Gain a competitive edge with our comprehensive Advertising Dataset, designed for marketers, analysts, and businesses to track ad performance, analyze competitor strategies, and optimize campaign effectiveness.

Dataset Features

Sponsored Posts & Ads: Access structured data on paid advertisements, including post content, engagement metrics, and platform details. Competitor Advertising Insights: Extract data on competitor campaigns, influencer partnerships, and promotional strategies. Audience Engagement Metrics: Analyze likes, shares, comments, and impressions to measure ad effectiveness. Multi-Platform Coverage: Track ads across LinkedIn, Instagram, Facebook, TikTok, Twitter (X), Pinterest, and more. Historical & Real-Time Data: Retrieve historical ad performance data or access continuously updated records for real-time insights.

Customizable Subsets for Specific Needs Our Advertising Dataset is fully customizable, allowing you to filter data based on platform, ad type, engagement levels, or specific brands. Whether you need broad coverage for market research or focused data for ad optimization, we tailor the dataset to your needs.

Popular Use Cases

Targeted Advertising & Audience Segmentation: Refine ad targeting by analyzing competitor content, audience demographics, and engagement trends. Campaign Performance Analysis: Measure ad effectiveness by tracking engagement metrics, reach, and conversion rates. Competitive Intelligence: Monitor competitor ad strategies, influencer collaborations, and promotional trends. Market Research & Trend Forecasting: Identify emerging advertising trends, high-performing content types, and consumer preferences. AI & Predictive Analytics: Use structured ad data to train AI models for automated ad optimization, sentiment analysis, and performance forecasting.

Whether you're optimizing ad campaigns, analyzing competitor strategies, or refining audience targeting, our Advertising Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

According to market estimates, total media advertising spending the United States in 2020 would amount to ***** billion U.S. dollars. By 2024, the figure is expected to grow to *** billion dollars.Advertising spending in the U.S. – additional informationAdvertising can utilize almost any form of media to meet its needs, including print, television, radio, cinema, outdoor, mobile and online. If there is a space where consumers are going to look at, advertisers will buy it up. The United States, in particular, is the largest advertising market in the world. China is to be the second leading market according to the ranking, yet its ad expenditures are estimated to represent less than **** of the amount calculated for the U.S. Television is the main medium for advertisers in the U.S., as it has accounted for about ** percent of all advertising spending in the country since 2010. However, with the rise of digital platforms, not all mediums are as heavily invested in as others. Particularly traditional mediums such as radio, magazines and newspapers all suffered a decrease in ad spending, with newspapers suffering the most. Newspaper ad spent is forecast to decline from nearly ** percent in 2010 to about *** percent in 2020. Despite being a leader in the advertising industry as of 2015, television’s share of advertising spending is also projected to decrease in the coming years. Digital is forecast to become the main media for advertisers in the U.S., accounting for ** percent of all advertising spending in the country in 2017. In terms of revenue, digital advertising in the U.S. is forecast to generate more than *** billion U.S. dollars by 2022, with search advertising accounting for the largest portion of this amount. Banner ads and social media advertising also belong to digital formats important for advertisers in the U.S. Within the digital market, mobile advertising is also a heavily invested in sub-sector of the advertising industry. In 2020, this spending on mobile ads in the country reached **** billion U.S. dollars. Search advertising and display advertising account for the majority of mobile advertising spending.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of all NAICS under advertising, public relations, and related services (NAICS 5418), annual, for five years of data.

In 2024, digital pure players (companies that operate primarily online, such as Google or Amazon) generated an advertising revenue of *** billion U.S. dollars worldwide. In 2025, their ad revenue is forecast to amount to *** billion dollars.

1) Data Introduction • The Advertising dataset consists of 200 tabular data that records TV, radio, and newspaper advertising costs and subsequent sales.

2) Data Utilization (1) Advertising dataset has characteristics that: • Each row consists of TV, radio, and newspaper advertising costs (in $1,000 each) and sales (in millions). • Data for small regression with a total of three input characteristics and one target variable (sales). (2) Advertising dataset can be used to: • Analysis of advertising effects: It can be used to develop regression models that analyze the impact of investment costs on sales by various advertising media. • Optimizing marketing strategy: It can be used to establish an efficient marketing strategy by predicting sales changes due to advertising budget allocation.

