.gov/ccd/files.asp. Notes: -1 or M Indicates that the data are missing. -2 or N Indicates that the data are not applicable. -9 Indicates that the data do not meet NCES data quality standards. All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The size of the US Data Center Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.00% during the forecast period.A data center is a facility that keeps computer systems and networking equipment housed, processing, and transmitting data. It represents the infrastructure on which organizations carry out their IT operations and host websites, email servers, and database servers. Data centers, therefore, are imperative to any size business: small start-ups or large enterprise since they enable digital transformation, thus making business applications available.The US data center industry is one of the largest and most developed in the world. The country boasts robust digital infrastructure, abundant energy resources, and a highly skilled workforce, making it an attractive destination for data center operators. Some of the drivers of the US data center market are the growing trend of cloud computing, internet of things (IoT), and high-performance computing requirements.Top-of-the-line technology companies along with cloud service providers set up major data center footprints in the US, mostly in key regions such as Silicon Valley and Northern Virginia, Dallas, for example. These data centers support applications such as e-commerce-a manner of accessing streaming services-whose development depends on its artificial intelligence financial service type. As demand increases concerning data center capacity, therefore, the US data centre industry will continue to prosper as the world's hub for reliable and scalable solutions. Recent developments include: February 2023: The expansion of Souther Telecom to its data center in Atlanta, Georgia, at 345 Courtland Street, was announced by H5 Data Centers, a colocation and wholesale data center operator. One of the top communication service providers in the southeast is Southern Telecom. Customers in Alabama, Georgia, Florida, and Mississippi will receive better service due to the expansion of this low-latency fiber optic network.December 2022: DigitalBridge Group, Inc. and IFM Investors announced completing their previously announced transaction in which funds affiliated with the investment management platform of DigitalBridge and an affiliate of IFM Investors acquired all outstanding common shares of Switch, Inc. for USD approximately USD 11 billion, including the repayment of outstanding debt.October 2022: Three additional data centers in Charlotte, Nashville, and Louisville have been made available to Flexential's cloud customers, according to the supplier of data center colocation, cloud computing, and connectivity. By the end of the year, clients will have access to more than 220MW of hybrid IT capacity spread across 40 data centers in 19 markets, which is well aligned with Flexential's 2022 ambition to add 33MW of new, sustainable data center development projects.. Key drivers for this market are: , High Mobile penetration, Low Tariff, and Mature Regulatory Authority; Successful Privatization and Liberalization Initiatives. Potential restraints include: , Difficulties in Customization According to Business Needs. Notable trends are: OTHER KEY INDUSTRY TRENDS COVERED IN THE REPORT.

Europe had the largest call center market in 2017, generating around 50 billion U.S. dollars in revenue, followed by North America, with 45 billion U.S. dollars. Latin America had the smallest market in that year, with 20 billion U.S. dollars in revenue. Call center market The call center market includes the section of an organization that provides assistance to customers by phone. This can be for existing customers, for example by answering queries about the product or service they purchased, or for sales-based activities to obtain new customers. Given the broad nature of these services, virtually every industry is represented in the call center market, making it a prime candidate for outsourcing. Outsourcing can achieve lower costs through locating call center infrastructure in countries with lower costs, such as India and the Philippines, and significantly reduce the capital expenditure required to set up a call center. This has led to a growing outsourced call center market that is expected to reach 82.6 billion U.S. dollars by 2020. Overall market growth Some analysts expect the overall call center market to experience strong growth in coming years, predicting it will more than double in size by 2022. However, other analysts expect growth to be more limited and unevenly spread. For example, some predict the European market to shrink in size by 2025, while the United States will grow to be the largest market. Data from the last few years seems to support the hypothesis that the U.S. market will overtake Europe, with many more new call centers opening there between 2016 and 2018.

Snapshot img

BFSI Contact Center Analytics Market Size 2024-2028

The BFSI contact center analytics market size is forecast to increase by USD 318 million at a CAGR of 14.9% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. One major trend is the increasing need for financial institutions to enhance their customer relationship management (CRM) systems for effective revenue generation. Another trend is the integration of chatbots to improve turnaround times and enhance customer experience. The integration of advanced technology, such as artificial intelligence and machine learning, enables BFSI contact centers to analyze customer conversations and transactions in real time. However, rising data privacy and security concerns are also posing challenges to market growth. Financial organizations must ensure the security and confidentiality of customer data while implementing analytics solutions. These trends and challenges are shaping the future of the market. By leveraging advanced analytics tools, financial institutions can gain valuable insights from customer interactions, improve operational efficiency, and deliver personalized services to their clients. This will not only help in enhancing customer satisfaction but also in driving revenue growth and competitiveness In the market.

