In today's digital landscape, data transparency and compliance are paramount. Organizations across industries are striving to maintain trust and adhere to regulations governing data privacy and security. To support these efforts, we present our comprehensive Ads.txt and App-Ads.txt dataset.

Key Benefits of Our Dataset:

• Coverage: Our dataset offers a comprehensive view of the Ads.txt and App-Ads.txt files, providing valuable information about publishers, advertisers, and the relationships between them. You gain a holistic understanding of the digital advertising ecosystem.
• Multiple Data Formats: We understand that flexibility is essential. Our dataset is available in multiple formats, including .CSV, .JSON, and more. Choose the format that best suits your data processing needs.
• Global Scope: Whether your business operates in a single country or spans multiple continents, our dataset is tailored to meet your needs. It provides data from various countries, allowing you to analyze regional trends and compliance.
  • Top-Quality Data: Quality matters. Our dataset is meticulously curated and continuously updated to deliver the most accurate and reliable information. Trust in the integrity of your data for critical decision-making.
  • Seamless Integration: We've designed our dataset to seamlessly integrate with your existing systems and workflows. No disruptions—just enhanced compliance and efficiency.

The Power of Ads.txt & App-Ads.txt: Ads.txt (Authorized Digital Sellers) and App-Ads.txt (Authorized Sellers for Apps) are industry standards developed by the Interactive Advertising Bureau (IAB) to increase transparency and combat ad fraud. These files specify which companies are authorized to sell digital advertising inventory on a publisher's website or app. Understanding and maintaining these files is essential for data compliance and the prevention of unauthorized ad sales.

How Can You Benefit? - Data Compliance: Ensure that your organization adheres to industry standards and regulations by monitoring Ads.txt and App-Ads.txt files effectively. - Ad Fraud Prevention: Identify unauthorized sellers and take action to prevent ad fraud, ultimately protecting your revenue and brand reputation. - Strategic Insights: Leverage the data in these files to gain insights into your competitors, partners, and the broader digital advertising landscape. - Enhanced Decision-Making: Make data-driven decisions with confidence, armed with accurate and up-to-date information about your advertising partners. - Global Reach: If your operations span the globe, our dataset provides insights into the Ads.txt and App-Ads.txt files of publishers worldwide.

Multiple Data Formats for Your Convenience: - CSV (Comma-Separated Values): A widely used format for easy data manipulation and analysis in spreadsheets and databases. - JSON (JavaScript Object Notation): Ideal for structured data and compatibility with web applications and APIs. - Other Formats: We understand that different organizations have different preferences and requirements. Please inquire about additional format options tailored to your needs.

Data That You Can Trust:

We take data quality seriously. Our team of experts curates and updates the dataset regularly to ensure that you receive the most accurate and reliable information available. Your confidence in the data is our top priority.

Seamless Integration:

Integrate our Ads.txt and App-Ads.txt dataset effortlessly into your existing systems and processes. Our goal is to enhance your compliance efforts without causing disruptions to your workflow.

In Conclusion:

Transparency and compliance are non-negotiable in today's data-driven world. Our Ads.txt and App-Ads.txt dataset empowers you with the knowledge and tools to navigate the complexities of the digital advertising ecosystem while ensuring data compliance and integrity. Whether you're a Data Protection Officer, a data compliance professional, or a business leader, our dataset is your trusted resource for maintaining data transparency and safeguarding your organization's reputation and revenue.

Get Started Today:

Don't miss out on the opportunity to unlock the power of data transparency and compliance. Contact us today to learn more about our Ads.txt and App-Ads.txt dataset, available in multiple formats and tailored to your specific needs. Join the ranks of organizations worldwide that trust our dataset for a compliant and transparent future.

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.

Adveritising data and real time bidding data from multiple screens (TV, mobile, and web) and detailed performance metrics that span impressions, clicks, geographic data, view-ability, and demographic targeting. Our dataset ensures high accuracy, derived from a proprietary advertising technology platform trusted by leading brands and agencies to deliver cross-platform campaigns.

This dataset includes key metrics from ad auctions, bids & wins such as: -impressions -geographic data -clicks -viewability -demographic targeting -click-through rates (CTR)

How is the data generally sourced?

This dataset is sourced from auction-level insights, tracking bids, wins, and performance metrics across major ad exchanges and programmatic platforms. Data collection adheres to strict compliance standards, ensuring transparency and reliability.

What are the primary use cases or verticals of this Data Product?

