10 datasets found
  1. Demographic profile of audience segments.

    • plos.figshare.com
    xls
    Updated Jan 31, 2024
    + more versions
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    Stephen Coleman; Michael D. Slater; Phil Wright; Oliver Wright; Lauren Skardon; Gillian Hayes (2024). Demographic profile of audience segments. [Dataset]. http://doi.org/10.1371/journal.pone.0296049.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 31, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Stephen Coleman; Michael D. Slater; Phil Wright; Oliver Wright; Lauren Skardon; Gillian Hayes
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Pandemics such as Covid-19 pose tremendous public health communication challenges in promoting protective behaviours, vaccination, and educating the public about risks. Segmenting audiences based on attitudes and behaviours is a means to increase the precision and potential effectiveness of such communication. The present study reports on such an audience segmentation effort for the population of England, sponsored by the United Kingdom Health Security Agency (UKHSA) and involving a collaboration of market research and academic experts. A cross-sectional online survey was conducted between 4 and 24 January 2022 with 5525 respondents (5178 used in our analyses) in England using market research opt-in panel. An additional 105 telephone interviews were conducted to sample persons without online or smartphone access. Respondents were quota sampled to be demographically representative. The primary analytic technique was k means cluster analysis, supplemented with other techniques including multi-dimensional scaling and use of respondent ‐ as well as sample-standardized data when necessary to address differences in response set for some groups of respondents. Identified segments were profiled against demographic, behavioural self-report, attitudinal, and communication channel variables, with differences by segment tested for statistical significance. Seven segments were identified, including distinctly different groups of persons who tended toward a high level of compliance and several that were relatively low in compliance. The segments were characterized by distinctive patterns of demographics, attitudes, behaviours, trust in information sources, and communication channels preferred. Segments were further validated by comparing the segmentation variable versus a set of demographic variables as predictors of reported protective behaviours in the past two weeks and of vaccine refusal; the demographics together had about one-quarter the effect size of the single seven-level segment variable. With respect to managerial implications, different communication strategies for each segment are suggested for each segment, illustrating advantages of rich segmentation descriptions for understanding public health communication audiences. Strengths and weaknesses of the methods used are discussed, to help guide future efforts.

  2. d

    Audience Targeting Data | 330M+ Global Devices | Audience Data & Advertising...

    • datarade.ai
    .json, .csv
    Updated Feb 4, 2025
    + more versions
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    DRAKO (2025). Audience Targeting Data | 330M+ Global Devices | Audience Data & Advertising | API Delivery [Dataset]. https://datarade.ai/data-products/audience-targeting-data-330m-global-devices-audience-dat-drako
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    .json, .csvAvailable download formats
    Dataset updated
    Feb 4, 2025
    Dataset authored and provided by
    DRAKO
    Area covered
    Armenia, Czech Republic, Russian Federation, Equatorial Guinea, Serbia, Curaçao, Namibia, Suriname, San Marino, Eritrea
    Description

    DRAKO is a Mobile Location Audience Targeting provider with a programmatic trading desk specialising in geolocation analytics and programmatic advertising. Through our customised approach, we offer business and consumer insights as well as addressable audiences for advertising.

    Mobile Location Data can be meaningfully transformed into Audience Targeting when used in conjunction with other dataset. Our expansive POI Data allows us to segment users by visitation to major brands and retailers as well as categorizes them into syndicated segments. Beyond POI visits, our proprietary Home Location Model determines residents of geographic areas such as Designated Market Areas, Counties, or States. Relatedly, our Home Location Model also fuels our Geodemographic Census Data segments as we are able to determine residents of the smallest census units. Additionally, we also have audiences of: ticketed event and venue visitors; survey data; and retail data.

    All of our Audience Targeting is 100% deterministic in that it only includes high-quality, real visits to locations as defined by a POIs satellite imagery buildings contour. We never use a radius when building an audience unless requested. We have a horizontal accuracy of 5m.