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.

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.

The Image and Video Advertisements collection consists of an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. The data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer ("What should I do according to this ad, and why should I do it? "), and symbolic references ads make (e.g. a dove symbolizes peace).

Introduction

Location Based Advertising Statistics: In the modern digital era advertising has become more personal and more targeted. One of the trends that fall within this paradigm is LBA, a.k.a. Location-Based Advertising. LBA offers ads based on geographic data to enhance the relevance and engagement of advertising based on an individual's current location.

By the end of 2024, the LBA market will have grown with the help of advancements in technology and changes in consumer behavior.

In this article, we discuss the latest data, trends, and insights related to Location Based Advertising statistics in 2025.

Online Advertising Market Outlook

The online advertising market has witnessed significant growth over the past decade and is poised to continue on this trajectory, with a global market size valued at approximately USD 378.16 billion in 2023. It is projected to reach a staggering USD 1,081.22 billion by 2032, growing at a robust compound annual growth rate (CAGR) of 12.5% during the forecast period. The proliferation of digital platforms, coupled with the ever-increasing penetration of the internet and mobile devices, has propelled this growth, making online advertising a crucial component of marketing strategies worldwide. The shift from traditional media to digital channels has been a significant driver, as businesses recognize the unparalleled reach and efficiency offered by online advertising.

Several factors contribute to the rapid expansion of the online advertising market. Firstly, the growing adoption of smartphones and the increasing time consumers spend online have created a fertile ground for advertisers to reach their target audiences. The ubiquity of the internet allows for precise targeting and personalized ad experiences, enhancing user engagement and conversion rates. Furthermore, advancements in data analytics and artificial intelligence have revolutionized how advertisers understand consumer behavior, enabling them to deliver highly relevant and timely advertisements. This data-driven approach not only improves the return on investment for advertisers but also enhances the overall user experience by reducing ad fatigue.

Another key growth factor is the evolution of social media platforms as powerful advertising channels. Social media networks like Facebook, Instagram, and TikTok have become integral parts of people's lives, providing advertisers with access to large, engaged audiences. The interactive and visual nature of social media ads makes them particularly effective in capturing users' attention and fostering brand loyalty. Additionally, social media platforms offer sophisticated targeting options based on user demographics, interests, and behaviors, allowing advertisers to reach niche markets with precision. As social media continues to grow in popularity, so too will its significance as a primary avenue for online advertising.

The increasing prevalence of e-commerce and online shopping has also contributed to the growth of the online advertising market. As more consumers turn to online platforms for their shopping needs, businesses are compelled to enhance their online presence and invest in digital advertising to remain competitive. The ability to seamlessly integrate advertising with e-commerce platforms provides a direct path from advertisement to purchase, streamlining the customer journey and increasing conversion rates. Furthermore, the rise of video content and the popularity of streaming services have opened new avenues for advertisers to engage consumers through compelling and immersive ad formats.

Contextual Advertising has emerged as a vital strategy in the digital marketing landscape, allowing brands to place their ads in environments that align with the content being consumed by users. This method enhances the relevance of advertisements by ensuring they appear alongside related content, thereby increasing the likelihood of user engagement. Unlike behavioral targeting, which relies on tracking user behavior, contextual advertising focuses on the context of the content, making it a privacy-friendly option. As privacy regulations tighten, many advertisers are turning to contextual advertising as a way to maintain ad effectiveness while respecting user privacy. This approach not only improves user experience by reducing the intrusiveness of ads but also helps brands connect with audiences in a more meaningful way.

