According to a survey conducted among marketers in the United States in March 2022, ** percent of respondents said they used intent data to deliver targeted ad content, while ** percent said they used it for email marketing. This was followed by ** percent of respondents stating they used intent data for marketing personalization.

Seventy percent of surveyed business-to-business (B2B) technology vendors use intent data for prospecting; in other words, for identifying potential customers. Furthermore, ** percent use it to support sales enablement and ** percent for targeting high-intent accounts.

As of 2020, ** percent of surveyed business-to-business (B2B) technology vendors work with an intent data provider, and ** percent said they did not. Intent data providers collect data about business users' online behaviour that provides insight into their purchase intent.

The Buyer Intent Tools market is experiencing a remarkable transformation, characterized by rapid growth and evolving technologies that cater to the needs of both businesses and consumers. These tools are designed to analyze potential buyers' behaviors, preferences, and engagement levels, enabling organizations to t

The Buyer Intent Software market is increasingly becoming a focal point for businesses looking to understand and harness consumer behavior in an ever-evolving digital landscape. This innovative category of software is designed to analyze online user activity and determine potential purchasing intentions, allowing or

The Intent-Based Networking (IBN) market is rapidly evolving, reflecting the increasing complexity and demands of modern networking environments. IBN streamlines network operations by translating business intentions into network configurations, facilitating a more proactive and automated approach to network manageme

The intent based networking market is projected to be valued at $2.1 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 15%, reaching approximately $8.5 billion by 2034.

During a 2023 survey carried out among media strategists, planners and buyers from North America who worked on programmatic campaigns, it was found that behavioral and interest/intent data were third-party data types used most in digital advertising campaigns, both named by ** percent of respondents. Demo and lifestyle data followed, mentioned by ** and ** percent, respectively.

The Buyers Intent Software market is experiencing a significant evolution, driven by the increasing demand for data-driven decision-making in various industries. This software focuses on understanding and interpreting buyer behavior, enabling businesses to tailor their marketing strategies effectively. By leveraging

The data is from the article "Privacy Signals: Exploring the Relationship Between Cookies and Online Purchase Intention" and shows consumer perceptions of website transparency about cookie requests in the e-commerce environment. Overall, we used a quantitative methodology, through a descriptive study and four experimental studies. The results studies show that cookie acceptance positively influences the intention to purchase, only when the consumer accepts cookie collection and when they have a need for the product, resulting in greater perception of benefits associated with information disclosure. Risks did not show significance in this process. However, providing more information to consumers about data collection is advantageous because the intention to purchase is higher, even for those who do not accept cookies. The data for the experiments were collected using Qualtrics software and online, using Facebook, through sponsored ads to ensure the randomness of responses. The products used in the experiment scenarios were chosen because of the pandemic context, where the consumption of products used in the home increased, to the detriment of superfluous or luxury products. The samples of the experiments respected the minimum criteria of 30 people in each experiment condition, as suggested by Hair et al. (2009). Statistical analyses were done through IBM SPSS Statistics statistical software using the Procces macro, which is an extension created for SPSS for multivariate data analysis and mediation analysis, as well as integrated conditional process models (Hayes, 2018). Finally, we also used General Linear Model (GLM) in the analyses, as it is an extension of the linear regression model and indicated in cases of probability distributions other than the normal distribution, which makes it more flexible to handle the data (Hair et al. 2009).

The Australian Organ Donor Register (AODR) is the national register for people to record their decision about becoming an organ and tissue donor. People must be 16 years or older to register.

The AODR is administered by the Australian Government Department of Human Services. Read more about the Australian Organ Donor Register on the Department of Human Services website.

AODR statistics are published monthly. The statistics provide the number of people who have registered their decision in regards to organ and tissue donation on the AODR by age group, state and gender. These statistics are further provided as a percentage of the Australian population using ABS Statistics.

Two reports are provided:

Consent registrations: A legally valid consent registration refers to people who have registered by a signed consent form. Only people aged 18 years and over can register their legally valid consent on the Australian Organ Donor Register. Those aged 16-17 years can still register their intention to donate.

Intent registrations: Intent registrations refers to people or have registered their intent to donate or their objection online or by phone, they have not signed a consent form.

Data is provided in the following formats:

Excel/ XLS/ XLSX: The human readable data for the current year (2016) is provided in an individual excel file for consent registrations and an individual excel file for intent registrations. Historical registration data (2007-2015) may be found in the relevant zipped excel files.

CSV: Due to the volume of data, the current (2016) and historical (2007-2015) machine readable files for consent registrations and intent registrations may be found in the relevant zipped files.

If you require statistics at a more detailed level, please contact statistics@humanservices.gov.au detailing your request. The Department of Human Services charges on a cost recovery basis for providing more detailed statistics and their provision is subject to privacy considerations.

Disclaimer:

This data is provided by the Department of Human Services (Human Services) for general information purposes only. While Human Services has taken care to ensure the information is as correct and accurate as possible, we do not guarantee, or accept legal liability whatsoever arising from, or connected to its use.

We recommend that users exercise their own skill and care with respect to the use of this data and that users carefully evaluate the accuracy, currency, completeness and relevance of the data for their needs.