    Additionally, we can always cross reference your audience targeting with our syndicated segments:

    Overview of our Syndicated Audience Data Segments: - Brand/POI segments (specific named stores and locations) - Categories (behavioural segments - revealed habits) - Census demographic segments (HH income, race, religion, age, family structure, language, etc.,) - Events segments (ticketed live events, conferences, and seminars) - Resident segments (State/province, CMAs, DMAs, city, county, sub-county) - Political segments (Canadian Federal and Provincial, US Congressional Upper and Lower House, US States, City elections, etc.,) - Survey Data (Psychosocial/Demographic survey data) - Retail Data (Receipt/transaction data)

    All of our syndicated segments are customizable. That means you can limit them to people within a certain geography, remove employees, include only the most frequent visitors, define your own custom lookback, or extend our audiences using our Home, Work, and Social Extensions.

    In addition to our syndicated segments, we’re also able to run custom queries return to you all the Mobile Ad IDs (MAIDs) seen at in a specific location (address; latitude and longitude; or WKT84 Polygon) or in your defined geographic area of interest (political districts, DMAs, Zip Codes, etc.,)

    Beyond just returning all the MAIDs seen within a geofence, we are also able to offer additional customizable advantages: - Average precision between 5 and 15 meters - CRM list activation + extension - Extend beyond Mobile Location Data (MAIDs) with our device graph - Filter by frequency of visitations - Home and Work targeting (retrieve only employees or residents of an address) - Home extensions (devices that reside in the same dwelling from your seed geofence) - Rooftop level address geofencing precision (no radius used EVER unless user specified) - Social extensions (devices in the same social circle as users in your seed geofence) - Turn analytics into addressable audiences - Work extensions (coworkers of users in your seed geofence)

    Data Compliance: All of our Audience Targeting Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

    Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

  3. O

    Homeless Services Program Demographics

    • data.mesaaz.gov
    • citydata.mesaaz.gov
    csv, xlsx, xml
    Updated Dec 3, 2025
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    HMIS AZ (2025). Homeless Services Program Demographics [Dataset]. https://data.mesaaz.gov/Community-Services/Homeless-Services-Program-Demographics/t3h8-4u4a
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    xlsx, csv, xmlAvailable download formats
    Dataset updated
    Dec 3, 2025
    Dataset authored and provided by
    HMIS AZ
    Description

    Information about individuals experiencing homelessness and receiving services through Maricopa Regional Continuum of Care Coordinated Entry Points managed by Maricopa Association of Governments (MAG). See Reporting Interval and Report Date columns for more information about the date range covered. Information about "Mesa residents only" defined by value "client" in Demographic Audience field. Information about all individuals (Mesa resident and non-resident) receiving services from a Mesa-based provider defined by value "provider" in the Demographic Audience field. Data is collected by the Homeless Management Information System Arizona (HMIS AZ). See also https://community.solari-inc.org/homeless-management-information-system/

  4. d

    Replication Data for: Missing the Target? Using Surveys to Validate Social...

    • dataone.org
    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Sances, Michael (2023). Replication Data for: Missing the Target? Using Surveys to Validate Social Media Ad Targeting [Dataset]. http://doi.org/10.7910/DVN/ICYNIF
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Sances, Michael
    Description

    Facebook ads are increasingly used by political scientists as a method of survey recruitment. A key advantage is said to be the ability to recruit targeted audiences defined by demographics, political beliefs, location, and numerous other attributes. The same feature has been decried by non-researchers concerned about potential racial discrimination and foreign influence in elections. The extent to which these ads actually reach their targets, however, is unknown. Using a series of 6 surveys and 20 targeted ads, I show these ads regularly fail to reach their targets. The success rate ranges from 23% to 99%, and ads targeted toward groups defined by self-reported data and broader geographic locations are generally more successful.