Primary use cases include:

Predictive analytics: Build models to forecast campaign success.

Audience segmentation: Create more personalized and targeted ad experiences.

Campaign optimization: Optimize ad placement, timing, and performance.

Ad personalization: Drive engagement by tailoring ads to demographic and geographic audiences.

Industries served include advertising, media, retail, and e-commerce, with applicability in both programmatic and direct ad placements.

Advertising Data is a key component of our comprehensive data suite, designed to empower companies and marketers with actionable insights. Enables a holistic view of the advertising ecosystem, helping clients achieve higher ROI and better campaign outcomes.

In 2021, Apple's advertising revenue in the United States grew by **** percent. This is the highest value in the period of 2020 to 2024 presented in the data set. The source projected that after 2021, the growth will gradually slow down but will remain in a double-digit range.

About

This dataset provides insights into user behavior and online advertising, specifically focusing on predicting whether a user will click on an online advertisement. It contains user demographic information, browsing habits, and details related to the display of the advertisement. This dataset is ideal for building binary classification models to predict user interactions with online ads.

Features

• id: Unique identifier for each user.
• full_name: User's name formatted as "UserX" for anonymity.
• age: Age of the user (ranging from 18 to 64 years).
• gender: The gender of the user (categorized as Male, Female, or Non-Binary).
• device_type: The type of device used by the user when viewing the ad (Mobile, Desktop, Tablet).
• ad_position: The position of the ad on the webpage (Top, Side, Bottom).
• browsing_history: The user's browsing activity prior to seeing the ad (Shopping, News, Entertainment, Education, Social Media).
• time_of_day: The time when the user viewed the ad (Morning, Afternoon, Evening, Night).
• click: The target label indicating whether the user clicked on the ad (1 for a click, 0 for no click).

Goal

The objective of this dataset is to predict whether a user will click on an online ad based on their demographics, browsing behavior, the context of the ad's display, and the time of day. You will need to clean the data, understand it and then apply machine learning models to predict and evaluate data. It is a really challenging request for this kind of data. This data can be used to improve ad targeting strategies, optimize ad placement, and better understand user interaction with online advertisements.

Advertising Market Outlook

The global advertising market size was valued at approximately $700 billion in 2023 and is projected to reach around $1.2 trillion by 2032, growing at a CAGR of about 6.2% during the forecast period. The primary growth factor driving this market is the rapid expansion of digital platforms and the increasing importance of targeted advertising. The proliferation of smartphones and the internet has significantly transformed the advertising landscape, enabling advertisers to reach their audience more efficiently and effectively.

A major growth factor for the advertising market is the ever-increasing penetration of digital devices and internet connectivity. With more than half of the global population now having access to the internet, advertisers have an unprecedented opportunity to reach a vast audience. The rise of social media platforms, search engines, and video-sharing sites has further enabled highly targeted and measurable advertising campaigns, which have proven to be more efficient and cost-effective compared to traditional advertising methods.

Another significant driver is the advancements in data analytics and artificial intelligence. These technologies allow advertisers to analyze vast amounts of consumer data to understand behavior patterns and preferences, enabling them to create highly personalized and relevant advertisements. AI-driven programmatic advertising is gaining traction, as it automates the buying process of ads and optimizes them in real-time based on performance metrics, thus ensuring higher engagement rates and better ROI.

The shift towards mobile advertising also contributes significantly to market growth. With the increasing use of smartphones and mobile applications, advertisers are focusing more on mobile platforms to reach consumers. Mobile advertising offers unique advantages such as location-based targeting and the use of interactive content, which can enhance user engagement. Additionally, the development of 5G technology is expected to further boost mobile advertising by providing faster data speeds and more reliable connections, creating new opportunities for innovative ad formats.

In the evolving landscape of advertising, Experiential Advertising Agency Services have become increasingly vital. These services focus on creating immersive and interactive experiences that engage consumers on a deeper emotional level. By leveraging experiential marketing, brands can foster stronger connections with their audience, leading to enhanced brand loyalty and advocacy. This approach allows consumers to experience a brand's message firsthand, often through events, pop-up installations, or interactive digital experiences. As the advertising market continues to grow, the demand for experiential services is likely to rise, offering unique opportunities for brands to differentiate themselves in a crowded marketplace.

