49 datasets found
  1. Mobile internet users worldwide 2020-2029

    • statista.com
    Updated Feb 5, 2025
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    Statista Research Department (2025). Mobile internet users worldwide 2020-2029 [Dataset]. https://www.statista.com/topics/779/mobile-internet/
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    Dataset updated
    Feb 5, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

  2. Mobile internet usage reach in North America 2020-2029

    • statista.com
    Updated Feb 5, 2025
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    Statista Research Department (2025). Mobile internet usage reach in North America 2020-2029 [Dataset]. https://www.statista.com/topics/779/mobile-internet/
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    Dataset updated
    Feb 5, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

  3. Mobile Penetration Rate

    • data.gov.sg
    Updated Jun 6, 2024
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    Info-communications Media Development Authority (2024). Mobile Penetration Rate [Dataset]. https://data.gov.sg/dataset/mobile-penetration-rate
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    Dataset updated
    Jun 6, 2024
    Dataset provided by
    Infocomm Media Development Authorityhttp://www.imda.gov.sg/
    Authors
    Info-communications Media Development Authority
    License

    https://data.gov.sg/open-data-licencehttps://data.gov.sg/open-data-licence

    Time period covered
    Jan 1997 - May 2019
    Description

    Dataset from Info-communications Media Development Authority. For more information, visit https://data.gov.sg/datasets/d_5fb7ffda1ffd756151b1650d4c64363c/view

  4. MobilePhone's Dataset

    • kaggle.com
    Updated Jan 20, 2023
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    Sudhanshu Yadav (2023). MobilePhone's Dataset [Dataset]. http://doi.org/10.34740/kaggle/dsv/4877251
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 20, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Sudhanshu Yadav
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This Dataset is instrumental if you are working on a machine-learning project where you are working in which you need information about smartphones, and feature phone available in the Indian market. This Dataset is having 5 columns -> model name, price, ratings, reviews, and specifications. Do not confuse it with the duplicated values in the name and the price columns, because in the model name, there are the same phones available with different color options Google pixel 6pro is available in 2-3 color options but the price was the same. So your domain knowledge and how better you do the feature engineering over this dataset is dependent. The price is in the Indian rupee you can convert it according to your use case. Now I Updated the dataset and added a new version of the dataset after some Preprocessing (Updated_Mobile_Dataset.csv) In which the new version does not contain any null values added the company column in the new version and also separated the Rom and Ram columns. The shape of the newly updated data set is (28036, 8) The objective here is to forecast the price of mobile phones. Please upvote if you find the dataset useful.

  5. f

    People Data | Global |Reach - 900 Million Records for Comprehensive Consumer...

    • factori.ai
    Updated Jul 15, 2025
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    (2025). People Data | Global |Reach - 900 Million Records for Comprehensive Consumer Insights & Data Enrichment [Dataset]. https://www.factori.ai/datasets/people-data/
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    Dataset updated
    Jul 15, 2025
    License

    https://www.factori.ai/privacy-policyhttps://www.factori.ai/privacy-policy

    Area covered
    Global
    Description

    Our proprietary People Data is a mobile user dataset that connects anonymous IDs to a wide range of attributes, including demographics, device ownership, audience segments, key locations, and more. This rich dataset allows our partner brands to gain a comprehensive view of consumers based on their personas, enabling them to derive actionable insights swiftly.

    People Data Graph

    • Record Count: 900 Million
    • Capturing Frequency: Once per Event
    • Delivering Frequency: Once per Month
    • Updated: Monthly

    People Data

    Reach Our extensive data reach covers a variety of categories, encompassing user demographics, Mobile Advertising IDs (MAID), device details, locations, affluence, interests, traveled countries, and more. Data Export Methodology We dynamically collect and provide the most updated data and insights through the best-suited method at appropriate intervals, whether daily, weekly, monthly, or quarterly.