  5. Participant demographics and viewing habits.

    • plos.figshare.com
    xls
    Updated Jun 12, 2024
    + more versions
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    Adam Gerace (2024). Participant demographics and viewing habits. [Dataset]. http://doi.org/10.1371/journal.pone.0302160.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 12, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Adam Gerace
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Fans may experience significant upset and distress when a television series ends. However, grief and loss reactions to the end of a fictional series have seldom been investigated. It is likely that the degree to which such reactions are felt is influenced by viewing motives (e.g., pleasure, meaning making), connection to the series and its fan community, relationships formed with characters, including parasocial bonds and experiences of empathy, and tendency to engage with others’ perspectives and emotions, including fictional characters. The purpose of this study was to examine predictors of fans’ grief and loss reactions to the end of the television series Neighbours, which aired from 1985 to 2022. Fans (n = 1289) completed an online survey shortly after the screening of the final episode. The survey measured grief emotions and cognitions, acceptance that the series had ended, distress at the loss of a parasocial relationship with a favourite character, feelings of closure, and expressions of gratitude for the series. Predictors of these grief and loss reactions examined in the survey were viewing motives, fan identity, strength of a parasocial relationship formed with a favourite character, empathy towards that character, and tendency to take others’ perspectives, experience empathic concern and personal distress, and tendency towards engagement with fictional characters. Greater grief and loss reactions were experienced by fans whose motives for watching involved being entertained and exposed to different lifestyles, who felt a stronger fan connection to the series, and who formed stronger parasocial empathic relationships with their favourite character. Factors such as gender, age, and empathic tendencies predicted various types of grief reactions. Understanding fan experiences when a long-running series ends advances theory and research on viewer parasocial relationships and engagement with media, as well as providing evidence that the loss of a series or favourite character can be viewed as a type of grief experience.

  6. Amazon Prime TV Shows

    • kaggle.com
    Updated Oct 13, 2020
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    Neelima Jauhari (2020). Amazon Prime TV Shows [Dataset]. https://www.kaggle.com/nilimajauhari/amazon-prime-tv-shows/discussion
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 13, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Neelima Jauhari
    Description

    Context

    This data set was created so as to analyze the latest shows available on Amazon Prime as well as the shows with a high rating.

    Content

    The data set contains the name of the show or title, year of the release which is the year in which the show was released or went on-air, No.of seasons means the number of seasons of the show which are available on Prime, Language is for the audio language of the show and does not take into consideration the language of the subtitles, genre of the show like Kids, Drama, Action and so on, IMDB ratings of the show: though for many tv shows and kid shows the rating was not available, Age of Viewers is to specify the age of the target audience- All in age means that the content is not restricted to any particular age group and all audiences can view it.

    Acknowledgements

    I have collected this data from Amazon Prime's Website.

    Inspiration

    Since a lot many TV shows have high IMDB ratings but don't get viewed that much because the audience is not aware of it or it is not advertised much. I have created this data set so as to find out the highest-rated shows in each category or in a particular genre.

  7. Comparison of YouTube videos demographics based on the usefulness score...

    • plos.figshare.com
    xls
    Updated Jun 14, 2023
    + more versions
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    Ahmed Jamleh; Shouq Mohammed Aljohani; Faisal Fahad Alzamil; Shahad Muhammad Aljuhayyim; Modhi Nasser Alsubaei; Showq Raad Alali; Nawaf Munawir Alotaibi; Mohannad Nassar (2023). Comparison of YouTube videos demographics based on the usefulness score categories. [Dataset]. http://doi.org/10.1371/journal.pone.0272765.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Ahmed Jamleh; Shouq Mohammed Aljohani; Faisal Fahad Alzamil; Shahad Muhammad Aljuhayyim; Modhi Nasser Alsubaei; Showq Raad Alali; Nawaf Munawir Alotaibi; Mohannad Nassar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    YouTube
    Description

    Comparison of YouTube videos demographics based on the usefulness score categories.

  8. f

    Comparison of YouTube videos demographics based on the date of upload.

    • figshare.com
    xls
    Updated Jun 14, 2023
    + more versions
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    Ahmed Jamleh; Shouq Mohammed Aljohani; Faisal Fahad Alzamil; Shahad Muhammad Aljuhayyim; Modhi Nasser Alsubaei; Showq Raad Alali; Nawaf Munawir Alotaibi; Mohannad Nassar (2023). Comparison of YouTube videos demographics based on the date of upload. [Dataset]. http://doi.org/10.1371/journal.pone.0272765.t005
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 14, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Ahmed Jamleh; Shouq Mohammed Aljohani; Faisal Fahad Alzamil; Shahad Muhammad Aljuhayyim; Modhi Nasser Alsubaei; Showq Raad Alali; Nawaf Munawir Alotaibi; Mohannad Nassar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    YouTube
    Description

    Comparison of YouTube videos demographics based on the date of upload.