Regionally, the Asia Pacific region is emerging as a significant market for advertising, driven by the expanding middle-class population, increasing urbanization, and growing internet penetration. Countries like China and India are experiencing rapid growth in digital advertising, fueled by their large populations and thriving e-commerce sectors. North America and Europe continue to be mature markets with substantial advertising spending, particularly in digital formats. The Middle East & Africa and Latin America are also witnessing growth, albeit at a slower pace, as they gradually adopt digital advertising technologies.

Type Analysis

The advertising market is segmented by type, which includes Digital Advertising, Traditional Advertising, Out-of-Home Advertising, and Others. Digital advertising has seen the most rapid growth and is expected to continue dominating the market. It encompasses various formats such as display ads, video ads, social media ads, search engine marketing, and more. The key advantage of digital advertising is its ability to target specific demographics and measure campaign performance in real-time, providing valuable insights for advertisers. This segment's growth is further fueled by increasing internet usage and the proliferation of digital content platforms.

Traditional advertising, which includes print media, television, and radio, still holds a significant share of the market. Television remains a powerful medium for reaching a broad audience, especially for brand-building campai

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.

At MFour, our Behavioral Data stands out for its uniqueness and depth of insights. What makes our data genuinely exceptional is the combination of several key factors:

• First-Party Opt-In Data: Our data is sourced directly from our opt-in panel of consumers who willingly participate in research and provide observed behaviors. This ensures the highest data quality and eliminates privacy concerns. CCPA compliant.

• Unparalleled Data Coverage: With access to 3B+ billion events, we have an extensive pool of participants who allow us to observe their brick + mortar location visitation, app + web smartphone usage, or both. This large-scale coverage provides robust and reliable insights.

• Our data is generally sourced through our Surveys On The Go (SOTG) mobile research app, where consumers are incentivized with cash rewards to participate in surveys and share their observed behaviors. This incentivized approach ensures a willing and engaged panel, leading to the highest-quality data.

The primary use cases and verticals of our Behavioral Data Product are diverse and varied. Some key applications include:

• Data Acquisition and Modeling: Our data helps businesses acquire valuable insights into consumer behavior and enables modeling for various research objectives.

• Shopper Data Analysis: By understanding purchase behavior and patterns, businesses can optimize their strategies, improve targeting, and enhance customer experiences.

• Media Consumption Insights: Our data provides a deep understanding of viewer behavior and patterns across popular platforms like YouTube, Amazon Prime, Netflix, and Disney+, enabling effective media planning and content optimization.

• App Performance Optimization: Analyzing app behavior allows businesses to monitor usage patterns, track key performance indicators (KPIs), and optimize app experiences to drive user engagement and retention.

• Location-Based Targeting: With our detailed location data, businesses can map out consumer visits to physical venues and combine them with web and app behavior to create predictive ad targeting strategies.

• Audience Creation for Ad Placement: Our data enables the creation of highly targeted audiences for ad campaigns, ensuring better reach and engagement with relevant consumer segments.

The Behavioral Data Product complements our comprehensive suite of data solutions in the broader context of our data offering. It provides granular and event-level insights into consumer behaviors, which can be combined with other data sets such as survey responses, demographics, or custom profiling questions to offer a holistic understanding of consumer preferences, motivations, and actions.

MFour's Behavioral Data empowers businesses with unparalleled consumer insights, allowing them to make data-driven decisions, uncover new opportunities, and stay ahead in today's dynamic market landscape.