    Business Needs

    Our People Data caters to various business needs, offering valuable insights for consumer analysis, data enrichment, sales forecasting, and retail analytics, empowering brands to make informed decisions and optimize their strategies.

  6. s

    BuzzCity mobile advertisement dataset

    • researchdata.smu.edu.sg
    • smu.edu.sg
    bin
    Updated May 30, 2023
    + more versions
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    Living Analytics Research Centre (2023). BuzzCity mobile advertisement dataset [Dataset]. http://doi.org/10.25440/smu.12062703.v1
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    binAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    SMU Research Data Repository (RDR)
    Authors
    Living Analytics Research Centre
    License

    http://rightsstatements.org/vocab/InC/1.0/http://rightsstatements.org/vocab/InC/1.0/

    Description

    This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

    publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

    On the other hand, the click database records the click traffics and has several fields:

    id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

  7. Z

    Data from: A 24-hour dynamic population distribution dataset based on mobile...

    • data.niaid.nih.gov
    • explore.openaire.eu
    • +1more
    Updated Feb 16, 2022
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    Matti Manninen (2022). A 24-hour dynamic population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4724388
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    Dataset updated
    Feb 16, 2022
    Dataset provided by
    Matti Manninen
    Henrikki Tenkanen
    Tuuli Toivonen
    Olle Järv
    Claudia Bergroth
    License

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

    Area covered
    Helsinki Metropolitan Area, Finland
    Description

    Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

    In this dataset:

    We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

    Please cite this dataset as:

    Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

    Organization of data

    The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

    HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

    HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

    HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

    target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

    Column names

    YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

    H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

    In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

    License Creative Commons Attribution 4.0 International.

    Related datasets

    Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

    Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

  8. Mobile_usage_dataset_individual_person

    • kaggle.com
    Updated Mar 14, 2020
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    arul08 (2020). Mobile_usage_dataset_individual_person [Dataset]. https://www.kaggle.com/arul08/mobile-usage-dataset-individual-person/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 14, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    arul08
    Description

    Do you know?

    Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

    What it consists of?

    This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

    It lists the usage time of apps for each day.

    What we can do?

    Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

    The dataset was collected from the app usage app.

  9. Mobile internet penetration in Europe 2024, by country

    • statista.com
    Updated Feb 5, 2025
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    Statista Research Department (2025). Mobile internet penetration in Europe 2024, by country [Dataset]. https://www.statista.com/topics/779/mobile-internet/
    Explore at:
    Dataset updated
    Feb 5, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    Switzerland is leading the ranking by population share with mobile internet access, recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

  10. C

    Canada Internet Usage: Search Engine Market Share: Mobile: Haosou

    • ceicdata.com
    Updated Dec 2, 2024
    + more versions
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    CEICdata.com (2024). Canada Internet Usage: Search Engine Market Share: Mobile: Haosou [Dataset]. https://www.ceicdata.com/en/canada/internet-usage-search-engine-market-share/internet-usage-search-engine-market-share-mobile-haosou
    Explore at:
    Dataset updated
    Dec 2, 2024
    Dataset provided by
    CEICdata.com
    License

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

    Time period covered
    Sep 18, 2024 - Sep 28, 2024
    Area covered
    Canada
    Description

    Canada Internet Usage: Search Engine Market Share: Mobile: Haosou data was reported at 0.010 % in 28 Sep 2024. This stayed constant from the previous number of 0.010 % for 27 Sep 2024. Canada Internet Usage: Search Engine Market Share: Mobile: Haosou data is updated daily, averaging 0.010 % from Sep 2024 (Median) to 28 Sep 2024, with 11 observations. The data reached an all-time high of 0.010 % in 28 Sep 2024 and a record low of 0.010 % in 28 Sep 2024. Canada Internet Usage: Search Engine Market Share: Mobile: Haosou data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Canada – Table CA.SC.IU: Internet Usage: Search Engine Market Share.