  9. Participant demographic targets vs actual recruitment.

    • plos.figshare.com
    xls
    Updated Jun 8, 2023
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    Michael W. Brunt; Daniel M. Weary (2023). Participant demographic targets vs actual recruitment. [Dataset]. http://doi.org/10.1371/journal.pone.0260114.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 8, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Michael W. Brunt; Daniel M. Weary
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Participant demographic targets vs actual recruitment.

  10. Van Cleef & Arpels brand profile in the United States 2022

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Van Cleef & Arpels brand profile in the United States 2022 [Dataset]. https://www.statista.com/forecasts/1351919/van-cleef-and-arpels-jewelry-brand-profile-in-the-united-states
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    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Aug 24, 2022 - Sep 27, 2022
    Area covered
    United States
    Description

    How high is the brand awareness of Van Cleef & Arpels in the United States?When it comes to jewelry owners, brand awareness of Van Cleef & Arpels is at **% in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Van Cleef & Arpels in the United States?In total, *% of U.S. jewelry owners say they like Van Cleef & Arpels. However, in actuality, among the **% of U.S. respondents who know Van Cleef & Arpels, **% of people like the brand.What is the usage share of Van Cleef & Arpels in the United States?All in all, *% of jewelry owners in the United States use Van Cleef & Arpels. That means, of the **% who know the brand, *% own them.How loyal are the owners of Van Cleef & Arpels?Around *% of jewelry owners in the United States say they are likely to buy Van Cleef & Arpels again. Set in relation to the *% usage share of the brand, this means that **% of their owners show loyalty to the brand.What's the buzz around Van Cleef & Arpels in the United States?In September 2022, about *% of U.S. jewelry owners had heard about Van Cleef & Arpels in the media, on social media, or in advertising over the past three months. Of the **% who know the brand, that's *%, meaning at the time of the survey there's little to no buzz around Van Cleef & Arpels in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

  11. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Stephen Coleman; Michael D. Slater; Phil Wright; Oliver Wright; Lauren Skardon; Gillian Hayes (2024). Demographic profile of audience segments. [Dataset]. http://doi.org/10.1371/journal.pone.0296049.t001
Organization logo

Demographic profile of audience segments.

Related Article
Explore at:
xlsAvailable download formats
Dataset updated
Jan 31, 2024
Dataset provided by
PLOShttp://plos.org/
Authors
Stephen Coleman; Michael D. Slater; Phil Wright; Oliver Wright; Lauren Skardon; Gillian Hayes
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Pandemics such as Covid-19 pose tremendous public health communication challenges in promoting protective behaviours, vaccination, and educating the public about risks. Segmenting audiences based on attitudes and behaviours is a means to increase the precision and potential effectiveness of such communication. The present study reports on such an audience segmentation effort for the population of England, sponsored by the United Kingdom Health Security Agency (UKHSA) and involving a collaboration of market research and academic experts. A cross-sectional online survey was conducted between 4 and 24 January 2022 with 5525 respondents (5178 used in our analyses) in England using market research opt-in panel. An additional 105 telephone interviews were conducted to sample persons without online or smartphone access. Respondents were quota sampled to be demographically representative. The primary analytic technique was k means cluster analysis, supplemented with other techniques including multi-dimensional scaling and use of respondent ‐ as well as sample-standardized data when necessary to address differences in response set for some groups of respondents. Identified segments were profiled against demographic, behavioural self-report, attitudinal, and communication channel variables, with differences by segment tested for statistical significance. Seven segments were identified, including distinctly different groups of persons who tended toward a high level of compliance and several that were relatively low in compliance. The segments were characterized by distinctive patterns of demographics, attitudes, behaviours, trust in information sources, and communication channels preferred. Segments were further validated by comparing the segmentation variable versus a set of demographic variables as predictors of reported protective behaviours in the past two weeks and of vaccine refusal; the demographics together had about one-quarter the effect size of the single seven-level segment variable. With respect to managerial implications, different communication strategies for each segment are suggested for each segment, illustrating advantages of rich segmentation descriptions for understanding public health communication audiences. Strengths and weaknesses of the methods used are discussed, to help guide future efforts.

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