The app analytics market, valued at $7.29 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 21.09% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of mobile applications across diverse industries, coupled with the rising need for businesses to understand user behavior and optimize app performance, fuels the demand for sophisticated analytics solutions. Furthermore, advancements in data analytics technologies, including artificial intelligence (AI) and machine learning (ML), are enabling more insightful and actionable data analysis, further propelling market expansion. The diverse application of app analytics across marketing/advertising, revenue generation, and in-app performance monitoring across various sectors like BFSI, e-commerce, media, travel and tourism, and IT and telecom significantly contributes to this growth. The market is segmented by deployment (mobile apps and website/desktop apps) and end-user industry, with mobile app analytics currently dominating due to the widespread adoption of smartphones. The competitive landscape is characterized by a mix of established technology giants like Google and Amazon alongside specialized app analytics providers like AppsFlyer and Mixpanel. These companies are continuously innovating, integrating new technologies, and expanding their product offerings to cater to the evolving needs of businesses. While the North American market currently holds a significant share, the Asia-Pacific region is expected to witness substantial growth in the coming years driven by increasing smartphone penetration and digitalization initiatives. However, factors like data privacy concerns and the rising complexity of integrating various analytics tools could pose challenges to market growth. Nonetheless, the overall outlook for the app analytics market remains positive, indicating substantial opportunities for players across the value chain. Recent developments include: June 2024 - Comscore and Kochava unveiled an innovative performance media measurement solution, providing marketers with enhanced insights. This cutting-edge cross-screen solution empowers marketers to understand better how linear TV ad campaigns impact both online and offline actions. By integrating Comscore’s Exact Commercial Ratings (ECR) data with Kochava’s sophisticated marketing mix modeling, the solution facilitates the measurement of crucial metrics, including mobile app activities (such as installs and in-app purchases) and website interactions., June 2024 - AppsFlyer announced its integration of the Data Collaboration Platform with Start.io, an omnichannel advertising platform that focuses on real-time mobile audiences for publishers. Through this collaboration, businesses leveraging the AppsFlyer Data Collaboration Platform can merge their Start.io data with campaign metrics and audience insights, creating a more comprehensive dataset for precise audience targeting.. Key drivers for this market are: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Potential restraints include: Increasing Usage of Mobile/Web Apps Across Various End-user Industries, Increasing Adoption of Technologies like 5G Technology and Deeper Penetration of Smartphones; Increase in the Amount of Time Spent on Mobile Devices Coupled With the Increasing Focus on Enhancing Customer Experience. Notable trends are: Media and Entertainment Industry Expected to Capture Significant Share.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through last 30 days. There are classical Demographic, Game Genre, Title and Studio segments. However, we provide also plenty of specific User Types, which contain e.g. Hardcore Gamers, Big Spenders or Parents of Gamers. There are also audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Moreover, we offer segments for Virtual Reality, interest in Gaming Subscriptions, Payments, Micropayments, Devices and Platforms. We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Theme (e.g. Historical), which is definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Our data base include millions of profiles divided into popular categories. You can choose which target groups you want to reach. Segments based on users' interests, purchase intentions or demography. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: • marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. • ad tech companies - enriching 1st party data or using our raw data by your own data science team

We are ready for a cookieless era. We already gather and provide non-cookie ID - for example Universal IDs, CTV IDs or Mobile IDs.

A comprehensive dataset providing insights into the advertising industry for 2025, highlighting global advertising spending, digital and traditional marketing trends, the influence of social media advertising, mobile ad growth, advertising impact on consumer behavior, and the rise of programmatic advertising.

The global number of Twitter users in was forecast to continuously increase between 2024 and 2028 by in total 74.3 million users (+17.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 503.42 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.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 Twitter users in countries like South America and the Americas.

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.

About this Dataset

About this Dataset

This is a pre-crawled dataset can't be used for advertising directly, the adid is reversed currently. If you need the adid for advertisement, please contact with us. The app usage dataset is taken as subset of a bigger dataset includes KYC data with anonymous that was created by extracting data from 1st party data.

Objective

Multiple data sources from telco and ad exchange data helps you engage your audience with ease.

Oriented

This dataset was created by the mobility intelligence team of Groundhog Technologies in-house Marketing Intelligence Solution.

Contact With Us

Want to get more details consumer portofolio in Indonesia? Please contact with us via the link down below.

https://bit.ly/3f1dkoA

Category

Markets

Keywords

Indonesia,Telecommunications,Online data,geography

Row Count

229349

Price

$1000.00

Global B2B Mobile Phone Number Database | 100M+ Verified Contacts | 95% Accuracy Forager.ai provides the world’s most reliable mobile phone number data for businesses that refuse to compromise on quality. With 100 million+ professionally verified mobile numbers refreshed every 3 weeks, our database ensures 95% accuracy – so your teams never waste time on dead-end leads.

Why Our Data Wins ✅ Accuracy You Can Trust 95% of mobile numbers are verified against live carrier records and tied to current job roles. Say goodbye to “disconnected number” voicemails.

✅ Depth Beyond Digits Each contact includes 150+ data points:

Direct mobile numbers

Current job title, company, and department

Full career history + education background

Location data + LinkedIn profiles

Company size, industry, and revenue

✅ Freshness Guaranteed Bi-weekly updates combat job-hopping and role changes – critical for sales teams targeting decision-makers.

✅ Ethically Sourced & Compliant First-party collected data with full GDPR/CCPA compliance.

Who Uses This Data?

Sales Teams: Cold-call C-suite prospects with verified mobile numbers.

Marketers: Run hyper-personalized SMS/WhatsApp campaigns.