  11. RICO dataset

    • kaggle.com
    Updated Dec 2, 2021
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    Onur Gunes (2021). RICO dataset [Dataset]. https://www.kaggle.com/onurgunes1993/rico-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 2, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Onur Gunes
    Description

    Context

    Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

    Content

    Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

    Acknowledgements

    UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

    Inspiration

    The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

  12. d

    TagX Web Browsing clickstream Data - 300K Users North America, EU - GDPR -...

    • datarade.ai
    .json, .csv, .xls
    Updated Sep 16, 2024
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    TagX (2024). TagX Web Browsing clickstream Data - 300K Users North America, EU - GDPR - CCPA Compliant [Dataset]. https://datarade.ai/data-products/tagx-web-browsing-clickstream-data-300k-users-north-america-tagx
    Explore at:
    .json, .csv, .xlsAvailable download formats
    Dataset updated
    Sep 16, 2024
    Dataset authored and provided by
    TagX
    Area covered
    United States
    Description

    TagX Web Browsing Clickstream Data: Unveiling Digital Behavior Across North America and EU Unique Insights into Online User Behavior TagX Web Browsing clickstream Data offers an unparalleled window into the digital lives of 1 million users across North America and the European Union. This comprehensive dataset stands out in the market due to its breadth, depth, and stringent compliance with data protection regulations. What Makes Our Data Unique?

    Extensive Geographic Coverage: Spanning two major markets, our data provides a holistic view of web browsing patterns in developed economies. Large User Base: With 300K active users, our dataset offers statistically significant insights across various demographics and user segments. GDPR and CCPA Compliance: We prioritize user privacy and data protection, ensuring that our data collection and processing methods adhere to the strictest regulatory standards. Real-time Updates: Our clickstream data is continuously refreshed, providing up-to-the-minute insights into evolving online trends and user behaviors. Granular Data Points: We capture a wide array of metrics, including time spent on websites, click patterns, search queries, and user journey flows.

    Data Sourcing: Ethical and Transparent Our web browsing clickstream data is sourced through a network of partnered websites and applications. Users explicitly opt-in to data collection, ensuring transparency and consent. We employ advanced anonymization techniques to protect individual privacy while maintaining the integrity and value of the aggregated data. Key aspects of our data sourcing process include:

    Voluntary user participation through clear opt-in mechanisms Regular audits of data collection methods to ensure ongoing compliance Collaboration with privacy experts to implement best practices in data anonymization Continuous monitoring of regulatory landscapes to adapt our processes as needed

    Primary Use Cases and Verticals TagX Web Browsing clickstream Data serves a multitude of industries and use cases, including but not limited to:

    Digital Marketing and Advertising:

    Audience segmentation and targeting Campaign performance optimization Competitor analysis and benchmarking

    E-commerce and Retail:

    Customer journey mapping Product recommendation enhancements Cart abandonment analysis

    Media and Entertainment:

    Content consumption trends Audience engagement metrics Cross-platform user behavior analysis

    Financial Services:

    Risk assessment based on online behavior Fraud detection through anomaly identification Investment trend analysis

    Technology and Software:

    User experience optimization Feature adoption tracking Competitive intelligence

    Market Research and Consulting:

    Consumer behavior studies Industry trend analysis Digital transformation strategies

    Integration with Broader Data Offering TagX Web Browsing clickstream Data is a cornerstone of our comprehensive digital intelligence suite. It seamlessly integrates with our other data products to provide a 360-degree view of online user behavior:

    Social Media Engagement Data: Combine clickstream insights with social media interactions for a holistic understanding of digital footprints. Mobile App Usage Data: Cross-reference web browsing patterns with mobile app usage to map the complete digital journey. Purchase Intent Signals: Enrich clickstream data with purchase intent indicators to power predictive analytics and targeted marketing efforts. Demographic Overlays: Enhance web browsing data with demographic information for more precise audience segmentation and targeting.