Regionally, the online advertising market presents diverse opportunities and challenges. North America, with its mature digital ecosystem and high internet penetration rates, continues to dominate the market. The region's advanced technological infrastructure and early adoption of digital marketing strategies have positioned it as a leader in online advertising. However, Asia Pacific is experiencing the fastest growth, driven by the rapid digitization of economies and the surge in internet users across countries like China and India. The increasing investments in digital infrastructure and the growing middle class in these regions are expected to further fuel market growth. Europe, Latin America, and the Middle East & Africa also present significant opportunities fo

Introduction

Targeted Advertising Statistics: Targeted advertising is a marketing strategy where ads are tailored to specific groups of people based on their interests, behaviors, demographics, or online activity. Instead of showing the same message to everyone, targeted ads aim to reach the right audience with relevant content. This makes the ads more likely to engage people and lead to sales.

However, there are challenges like privacy issues, changing regulations, and the need to keep up with shifting consumer preferences. To stay effective, companies must keep improving their targeting strategies. This article will guide you accordingly, as it includes several current trends and analyses from different insights of recent years.

Introduction

Social Media Marketing Statistics: Social media marketing is a key part of any digital marketing plan today. With over 50% of the world’s population using social media, brands need to be active on these platforms. But it’s not just about making profiles and posting content. Effective social media marketing involves keeping up with changing algorithms and trends and understanding the behaviors of your target audience. Social media’s interactive and engaging nature helps businesses connect with their audience in ways they couldn’t before.

This opens up new opportunities for engaging with people, building the brand, and doing direct marketing. We shall shed more light on Social Media Marketing Statistics through this article.

Per Local Law 83 of 2021, the Mayor's Office of Ethnic and Community Media is required to report annually on each agency's full advertising spend across all media categories, including ethnic and community (ECM), mainstream, out-of-home, social media, etc. This dataset reflects the raw data that MOECM received from City Agencies on their annual advertising spend. For more information, please visit the MOECM website.

In 2023, Google's ad revenue amounted to 264.59 billion U.S. dollars. The company generates advertising revenue through its Google Ads platform, which enables advertisers to display ads, product listings and service offerings across Google’s extensive ad network (properties, partner sites, and apps) to web users. Google advertising Advertising accounts for the majority of Google’s revenue, which amounted to a total of 305.63 billion U.S. dollars in 2023. The majority of Google's advertising revenue comes from search advertising. Google market share These revenue figures come as no surprise, as Google accounts for the majority of the online and mobile search market worldwide. As of September 2023, Google was responsible for more than 84 percent of global desktop search traffic. The company holds a market share of more than 80 percent in a wide range of digital markets, having little to no domestic competition in many of them. China, Russia, and to a certain extent, Japan, are some of the few notable exceptions, where local products are more preferred.

This dataset is extracted from the records of cosmetics advertisements filed by various county and city health bureaus. The displayed fields are limited to those open to the system, but the dataset may change due to subsequent revisions. This does not necessarily mean that the products of the subject of sanctions are illegal. Please use caution when referring to it.

Snapshot img

Online Ad Spending Market Size 2025-2029

The online ad spending market size is forecast to increase by USD 286.6 billion, at a CAGR of 11.7% between 2024 and 2029.

The market is experiencing significant shifts, with a noticeable decline in offline advertising expenditures driving more businesses towards digital channels. This trend is fueled by the increasing popularity of online video and connected TV (CTV) advertising, as consumers increasingly engage with content on digital platforms. However, this market is not without challenges. The rise of click fraud activities poses a significant threat, requiring robust fraud detection mechanisms and continuous optimization of digital ad campaigns to mitigate potential losses. Companies seeking to capitalize on the opportunities presented by this dynamic market must stay abreast of these trends and proactively address the challenges to maintain a competitive edge.
Effective strategies include investing in advanced ad fraud prevention technologies, optimizing video and CTV ad placements, and leveraging data-driven insights to create targeted and personalized campaigns. By navigating these trends and challenges, businesses can maximize their online ad spending and effectively reach their audiences in the digital realm.

What will be the Size of the Online Ad Spending Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with digital marketing strategies becoming increasingly intricate and data-driven. Entities such as website structure, voice search optimization, and search network play pivotal roles in this dynamic landscape. Artificial intelligence and machine learning are revolutionizing the way businesses approach marketing, from keyword research and content marketing to predictive analytics and decision making. Marketing technology, including SEO tools and marketing automation, enables businesses to optimize their online presence and enhance user experience. Disruptive technologies like schema markup, ad extensions, and social media advertising are reshaping consumer behavior and influencing marketing ROI. Moreover, the importance of data security and privacy in the digital age cannot be overstated.