Recruiters: Source passive candidates with up-to-date contact intel.

Data Vendors: License premium datasets to enhance your product.

Tech Platforms: Power your SaaS tools via API with enterprise-grade B2B data.

Flexible Delivery, Instant Results

API (REST): Real-time integration for CRMs, dialers, or marketing stacks

CSV/JSON: Campaign-ready files.

PostgreSQL: Custom databases for large-scale enrichment

Compliance: Full audit trails + opt-out management

Why Forager.ai? → Proven ROI: Clients see 62% higher connect rates vs. industry averages (request case studies). → No Guesswork: Test-drive free samples before committing. → Scalable Pricing: Pay per record, license datasets, or get unlimited API access.

B2B Mobile Phone Data | Verified Contact Database | Sales Prospecting Lists | CRM Enrichment | Recruitment Phone Numbers | Marketing Automation | Phone Number Datasets | GDPR-Compliant Leads | Direct Dial Contacts | Decision-Maker Data

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Advertising professionals have great expectations of Location-Based Advertising (LBA). The present study therefore set out to investigate whether ads that are tailored to consumers’ location are indeed more effective than ads that are not. In addition it was investigated whether LBA is particularly likely to be effective when the ad is not only location-congruent, but also relevant toconsumers’ goals. Therefore, a 2 (location congruence) X 2 (goal relevance) experimental design was employed. These expectations were borne out: the location-congruent ad resulted in more purchases than the location-incongruent ad, but only when the ad was high in goal relevance. These results suggest that it is only profitable for advertisers to send ‘local’ messages when these messages are relevant.Participants were 139 Dutch adults, who were recruited through a market-research company. Of all participants, 65 (46.8%) were women, 60 participants (43.2%) had finished tertiary education, and the mean age was M(SD) = 38.9(15.0). The research setting was a Virtual Supermarket (VSM), where participants received a short grocery list with the instruction to purchase the listed products. The following variables were assessed: manipulation checks of goal relevance, manipulation checks of location congruence, purchase behaviour, presence, perceived ad intrusiveness, ad receptiveness, ad attitude. Furthermore, perceived entertainment value of the app, and irritation, value, informativeness, ease of use, credibility with regards to the app were assessed. Finally, the following stable individual differences were assessed: product involvement, privacy concerns, attitude towards advertising in general, and personal innovativeness.The dataset contains the following files:- Mother (raw) data: 2016_Vantriet_Investigating the effects of location-based_mother data (including a portable file)- Edited data including recodes and constructed scale: 2016_Vantriet_Investigating the effects of location-based_edited_data (including a portable file)- Syntax for scale construction and analyses: 2016_Vantriet_Investigating the effects of location-based_syntax from mother to edited- Questionnaire: 2016_Vantriet_Investigating the effects of location-based_questions- Codebook containing details on the dataset: 2016_Vantriet_Investigating the effects of location-based_codebook

This dataset contains response data collected for the research titled “Enhancing User Attitudes through Mobile Marketing: The Roles of Informativeness and Brand Satisfaction in Social Media Advertising.” The data were obtained through an online questionnaire distributed among Gen-Z respondents. The dataset includes variables related to attitude toward social media advertisement, mobile marketing, informativeness, and brand satisfaction.

In the first half of 2022, the ad fraud rate of mobile web video advertising in Germany stood at 1.9 percent and was the highest among the 12 countries presented in the data set. The lowest rate among them was recorded in Belgium, France, and Poland, with 0.1 percent.

This dataset provides Media agencies, Creative agencies, Brands, Online Advertisers, Web Publishers, digital visibility into competitors' cross-channel advertising strategies. Comprehensive competitor ad tracking across display, video, social, mobile app, and eCommerce channels to inform smarter media planning. Inspires creative ideation with real-world ad examples. Providing a clear view of what’s running across formats, channels, and markets. Monitor competitor campaigns, optimize media mix, and uncover trends across channels and creatives. Providing a unified view of adspend, strategy, and performance.

This dataset provides all Brands, Online Advertisers, Web Publishers including CTV & all Social platforms. Digital visibility into competitors' cross-channel advertising strategies. Comprehensive competitor ad tracking across display, video, social, mobile app, and eCommerce channels to inform smarter media planning. Inspires creative ideation with real-world ad examples. Providing a clear view of what’s running across formats, channels, and markets. Monitor competitor campaigns, optimize media mix, and uncover trends across channels and creatives. Providing a unified view of adspend, strategy, and performance.