    By leveraging these complementary datasets, businesses can unlock deeper insights and drive more impactful strategies across their digital initiatives. Data Quality and Scale We pride ourselves on delivering high-quality, reliable data at scale:

    Rigorous Data Cleaning: Advanced algorithms filter out bot traffic, VPNs, and other non-human interactions. Regular Quality Checks: Our data science team conducts ongoing audits to ensure data accuracy and consistency. Scalable Infrastructure: Our robust data processing pipeline can handle billions of daily events, ensuring comprehensive coverage. Historical Data Availability: Access up to 24 months of historical data for trend analysis and longitudinal studies. Customizable Data Feeds: Tailor the data delivery to your specific needs, from raw clickstream events to aggregated insights.

    Empowering Data-Driven Decision Making In today's digital-first world, understanding online user behavior is crucial for businesses across all sectors. TagX Web Browsing clickstream Data empowers organizations to make informed decisions, optimize their digital strategies, and stay ahead of the competition. Whether you're a marketer looking to refine your targeting, a product manager seeking to enhance user experience, or a researcher exploring digital trends, our cli...

  13. c

    Unlocking User Sentiment: The App Store Reviews Dataset

    • crawlfeeds.com
    json, zip
    Updated Jun 20, 2025
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    Crawl Feeds (2025). Unlocking User Sentiment: The App Store Reviews Dataset [Dataset]. https://crawlfeeds.com/datasets/app-store-reviews-dataset
    Explore at:
    json, zipAvailable download formats
    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Crawl Feeds
    License

    https://crawlfeeds.com/privacy_policyhttps://crawlfeeds.com/privacy_policy

    Description

    This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

    Dataset Specifications:

    • Investment: $45.0
    • Status: Published and immediately available.
    • Category: Ratings and Reviews Data
    • Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
    • Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
    • Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

    Richness of Detail (11 Comprehensive Fields):

    Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

    1. Review Content:

      • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
      • title: The title given to the review by the user, often summarizing their main point.
      • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.
    2. Reviewer & Rating Information:

      • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
      • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.
    3. App & Origin Context:

      • app_name: The name of the application being reviewed.
      • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
      • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.
    4. Metadata & Timestamps:

      • _id: A unique identifier for the specific review record in the dataset.
      • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
      • date: The original date the review was posted by the user on the App Store.

    Expanded Use Cases & Analytical Applications:

    This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

    • Product Development & Improvement:

      • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
      • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
      • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
      • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.
    • Market Research & Competitive Intelligence:

      • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
      • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
      • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.
    • Marketing & App Store Optimization (ASO):

      • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
      • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
      • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
      • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.
    • Academic & Data Science Research:

      • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
      • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
      • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

    This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

  14. g

    The major statistical data of natural referencing | gimi9.com

    • gimi9.com
    Updated Nov 30, 2024
    + more versions
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    (2024). The major statistical data of natural referencing | gimi9.com [Dataset]. https://gimi9.com/dataset/eu_65f594ba5cf5f141524928b6/
    Explore at:
    Dataset updated
    Nov 30, 2024
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This dataset gathers the most crucial SEO statistics for the year, providing an overview of the dominant trends and best practices in the field of search engine optimization. Aimed at digital marketing professionals, site owners, and SEO analysts, this collection of information serves as a guide to navigate the evolving SEO landscape with confidence and accuracy. Mode of Data Production: The statistics have been carefully selected and compiled from a variety of credible and recognized sources in the SEO industry, including research reports, web traffic data analytics, and consumer and marketing professional surveys. Each statistic was checked for reliability and relevance to current trends. Categories Included: User search behaviour: Statistics on the evolution of search modes, including voice and mobile search. Mobile Optimisation: Data on the importance of site optimization for mobile devices. Importance of Backlinks: Insights on the role of backlinks in SEO ranking and the need to prioritize quality. Content quality: Statistics highlighting the importance of relevant and engaging content for SEO. Search engine algorithms: Information on the impact of algorithm updates on SEO strategies. Usefulness of the Data: This dataset is designed to help users quickly understand current SEO dynamics and apply that knowledge in optimizing their digital marketing strategies. It provides a solid foundation for benchmarking, strategic planning, and informed decision-making in the field of SEO. Update and Accessibility: To ensure relevance and timeliness, the dataset will be regularly updated with new information and emerging trends in the SEO world.

