In this blog are the latest Facebook advertising statistics that show how effective Facebook ads are now and what’s likely to happen in the future.

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.

As of September 2024, approximately ** percent of adults surveyed in the United States said they had seen or heard a social media advertisement that caused them to buy a product, while ** percent reported watching a TV commercial that led them to make a purchase. However, the shares varied depending on the interviewees' age group. Around ** percent of Gen Zers shopped after seeing a social ad, while ** percent of Gen Xers did so after watching a TV commercial.

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.

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.

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.

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.

During a 2023 survey, ** percent of responding marketers from across the world stated they often or sometimes used Facebook ads in their work. According to the results of this survey, Facebook was the most used ad platform. Instagram ranked second, with ** percent of respondents saying they often or sometimes used ads on this platform.

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

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.

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.

36.8% of the entire world’s population uses Facebook at least once per month.

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.

Around *************** consumers interviewed in a 2024 survey considered getting recommendations from a brand based on past purchases cool. Shoppers also liked to receive personalized offers and email reminders about an abandoned shopping cart. On the other hand, ** percent of respondents stated that ads based on location data were creepy.

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.

People think of paid advertising as the holy grail of digital marketing. The truth is that paid ads can deliver excellent results for your business very quickly. Here are some digital marketing statistics about paid advertising to keep in mind.

Dataset for content analysis published in "Hornikx, J., Meurs, F. van, Janssen, A., & Heuvel, J. van den (2020). How brands highlight country of origin in magazine advertising: A content analysis. Journal of Global Marketing, 33 (1), 34-45."*Abstract (taken from publication)Aichner (2014) proposes a classification of ways in which brands communicate their country of origin (COO). The current, exploratory study is the first to empirically investigate the frequency with which brands employ such COO markers in magazine advertisements. An analysis of about 750 ads from the British, Dutch, and Spanish editions of Cosmopolitan showed that the prototypical ‘made in’ marker was rarely used, and that ‘COO embedded in company name’ and ‘use of COO language’ were most frequently employed. In all, 36% of the total number of ads contained at least one COO marker, underlining the importance of the COO construct.*Methodology (taken from publication)SampleThe use of COO markers in advertising was examined in print advertisements from three different countries to increase the robustness of the findings. Given the exploratory nature of this study, two practical selection criteria guided our country choice: the three countries included both smaller and larger countries in Europe, and they represented languages that the team was familiar with in order to reliably code the advertisements on the relevant variables. The three European countries selected were the Netherlands, Spain, and the United Kingdom. The dataset for the UK was discarded for testing H1 about the use of English as a foreign language, as will be explained in more detail in the coding procedure.The magazine Cosmopolitan was chosen as the source of advertisements. The choice for one specific magazine title reduces the generalizability of the findings (i.e., limited to the corresponding products and target consumers), but this magazine was chosen intentionally because an informal analysis suggested that it carried advertising for a large number of product categories that are considered ethnic products, such as cosmetics, watches, and shoes (Usunier & Cestre, 2007). This suggestion was corroborated in the main analysis: the majority of the ads in the corpus referred to a product that Usunier and Cestre (2007) classify as ethnic products. Table 2 provides a description of the product categories and brands referred to in the advertisements. Ethnic products have a prototypical COO in the minds of consumers (e.g., cosmetics – France), which makes it likely that the COOs are highlighted through the use of COO markers.Cosmopolitan is an international magazine that has different local editions in the three countries. The magazine, which is targeted at younger women (18–35 years old), reaches more than three million young women per month through its online, social and print platforms in the Netherlands (Hearst Netherlands, 2016), has about 517,000 readers per month in Spain (PrNoticias, 2016) and about 1.18 million readers per month in the UK (Hearst Magazine U.K., 2016).The sample consisted of all advertisements from all monthly issues that appeared in 2016 in the three countries. This whole-year cluster was selected so as to prevent potential seasonal influences (Neuendorf, 2002). In total, the corpus consisted of 745 advertisements, of which 111 were from the Dutch, 367 from the British and 267 from the Spanish Cosmopolitan. Two categories of ads were excluded in the selection process: (1) advertisements for subscription to Cosmopolitan itself, and (2) advertisements that were identical to ads that had appeared in another issue in one of the three countries. As a result, each advertisement was unique.Coding procedureFor all advertisements, four variables were coded: product type, presence of types of COO markers, COO referred to, and the use of English as a COO marker. In the first place, product type was assessed by the two coders. Coders classified each product to one of the 32 product types. In order to assess the reliability of the codings, ten per cent of the ads were independently coded by a second coder. The interrater reliability of the variable product category was good (κ = .97, p < .000, 97.33% agreement between both coders). Table 2 lists the most frequent product types; the label ‘other’ covers 17 types of product, including charity, education, and furniture.In the second place, it was recorded whether one or more of the COO markers occurred in a given ad. In the third place, if a marker was identified, it was assessed to which COO the markers referred. Table 1 lists the nine possible COO markers defined by Aichner (2014) and the COOs referred to, with examples taken from the current content analysis. The interrater reliability for the type of COO marker was very good (κ = .80, p < .000, 96.30% agreement between the coders), and the interrater reliability for COO referred to was excellent (κ = 1.00, p < .000).After the independent assessments of the two...