What will be the BFSI Contact Center Analytics Market Size During the Forecast Period?

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The market In the US is witnessing significant growth due to the increasing demand for efficient systems to manage customer interactions and financial risks In the highly regulated financial services industry. With the rise of technology, call centers have evolved from traditional inbound and outbound call handling to customer-centric platforms that leverage digital tools to analyze customer conversations and transactions across various communication channels. Financial institutions, ranging from conglomerates to mid-sized and smaller institutions, are adopting cloud-based phone systems and call center software to improve customer experience, accessibility, and productivity. Remote and distributed teams are increasingly common In the virtual call center landscape, making it crucial for financial institutions to invest in advanced analytics solutions to manage their contact centers effectively and mitigate risks.

How is this BFSI Contact Center Analytics Industry segmented and which is the largest segment?

The BFSI contact center analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud-based
  On-premises


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

The BFSI sector has witnessed significant growth In the adoption of cloud-based contact center solutions over the past five years. This trend is driven by the need for scalability, flexibility, and cost-effectiveness in managing contact centers. BFSI companies are investing in contact center analytics to optimize operations, enhance customer experience, and ensure regulatory compliance. Digital transformation initiatives and the offering of financial advisory services have further increased the demand for advanced contact center analytics. Additionally, by leveraging these solutions, BFSI institutions can gain valuable insights into customer behavior, preferences, and needs, enabling them to provide personalized services and improve overall customer satisfaction. Cloud-based call center software offers a secure and automated environment, allowing for efficient handling of both inbound and outbound calls. This technology is essential for mid-sized and smaller institutions, as well as conglomerates and regulated entities, to stay competitive In the market.

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The cloud-based segment was valued at USD 128.20 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 45% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in North America is expected to dominate the global market due to the increasing adoption of cloud services and the presence of key market players. The use of analytics in BFSI contact ce

latest Data: April - June 2020 .Monthly summary of service requests received, Origin of service request, closed with aging analysis and service notification type of problem. Historic Data: Jan - March 2022; 2021; 2020; 2019; 2018; 2017; 2016.read more

Snapshot img

Voice and Speech Analytics Market Size 2024-2028

The voice and speech analytics market size is projected to increase by USD 3.77 billion at a CAGR of 19.52% between 2023 and 2028.

The global voice and speech analytics market is seeing significant growth, fueled by rising demand for understanding customer sentiment and rapid advancements in AI technology. Businesses are increasingly turning to these tools to interpret consumer behavior shifts, such as the need for personalized experiences, while innovations like machine learning enhance real-time data analysis capabilities.
This report offers a clear view of the market, detailing its current size, growth projections through 2028, and key segments like large enterprises and cloud-based solutions. It provides actionable insights for strategy development, client engagement, and operational improvements, with data on market value and regional trends. A standout trend is the integration of AI for deeper analytics, though a notable challenge remains: the lack of accuracy in interpreting complex speech patterns, which can hinder reliability. For businesses navigating the global landscape, this report delivers practical guidance to tackle evolving trends and address accuracy challenges, helping leaders make informed decisions to maintain a competitive edge in the voice and speech analytics market.

What will be the Size of the Voice and Speech Analytics Market During the Forecast Period?

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Voice and Speech Analytics Market Segmentation

The voice and speech analytics market growth analysis report provides comprehensive data (region wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024 to 2028, as well as historical data from 2018 to 2022 for the following segments.

End-user

  Large enterprise
  Small and medium enterprise


Component

  Solution
  Services


Deployment Model

  On-premise
  Cloud-based
  Hybrid


Application

  Customer Experience Management
  Sales and Marketing
  Risk and Compliance Management
  Emotion Detection
  Call Monitoring and Agent Performance


Geography

  North America

    US
    Canada
    Mexico


  Europe

    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

    Saudi Arabia

By End-user

The market share growth by the large enterprise segment will be significant during the forecast period. The market is driven by the need for Intelligent systems to address Customer concerns and process their Requests efficiently. Artificial intelligence and text analytics are used to identify sentiments, root causes, and positive or negative sentiment scores.