  15. d

    Phone Call Actions from State of Iowa Google My Business Profiles

    • datasets.ai
    • s.cnmilf.com
    • +1more
    Updated Sep 11, 2024
    + more versions
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    State of Iowa (2024). Phone Call Actions from State of Iowa Google My Business Profiles [Dataset]. https://datasets.ai/datasets/phone-call-actions-from-state-of-iowa-google-my-business-profiles
    Explore at:
    Dataset updated
    Sep 11, 2024
    Dataset authored and provided by
    State of Iowa
    Area covered
    Iowa
    Description

    The number of times during the month someone called State Offices from their Google My Business profiles.

  16. Data from: SBU Mobile Soundings IMPACTS

    • catalog.data.gov
    • s.cnmilf.com
    • +4more
    Updated Jul 3, 2025
    + more versions
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    NASA/MSFC/GHRC (2025). SBU Mobile Soundings IMPACTS [Dataset]. https://catalog.data.gov/dataset/sbu-mobile-soundings-impacts
    Explore at:
    Dataset updated
    Jul 3, 2025
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    The SBU Mobile Sounding IMPACTS dataset consists of mobile sounding profiles collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. Funded by NASA’s Earth Venture program, IMPACTS is the first comprehensive study of East Coast snowstorms in 30 years. Mobile-sounding profiles were obtained about every three hours during snow events by Stony Brook University (SBU). The sounding measures temperature, humidity, height, and horizontal wind direction and speed in the atmosphere. Atmospheric pressure is calculated from GPS height. Data files are available from January 18, 2020, through February 28, 2023 in netCDF-3 format.

  17. d

    BDEX In-Market Automotive Consumers Data⎢USA⎢Real-time Datasets

    • datarade.ai
    Updated Jun 2, 2020
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    BDEX (2020). BDEX In-Market Automotive Consumers Data⎢USA⎢Real-time Datasets [Dataset]. https://datarade.ai/data-providers/bdex/data-products/in-market-automotive
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    Dataset updated
    Jun 2, 2020
    Dataset authored and provided by
    BDEX
    Area covered
    United States
    Description

    BDEX identifies in-market automotive consumers via multiple real-time datasets including:

    1) Online vehicle searches from popular car search web sites 2) Lease expirations 3) Geolocation data identifying consumers visiting a dealership location

    We use all of the following to identify consumer behaviors, map them against our comprehensive identity graph and send you a feed of these in-market consumers in any output format you need including Mobile IDs (MAIDs), Hashed Emails (MD5, SHA256) or full postal including Name, Address, Phone and Email.

  18. d

    815 Million Global Contact Data - B2B / Email / Mobile Phone / LinkedIn URL...

    • datarade.ai
    .json, .csv
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    RampedUp Global Data Solutions, 815 Million Global Contact Data - B2B / Email / Mobile Phone / LinkedIn URL - RampedUp [Dataset]. https://datarade.ai/data-products/global-contact-data-personal-and-professional-840-million-rampedup-global-data-solutions
    Explore at:
    .json, .csvAvailable download formats
    Dataset authored and provided by
    RampedUp Global Data Solutions
    Area covered
    Haiti, Ireland, Pakistan, Chad, Greece, Uganda, Sint Eustatius and Saba, Bolivia (Plurinational State of), Grenada, United States Minor Outlying Islands
    Description

    Sign Up for a free trial: https://rampedup.io/sign-up-%2F-log-in - 7 Days and 50 Credits to test our quality and accuracy.

    These are the fields available within the RampedUp Global dataset.

