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.

To demonstrate discovery, measurement, and mitigation of bias in advertising, we provide a dataset that contains synthetic generated data for users who were shown a certain advertisement (ad). Each instance of the dataset is specific to a user and has feature attributes such as gender, age, income, political/religious affiliation, parental status, home ownership, area (rural/urban), and education status. In addition to the features we also provide information on whether users actually clicked on or were predicted to click on the ad. Clicking on the ad is known as conversion, and the three outcome variables included are: (1) The predicted probability of conversion, (2) Predicted conversion (binary 0/1) which is obtained by thresholding the predicted probability, (3) True conversion (binary 0/1) that indicates whether the user actually clicked on the ad.

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.

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).

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.

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.

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.

The marketing budget dataset captures the sales revenue generated with respect to investments in different marketing strategies including phone calls, emails, flyers, and salespersons' time.

It is required to understand the impact of marketing budgets on overall sales.

Acknowledgment: The dataset is taken from AbsentData

Objective: Understand the Dataset & cleanup (if required). Build Regression models to generate more successful marketing budgets.

A large commercial Ads Dataset includes 480K labeled query-ad pairwise data with structured information of image, title, seller, description, and so on.

At VisitIQ™, we provide a wealth of consumer marketing data to help businesses unlock deeper insights and optimize their B2C strategies. Our extensive and meticulously curated datasets are designed to provide a 360-degree view of your target consumers, combining a wide range of behavioral, demographic, and psychographic data points to deliver actionable insights that drive measurable results.

Our comprehensive consumer marketing database is built to fuel data-driven marketing strategies. With our rich behavioral insights, you can understand not just who your customers are, but also how they interact with your brand, what they are looking for, and what motivates their purchasing decisions. By tracking online and offline behaviors, preferences, purchase history, and engagement patterns, VisitIQ™ enables you to segment your audience more effectively and craft personalized marketing messages that resonate with your ideal customer profiles.

In addition to behavioral insights, our datasets provide detailed demographic information, including age, gender, location, income level, education, and household characteristics. This allows you to pinpoint your marketing efforts with incredible precision, reaching the right audience with the right message at the right time. Our data also includes psychographic attributes, such as lifestyle preferences, interests, and values, providing a deeper understanding of what drives consumer behavior and helping you create more compelling and relevant content.

VisitIQ's™ platform integrates seamlessly with your existing marketing stack, enabling you to utilize our consumer marketing data across multiple channels, from digital and social media to email and direct mail. With our data, you can improve targeting, increase engagement, reduce customer acquisition costs, and ultimately achieve a higher return on your marketing investment.

Whether you’re looking to attract new customers, retain existing ones, or re-engage lapsed consumers, VisitIQ™ provides the data you need to build effective, data-driven B2C marketing strategies. Our comprehensive datasets empower you to make informed decisions, optimize your marketing campaigns in real-time, and drive successful outcomes.

Unlock the full potential of your consumer marketing efforts with VisitIQ™. Transform your approach with powerful insights, sharpen your competitive edge, and achieve unparalleled marketing success.

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

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

During a July 2024 survey among marketers worldwide, 56 percent of respondents included connected TV (CTV) and streaming among the most important consumer trends they were watching for the second half of that year. Generative artificial intelligence (GenAI) followed closely, mentioned by 55 percent, while TikTok and social video rounded up the top three with a share of 47 percent. Generative AI in marketing Next to effective use cases of AI, such as aligning web content with search intent and improving the consumer experience on websites, AI tools in marketing are used for creative production. For example, influencers worldwide stated they were using tools such as Canva and DALL-E to generate images for their social media accounts. Moreover, entire ad campaigns exist that have been produced by prompting generative AI for creative purposes. TikTok for marketing The short-video format of TikTok has taken the scene by storm. In 2023, the Chinese platform generated solid engagement rates for all the various influencer tiers – from nano to mega. As of April 2023, TikTok was the leading global unicorn – a start-up company with a value of over one billion U.S. dollars –followed by Musk’s SpaceX. However, multiple worldwide ban discussions revolve around the social media due to its highly engaging, or as some may deem addictive, character.

Online Advertising Market Outlook

According to our latest research, the global online advertising market size reached USD 536.5 billion in 2024, with a robust year-over-year growth trajectory. The market is expected to register a CAGR of 10.2% from 2025 to 2033, ultimately reaching a projected value of USD 1,288.4 billion by 2033. This expansion is being propelled by the rapid digitization of businesses, rising internet penetration, and the increasing shift of advertising budgets from traditional to digital channels. The online advertising market's dynamic nature and adaptability to emerging technologies are key drivers of its sustained growth.