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The large enterprise segment was valued at USD 878.90 million in 2018. Predictive insights from unstructured data enable sales effectiveness, customer churn reduction, and improved call center performance. Cost-effective solutions, such as cloud-based or on-premise deployments, are crucial for large organizations dealing with vast IT infrastructure, data collection, and file sharing. Deep analytics provide valuable customer preferences and competitor insights, while compliance monitoring and agent performance monitoring are essential for third-party risk management. Contact center professionals benefit from these solutions, but must consider the price and availability of Internal resources for deployment analysis. Hence, such factors are expected to drive the segment, which will drive the market during the forecast period.

Conversational analytics solutions are transforming business operations across deployment models and applications. On-premise systems, preferred by organizations like financial institutions, offer robust security and control. Cloud-based solutions, the fastest-growing segment, provide scalability, cost-efficiency, and real-time access, appealing to diverse industries. Hybrid models blend both, delivering flexibility and compliance. In applications, Customer Experience Management leads, enhancing service quality by analyzing call center interactions. Sales and Marketing leverage sentiment tracking for upselling and campaign success, while Risk and Compliance Management ensures adherence to regulations like GDPR and HIPAA. Emotion Detection refines responses by identifying feelings like anger or satisfaction, and Call Monitoring boosts agent performance. SMEs favor scalable cloud options, while large enterprises opt for comprehensive systems.

Regional Analysis

For more insights on the market share of various regions, Download Sample PDF now!

North America is estimated to contribute 42% to the market growth during the forecast year. Technavio's analysts have provided extensive insight into market forecasting, detailing the regi

[226+ Pages Report] The global Data Center Deployment Spending market size is expected to grow from USD 51.2 billion to USD 104.4 billion by 2028, at a CAGR of 12.60% from 2022-2028

About this dataset

Context

The dataset tabulates the population of Royal Center by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Royal Center across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 53.68% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Royal Center is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Royal Center total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Royal Center Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Yates Center by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Yates Center across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 52.15% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Yates Center is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Yates Center total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Yates Center Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Westfield Center by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Westfield Center across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 53.58% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Westfield Center is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Westfield Center total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Westfield Center Population by Race & Ethnicity. You can refer the same here

This data represents calls for police services, which are separated by year due to file size, and updated annually. Historical data is available back to 2006. Available fields include:Event Number – Unique numerical identifier of the call for service.Case Number – If an incident report was written, this is the file number, which can be joined to the incidents and arrests datasets.Call Date – Date the call for service was generated.Call Time – Time the call for service was generated.Source – How the call was generated from the public, or self-initiated by an officer.Priority – The priority level of the call response, with ‘P’ being the highest, followed in order numerically.Nature – The description of the call for service.Address – The block number and street or intersection of the call for service.X – Mapping coordinate of the call for service, projected as NC State Plane (feet).Y – Mapping coordinate of the call for service, projected as NC State Plane (feet).District – The patrol district where the call for service was located.Beat – The patrol beat where the call for service was located, which is a sub-division of the district.Tract – The census tract where the incident occurred, based on 2010 census data.Disposition – How the call for service was closed.Cancelled – Whether the call for service was cancelled prior to officer arrival.

About this dataset

Context

The dataset tabulates the population of Guthrie Center by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Guthrie Center. The dataset can be utilized to understand the population distribution of Guthrie Center by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Guthrie Center. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Guthrie Center.

Key observations

Largest age group (population): Male # 25-29 years (87) | Female # 25-29 years (87). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Guthrie Center population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Guthrie Center is shown in the following column.
• Population (Female): The female population in the Guthrie Center is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Guthrie Center for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Guthrie Center Population by Gender. You can refer the same here