    CONTACT DATA: Personal Email Address - We manage over 115 million personal email addresses Professional Email - We manage over 200 million professional email addresses Home Address - We manage over 20 million home addresses Mobile Phones - 65 million direct lines to decision makers Social Profiles - Individual Facebook, Twitter, and LinkedIn Local Address - We manage 65M locations for local office mailers, event-based marketing or face-to-face sales calls.

    JOB DATA: Job Title - Standardized titles for ease of use and selection Company Name - The Contact's current employer Job Function - The Company Department associated with the job role Title Level - The Level in the Company associated with the job role Job Start Date - Identify people new to their role as a potential buyer

    EMPLOYER DATA: Websites - Company Website, Root Domain, or Full Domain Addresses - Standardized Address, City, Region, Postal Code, and Country Phone - E164 phone with country code Social Profiles - LinkedIn, CrunchBase, Facebook, and Twitter

    FIRMOGRAPHIC DATA: Industry - 420 classifications for categorizing the company’s main field of business Sector - 20 classifications for categorizing company industries 4 Digit SIC Code - 239 classifications and their definitions 6 Digit NAICS - 452 classifications and their definitions Revenue - Estimated revenue and bands from 1M to over 1B Employee Size - Exact employee count and bands Email Open Scores - Aggregated data at the domain level showing relationships between email opens and corporate domains. IP Address -Company level IP Addresses associated to Domains from a DNS lookup

    CONSUMER DATA: Education - Alma Mater, Degree, Graduation Date Skills - Accumulated Skills associated with work experience
    Interests - Known interests of contact Connections - Number of social connections. Followers - Number of social followers

    Download our data dictionary: https://rampedup.io/our-data

  19. p

    Uruguay Number Dataset

    • listtodata.com
    .csv, .xls, .txt
    Updated Jul 17, 2025
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    List to Data (2025). Uruguay Number Dataset [Dataset]. https://listtodata.com/uruguay-dataset
    Explore at:
    .csv, .xls, .txtAvailable download formats
    Dataset updated
    Jul 17, 2025
    Dataset authored and provided by
    List to Data
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2025 - Dec 31, 2025
    Area covered
    Uruguay
    Variables measured
    phone numbers, Email Address, full name, Address, City, State, gender,age,income,ip address,
    Description

    Uruguay number dataset has accurate numbers attached with verified through our team. Most importantly, these cell phone numbers belong to active users only. In fact, these specialties make it a valuable marketing help. if your business is new or old, you can increase your reach and connect to a large audience with this directory. Yet, you will find many people who have an attraction to your products and will purchase from you. The Uruguay number dataset will support you make your brand more renowned. In general, by becoming a known brand in the market, you can grow your brand value significantly. In other words, the contacts on this mobile number list are active and real. As a result, you will benefit greatly if you purchase this cheap but valuable database. Uruguay phone data can be a great solution for telemarketing. Anyone can use the contact lead to reach various people all over the country. Uruguay phone data allows you to give product details with your messages to make them more appealing and reliable. Your product quality and content will increase the awareness of the interested client. Our team gives the number lead in an Excel and CSV file. Uruguay phone data is an opt-in and permission-based contact list. Even, with a cheap yet fresh list like ours, your SMS marketing will be more influential. People can now relate to your business more after you successfully utilize this library from our website. So, order this number package now from List To Data to promote your goods and services everywhere inside the country. Uruguay phone number list is a massive database. However, many people will show interest in your products and services. We promise you sincere service and active support, we create it following GDPR rules. Our special team will solve the issue for you, thus you don’t have to worry about not obtaining the worth of your money. Further, the Uruguay phone number list will aid your business in many new ways. On the other hand, no one wants to miss out on such a huge and versatile audience in Uruguay. To this end, purchasing this contact list will be a gem for any business any day.

  20. d

    US Consumer Marketing Data - 269M+ Consumer Records - 95% Email and Direct...