According to Cognitive Market Research, the global Mobile Advertising Market was valued at approximately USD XX billion in 2025 and is expected to grow to USD XX billion by 2031, expanding at a CAGR of XX% during the forecast period.

North America held largest share of XX% in the year 2025. Europe held share of XX% in the year 2025. Asia-Pacific held significant share of XX% in the year 2025. South America held significant share of XX% in the year 2025. Middle East and Africa held significant share of XX% in the year 2025. Market Dynamics Key Drivers

Consumer behavior – The mobile first is a key driver in the mobile advertising market

Consumer behavior has increasingly shifted towards a mobile-first approach, driven by the widespread use of smartphones and the availability of mobile internet. This surge in mobile connectivity has transformed consumer behavior with individuals increasingly relying on smartphones for daily activities like shopping, entertainment and financial transactions. The convenience and portability of mobile devices have them a preferred medium for tapping into digital services which has led businesses to adopt mobile-first strategies to engage with consumers.

For instance,

As of 2023, over half (54%) of the global population use smartphones. 
49% of the global population use mobile internet on smartphones.

(Source:https://www.gsma.com/newsroom/press-release/smartphone-owners-are-now-the-global-majority-new-gsma-report-reveals/)

This shift has led to an increase in mobile ad spending. Advertisers are no focusing on campaigns designed for smaller screens and shorter attention spans to reach a wider audience.

The rise of social media is driving the growth of mobile advertising

The growth of mobile advertising is heavily influenced by the rise of social media and its increased usage on mobile devices. Social media platforms such as Facebook, Instagram, Twitter have become major hubs for mobile advertising.

For instance,

 5.24 billion use social media worldwide, as of January, 2025.Facebook remains to be the leading social media platform with over 3 billion monthly active users, followed by YouTube with 2.5 billion and Instagram with 2 billion monthly active users.
90% of consumers rely on social media to keep up with trends and cultural moments and nearly half of them interact with brands more often on social media platforms. 

(Source: https://backlinko.com/social-media-users)

https://sproutsocial.com/insights/social-media-statistics/#social-media-usage-statistics)

The rise of in-app advertising across these platforms with the growing popularity of video and interactive ad formats has further fueled the market growth. Such advertising also leverages data to optimize targeting and engagement, leading to more effective campaigns. Paid ads now dominate social feeds.

For instance, the total spend on social media advertising is expected to reach $276 billion in 2025. It is projected that more than 80% of this spend will be generated though mobile by 2030.

(Source:https://sproutsocial.com/insights/social-media-statistics/#social-media-advertising-statistics-and-roi)

Key Restraints

Data privacy concerns to hinder mobile advertising market

Mobile phones have become a personal hub for information. With the increasing amount of sensitive data stored on these devices, privacy concerns have emerged as one of the most pressing issues. These concerns significantly hinder mobile advertising by leading to consumer distrust, ad avoidance and increased regulations that impact the effectiveness and reach of ad campaigns.

Consumers are becoming more aware of how their data is being collected and used under the context of mobile advertising, making them actively avoid interacting with ads and uninstalling apps they believe violate their privacy. This has also led to widespread adoption of ad-blocking technologies.

For instance, as of 2024, 43% of global internet users use ad-blocking tools with mobile device users accounting for 63% of them. 

(Source: https://seosandwitch.com/new-ad-blocking-stats/)

This trend is largely driven by the growing desire to protect personal data. However, the same has had implications on advertisers. Regulatory development amid these concerns further add to the challenges faced by the mobile industry market. Strict data privacy laws have b...

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.

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.