On December 20 2021, all estimates and standard errors for 2017–2018 were revised in this table to correct programming errors. Data on initial injury-related visits to hospital emergency departments, by sex, age, and intent and mechanism of injury. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. Due to a change in national medical data coding standards in 2015, from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) to the ICD-10-CM, the definition for injuries and injury subcategories changed for the 2017 reporting period and beyond. Results from 2017 and subsequent years should not be compared with previous reporting periods. Any observed changes in trends across this transition period should not be considered. Data for 2016 are not included. Additional information regarding injury definitions and categorization of injuries by mechanism and intent of injury is available at: https://www.cdc.gov/nchs/injury/injury_tools.htm. Note that the data file available here has more recent years of data than what is shown in the PDF or Excel version. SOURCE: NCHS, National Hospital Ambulatory Medical Care Survey. For more information on the National Hospital Ambulatory Medical Care Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus17_appendix.pdf.

From July 2016 to May 2020, there were 27 intentional data breaches that revealed academic records and personally identifiable identification (PII) with the responsible actors being students. The most common reason for students to commit data breaches was in order to change grades.

This dataset describes injury mortality in the United States beginning in 1999. Two concepts are included in the circumstances of an injury death: intent of injury and mechanism of injury. Intent of injury describes whether the injury was inflicted purposefully (intentional injury) and, if purposeful, whether the injury was self-inflicted (suicide or self-harm) or inflicted by another person (homicide). Injuries that were not purposefully inflicted are considered unintentional (accidental) injuries. Mechanism of injury describes the source of the energy transfer that resulted in physical or physiological harm to the body. Examples of mechanisms of injury include falls, motor vehicle traffic crashes, burns, poisonings, and drownings (1,2). Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia. Age-adjusted death rates (per 100,000 standard population) are based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2015 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of injury death are classified by the International Classification of Diseases, Tenth Revision (ICD–10). Categories of injury intent and injury mechanism generally follow the categories in the external-cause-of-injury mortality matrix (1,2). Cause-of-death statistics are based on the underlying cause of death. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics. ICD–10: External cause of injury mortality matrix. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf. Miniño AM, Anderson RN, Fingerhut LA, Boudreault MA, Warner M. Deaths: Injuries, 2002. National vital statistics reports; vol 54 no 10. Hyattsville, MD: National Center for Health Statistics. 2006.

Colombia No. of Deaths: Caused by: Events of Undetermined Intent & Aftermath data was reported at 554.000 Person in Sep 2024. This records an increase from the previous number of 503.000 Person for Jun 2024. Colombia No. of Deaths: Caused by: Events of Undetermined Intent & Aftermath data is updated quarterly, averaging 533.500 Person from Mar 2017 (Median) to Sep 2024, with 30 observations. The data reached an all-time high of 723.000 Person in Mar 2019 and a record low of 187.000 Person in Sep 2017. Colombia No. of Deaths: Caused by: Events of Undetermined Intent & Aftermath data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G012: Number of Deaths: Cause of Death.

From October to November 2024, approximately **** percent of search queries on Google were navigational, when users seek specific websites. Alternatively, more than ** percent of the intent on ChatGPT was informational, when users look for answers or data. On the other hand, the percentage of transactional and commercial queries stayed practically the same on both platforms.

SVS267 - Those who experienced stalking with fear of sexual violence by whether they ever disclosed a stalking experience (% of persons aged 18 years and over who experienced stalking with intent of sexual violence in the previous 12 months). Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Those who experienced stalking with fear of sexual violence by whether they ever disclosed a stalking experience (% of persons aged 18 years and over who experienced stalking with intent of sexual violence in the previous 12 months)...

Data on initial injury-related visits to hospital emergency departments in the United States, by sex, age, and intent and mechanism of injury. Data are from Health, United States. SOURCE: National Center for Health Statistics, National Hospital Ambulatory Medical Care Survey. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

Descriptive statistics on the intent to use symptom checkers.

This dataset presents the footprint of the rates of family violence patients retrieved from the Victorian Emergency Minimum Dataset (VEMD), which holds information detailing presentations at Victorian public hospitals with designated Emergency Departments. The data spans the financial years in the period of July 2013 to June 2018 and is aggregated to 2011 Australian Statistical Geography Standard (ASGS) Local Government Areas (LGA).

The Victorian Family Violence Database is a repository for a range of different datasets relating to family violence clients and service use, extracted from the data holdings of a variety of government agencies.

The rate of patients per 100,000 population is calculated using the count of patients recorded in a LGA and the Estimated Resident Population (ERP) of that LGA. The rate is calculated using the following formula: VEMD Patient Rate = (Number of Patients/ERP count) x 100,000. ERPs are based on populations provided by the Australian Bureau of Statistics (ABS).

For further information about this dataset and related statistics, visit the data source:Crime Statistics Australia.

Please note:

• AURIN has spatially enabled the original data.

• To maintain confidentiality, person-based counts with a value of 3 or less are given a value of 2 to calculate totals.

• For the financial years from July 2013 to June 2016, patients presenting for family violence reasons were identified with the human intent injuries of 'Maltreatment, assault by domestic partner' or 'Child neglect/maltreatment by parent or guardian'.

• For the financial year 2016-2017 and onwards, the human intent data item changed, and patients presenting for family violence reasons are identified using the following categories: sexual assault by current or former intimate partner; sexual assault by other family member (excluding intimate partner); neglect, maltreatment, assault by current or former intimate partner or; neglect, maltreatment, assault by other family member (excluding intimate partner).

• Due to methodological improvements made to the calculation of financial year, numbers for some historical years have changed slightly from what was reported in the June 2017 publication of the Family Violence Database.