    • datarade.ai
    Updated Jun 1, 2022
    + more versions
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    Giant Partners (2022). US Consumer Marketing Data - 269M+ Consumer Records - 95% Email and Direct Dials Accuracy [Dataset]. https://datarade.ai/data-products/consumer-business-data-postal-phone-email-demographics-giant-partners
    Explore at:
    Dataset updated
    Jun 1, 2022
    Dataset authored and provided by
    Giant Partners
    Area covered
    United States of America
    Description

    Premium B2C Consumer Database - 269+ Million US Records

    Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

    Core Database Statistics

    Consumer Records: Over 269 million

    Email Addresses: Over 160 million (verified and deliverable)

    Phone Numbers: Over 76 million (mobile and landline)

    Mailing Addresses: Over 116,000,000 (NCOA processed)

    Geographic Coverage: Complete US (all 50 states)

    Compliance Status: CCPA compliant with consent management

    Targeting Categories Available

    Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

    Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

    Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

    Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

    Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

    Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

    Multi-Channel Campaign Applications

    Deploy across all major marketing channels:

    Email marketing and automation

    Social media advertising

    Search and display advertising (Google, YouTube)

    Direct mail and print campaigns

    Telemarketing and SMS campaigns

    Programmatic advertising platforms

    Data Quality & Sources

    Our consumer data aggregates from multiple verified sources:

    Public records and government databases

    Opt-in subscription services and registrations

    Purchase transaction data from retail partners

    Survey participation and research studies

    Online behavioral data (privacy compliant)

    Technical Delivery Options

    File Formats: CSV, Excel, JSON, XML formats available

    Delivery Methods: Secure FTP, API integration, direct download

    Processing: Real-time NCOA, email validation, phone verification

    Custom Selections: 1,000+ selectable demographic and behavioral attributes

    Minimum Orders: Flexible based on targeting complexity

    Unique Value Propositions

    Dual Spouse Targeting: Reach both household decision-makers for maximum impact

    Cross-Platform Integration: Seamless deployment to major ad platforms

    Real-Time Updates: Monthly data refreshes ensure maximum accuracy

    Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

    Compliance Management: Built-in opt-out and suppression list management

    Ideal Customer Profiles

    E-commerce retailers seeking customer acquisition

    Financial services companies targeting specific demographics

    Healthcare organizations with compliant marketing needs

    Automotive dealers and service providers

    Home improvement and real estate professionals

    Insurance companies and agents

    Subscription services and SaaS providers

    Performance Optimization Features

    Lookalike Modeling: Create audiences similar to your best customers

    Predictive Scoring: Identify high-value prospects using AI algorithms

    Campaign Attribution: Track performance across multiple touchpoints

    A/B Testing Support: Split audiences for campaign optimization

    Suppression Management: Automatic opt-out and DNC compliance

    Pricing & Volume Options

    Flexible pricing structures accommodate businesses of all sizes:

    Pay-per-record for small campaigns

    Volume discounts for large deployments

    Subscription models for ongoing campaigns

    Custom enterprise pricing for high-volume users

    Data Compliance & Privacy

    VIA.tools maintains industry-leading compliance standards:

    CCPA (California Consumer Privacy Act) compliant

    CAN-SPAM Act adherence for email marketing

    TCPA compliance for phone and SMS campaigns

    Regular privacy audits and data governance reviews

    Transparent opt-out and data deletion processes

    Getting Started

    Our data specialists work with you to:

    1. Define your target audience criteria

    2. Recommend optimal data selections

    3. Provide sample data for testing

    4. Configure delivery methods and formats

    5. Implement ongoing campaign optimization

    Why We Lead the Industry

    With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

    Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

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Statista Research Department (2025). Mobile internet users worldwide 2020-2029 [Dataset]. https://www.statista.com/topics/779/mobile-internet/
Organization logo

Mobile internet users worldwide 2020-2029

Explore at:
181 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Feb 5, 2025
Dataset provided by
Statistahttp://statista.com/
Authors
Statista Research Department
Description

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

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