Release Date: 2024-09-26.Key Table Information:.The BDS data tables are compiled from the Longitudinal Business Database (LBD). The LBD is a longitudinal database of business establishments and firms with coverage starting in 1976. The LBD is constructed by linking annual snapshot files from the Census Bureau's Business Register (BR), and incorporating edits to BR data made by the County Business Patterns program. See: About This Program and BDS Methodology for complete information on the coverage, scope, and methodology of the Business Dynamics Statistics data series...Data Items and Other Identifying Records: .This file contains data classified by Establishment age and Employment size of establishments.Number of firms.Number of establishments.Number of employees.(DHS) denominator.Number of establishments born during the last 12 months.Rate of establishments born during the last 12 months.Number of establishments exited during the last 12 months.Rate of establishments exited during the last 12 months.Number of jobs created from expanding and opening establishments during the last 12 months.Number of jobs created from opening establishments during the last 12 months.Number of jobs created from expanding establishments during the last 12 months.Rate of jobs created from opening establishments during the last 12 months.Rate of jobs created from expanding and opening establishments during the last 12 months.Number of jobs lost from contracting and closing establishments during the last 12 months.Number of jobs lost from closing establishments during the last 12 months.Number of jobs lost from contracting establishments during the last 12 months.Rate of jobs lost from closing establishments during the last 12 months.Rate of jobs lost from contracting and closing establishments during the last 12 months.Number of net jobs created from expanding/contracting and opening/closing establishments during the last 12 months.Rate of net jobs created from expanding/contracting and opening/closing establishments during the last 12 months.Rate of reallocation during the last 12 months.Number of firms that exited during the last 12 months.Number of establishments associated with firm deaths during the last 12 months.Number of employees associated with firm deaths during the last 12 months...Geography Coverage:.The data are shown at the U.S. level...Industry Coverage:.The data are shown at the NAICS 00 "Total for all Sectors" level...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/bds/data/BDSEAGEESIZE.zip..API Information:.Business Dynamics Statistics (BDS) data are housed in the Business Dynamics Statistics (BDS) API. For more information, see Business Dynamics Statistics (BDS) Data (census.gov)...Methodology:.In accordance with U.S. Code, Title 13, Section 9, no data are published that would disclose the operations of an individual employer. The BDS has adapted the disclosure avoidance method of the County Business Patterns (CBP) in using Hybrid Balanced Multiplicative Noise Infusion. CBP has been released with noise-infusion since 2007; see the CBP methodology webpage..In addition to noise infusion, cells with fewer than three firms are suppressed with a publication flag 'D'. In addition, cells with identified data quality concerns are suppressed with a publication flag 'S'. Cells that are "structurally missing" or "structurally zero" are indicated with a publication flag of 'X'. Finally, rate cells that cannot be calculated are indicated with a publication flag of 'N'..For more information about BDS methodology, see the BDS methodology pages...Source:.U.S. Census Bureau, 2022 Business Dynamics Statistics..Contact Information:.U.S. Census Bureau.Economy-Wide Statistics Division.Business Dynamics Statistics.Tel: (301) 763 - 6090 .Email: ewd.bds@census.gov

Note: This dataset has been limited to show metrics for Ramsey County, Minnesota.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information: As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS). While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations. Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files. Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Calculation of county-level hospital metrics: County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level. Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hospitals within the selected HSA. Therefore, admissions, capacity, and occupancy are not limited to residents of the selected HSA. For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA. For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.

Metric details: Time period: data for the previous MMWR week (Sunday-Saturday) will update weekly on Thursdays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections. New hospital admissions (count): Total number of admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction New Hospital Admissions Rate Value (Admissions per 100k): Total number of new admissions of patients with laboratory-confirmed COVID-19 in the past week (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000. (Note: This metric is used to determine each county’s COVID-19 Hospital Admissions Level for a given week). New COVID-19 Hospital Admissions Rate Level: qualitative value of new COVID-19 hospital admissions rate level [Low, Medium, High, Insufficient Data] New hospital admissions percent change from prior week: Percent change in the current weekly total new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week. New hospital admissions percent change from prior week level: Qualitative value of percent change in hospital admissions rate from prior week [Substantial decrease, Moderate decrease, Stable, Moderate increase, Substantial increase, Insufficient data] COVID-19 Inpatient Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the in the entire jurisdiction is calculated as an average of valid daily values within the past week (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction. COVID-19 Inpatient Bed Occupancy Level: Qualitative value of inpatient beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data] COVID-19 Inpatient Bed Occupancy percent change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past week, compared with the prior week, in the entire jurisdiction. COVID-19 ICU Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as an average of valid daily values within the past week (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction. COVID-19 ICU Bed Occupancy Level: Qualitative value of ICU beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data] COVID-19 ICU Bed Occupancy percent change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past week, compared with the prior week, in the in the entire jurisdiction. For all metrics, if there are no data in the specified locality for a given week, the metric value is displayed as “insufficient data”.