As businesses collect and analyze vast amounts of data, data ethics and privacy policies become essential components of marketing strategies. The ongoing unfolding of market activities also encompasses the integration of marketing technology, content syndication, and SEO reporting to streamline marketing efforts and improve marketing measurement. Ad copywriting and natural language processing are crucial elements in creating engaging and effective ad campaigns. Backlink analysis and page speed optimization are essential components of SEO, while link building and conversion tracking help businesses measure the success of their digital marketing initiatives. Core web vitals and mobile optimization are vital for ensuring a seamless user experience across devices.

In the ever-evolving digital marketing landscape, businesses must stay informed and adapt to the latest trends and technologies to remain competitive. From local SEO to e-commerce SEO, marketing budgets and strategies must be agile and responsive to the continuous shifts in consumer behavior and market dynamics.

How is this Online Ad Spending Industry segmented?

The online ad spending industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Platform

  Mobile devices
  Desktops


Application

  Retail and e-commerce
  Healthcare and pharma
  Media and entertainment
  Travel and hospitality
  Others


Type

  Search Ads
  Social Media Ads
  Display Ads
  Video Ads
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Platform Insights

The mobile devices segment is estimated to witness significant growth during the forecast period.

The digital advertising landscape is undergoing significant shifts, with mobile advertising emerging as a key driver of growth. The proliferation of smartphones and increasing mobile Internet usage has led to a surge in mobile advertising spend. In 2023, global smartphone shipments reached an impressive 1.17 billion units, fueling the demand for mobile ads. Major players in the smartphone market, including Samsung Electronics, Apple, Xiaomi, Oppo, and Vivo, have reported increased shipments, indicating a strong consumer preference for mobile devices. To maximize the potential of mobile adver

Point geometry with attributes for outdoor advertising signs in East Baton Rouge Parish, Louisiana.

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.

Context

The dataset generated by an E-commerce website which sells a variety of products at its online platform. The records user behaviour of its customers and stores it as a log. However, most of the times, users do not buy the products instantly and there is a time gap during which the customer might surf the internet and maybe visit competitor websites. Now, to improve sales of products, website owner has hired an Adtech company which built a system such that ads are being shown for owner products on its partner websites. If a user comes to owner website and searches for a product, and then visits these partner websites or apps, his/her previously viewed items or their similar items are shown on as an ad. If the user clicks this ad, he/she will be redirected to the owner website and might buy the product.

The task is to predict the probability i.e. probability of user clicking the ad which is shown to them on the partner websites for the next 7 days on the basis of historical view log data, ad impression data and user data.

Content

You are provided with the view log of users (2018/10/15 - 2018/12/11) and the product description collected from the owner website. We also provide the training data and test data containing details for ad impressions at the partner websites(Train + Test). Train data contains the impression logs during 2018/11/15 – 2018/12/13 along with the label which specifies whether the ad is clicked or not. Your model will be evaluated on the test data which have impression logs during 2018/12/12 – 2018/12/18 without the labels. You are provided with the following files:

• train.zip: This contains 3 files and description of each is given below:
• train.csv
• view_log.csv

• item_data.csv

  • test.csv: test file contains the impressions for which the participants need to predict the click rate sample_submission.csv: This file contains the format in which you have to submit your predictions.

Inspiration

• Predict the probability probability of user clicking the ad which is shown to them on the partner websites for the next 7 days on the basis of historical view log data, ad impression data and user data.

The evaluated metric could be "area under the ROC curve" between the predicted probability and the observed target.

14 Datasets used in experiments contain user data of the day of online advertisements from a cross-border e-commerce enterprise from September 1st (9.01) to September 14th (9.14), 2018. Table 3 summarizes the 14 datasets. Each instance of the datasets represents the corresponding online advertisement and is described by 22 attributes.