One of the primary growth factors for the online advertising market is the exponential increase in internet users globally, which has directly contributed to the surge in digital content consumption. As of 2024, more than 5.1 billion people are active internet users, representing a vast audience for targeted advertising. The proliferation of smartphones and affordable data plans has made it easier for advertisers to reach consumers at any time and place, further enhancing the efficacy of online campaigns. Moreover, the rise of social media platforms, streaming services, and e-commerce websites has created a plethora of opportunities for brands to engage with their target demographics. These trends underscore the market's evolution, as advertisers continue to leverage data-driven insights and advanced analytics to optimize campaign performance and maximize return on investment.

Another critical driver is the advancement of programmatic advertising technologies, which have revolutionized the way digital ads are bought, placed, and optimized. Programmatic platforms utilize artificial intelligence and machine learning algorithms to automate the ad buying process, enabling real-time bidding and precise audience targeting. This has resulted in higher efficiency, reduced costs, and improved campaign outcomes for advertisers. Additionally, the integration of big data analytics allows for granular segmentation and personalization, ensuring that ads are delivered to the most relevant audiences. As privacy regulations evolve and third-party cookies phase out, advertisers are increasingly investing in first-party data strategies and contextual targeting methods to maintain campaign effectiveness and compliance.

The online advertising market also benefits from the growing adoption of innovative ad formats and immersive technologies, such as augmented reality (AR) and virtual reality (VR) ads. Brands are experimenting with interactive experiences, shoppable videos, and influencer collaborations to capture consumer attention and drive engagement. The rise of connected TV (CTV) and over-the-top (OTT) platforms has opened new avenues for video advertising, enabling brands to reach cord-cutters and younger audiences who are shifting away from traditional television. Furthermore, the integration of e-commerce functionalities within social and video platforms is blurring the lines between content and commerce, creating seamless shopping experiences that drive conversions and boost advertiser revenues.

Regionally, North America continues to dominate the online advertising market, accounting for the largest share in 2024, followed closely by Asia Pacific and Europe. The United States, in particular, remains a global leader due to its mature digital ecosystem, high ad spend per capita, and early adoption of advanced advertising technologies. Asia Pacific is witnessing the fastest growth, driven by the rapid expansion of internet infrastructure, rising smartphone adoption, and increasing digital literacy in countries such as China, India, and Southeast Asian nations. Europe maintains strong momentum, supported by robust regulatory frameworks and a diverse mix of established and emerging markets. Meanwhile, Latin America and the Middle East & Africa are experiencing steady growth, albeit from a lower base, as more businesses embrace digital transformation and online advertising strategies.

Format A

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.

Dataset Summary

The Programmatic Ad Creatives dataset contains 1000 samples of online programmatic ad creatives along with their ad sizes. The dataset includes 8 unique ad sizes, such as (300, 250), (728, 90), (970, 250), (300, 600), (160, 600), (970, 90), (336, 280), and (320, 50). The dataset is in a tabular format and represents a random sample from Project300x250.com's complete creative data set. It is primarily used for training and evaluating natural language processing models… See the full description on the dataset page: https://huggingface.co/datasets/PeterBrendan/Ads_Creative_Text_Programmatic.

E-commerce sales for North American Industry Classification System (NAICS) Advertising, public relations, and related services, includes all members under sales, for Canada, for one year of data.

On September 30, 2020, AB 323 (Ch. 341, Stats. 2020) was passed and chaptered into law thereby establishing the requirement for DGS to post on its Internet website purchases related to marketing and outreach advertising materials for every state office, officer, department, division, bureau, board, and commission identified in GC 11000, and including the California State University. These purchases must be further disaggregated to show placement of marketing and outreach advertising materials targeting communications with specific ethnic communities including but not limited to Latino, African American, Asian-Pacific Islander, Indigenous, Middle Eastern and LGTBQIA communities as outlined by Public Contract Code 11800-11804.

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.

This dataset contains detailed user interaction data from various immersive advertising formats, including 3D, AR, and 2D ads. It captures essential metrics such as clicks, time spent, engagement scores, and user demographics, along with the type of device used (mobile, desktop, tablet). The dataset also includes additional information like conversion rates, bounce rates, and click-through rates (CTR), all of which are valuable for analyzing and optimizing interactive ad performance. Visual complexity and user movement data (e.g., gaze, movement) are also included to assess how different immersive elements influence user engagement.

The dataset is designed to support the development of models that predict user engagement based on various ad features and help optimize immersive ad designs for enhanced interaction outcomes. It can be used to explore the effectiveness of different ad types and user behaviors, making it a useful resource for digital marketers, advertisers, and machine learning researchers focused on advertising technology and user experience optimization.