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.

The advertising landscape during the Super Bowl in the past years has been fairly steady, with numbers of ads oscillating roughly between 80 and 100, and total advertising time around 50 minutes. In 2020, 70 commercials were shown during the Super Bowl broadcast, amounting to a total ad time of 46 minutes. The majority of TV commercials airing during the Super Bowl are 30 seconds long, with some stretching out to a minute or longer, but 30 seconds remains the norm and few ads are shorter than that. How much is spent on TV advertising during Super Bowl?   In the past three years, Super Bowl ads generated more than 300 million U.S dollars in revenue, and in 2020 specifically the figure amounted to 449 million. The average cost of a 30-second spot during that year's game was 5.6 million U.S. dollars. The list of those who were willing to pay that price includes some prominent FMCG names, such as Anheuser-Busch, as well as major auto makers like Volksvagen.
Is spending on Super Bowl ads worth it?   It is a popular opinion among adults in the United States that Super Bowl commercials are good entertainment - the majority of surveyed Americans perceive them that way. What is more, some 19 percent indicate that Super Bowl advertising makes them aware of advertiser brands, which fortunately means that companies can count on exposure to prospective customers. Therefore, Super Bowl advertising is definitely effective.

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 graph shows the share of adults who pay attention to commercials in the United States as of April 2019, by age group. During the survey it was found that ** percent of respondents aged between 30 and 44 said they generally paid attention to commercials, compared to ** percent who said they did not.

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

Podcast Advertising Statistics: Podcast advertising refers to media advertising that tends to take place in a podcast episode. It has emerged as a dynamic and rapidly growing media landscape, transforming how audiences consume audio content. By going through Podcast Advertising Statistics, we can learn about global podcast trends, explore revenue streams, listener demographics, market dynamics, and the evolving ecosystem of digital audio entertainment across different countries and platforms, and understand the aspects that have promoted it to become a popular media for advertising.

In 2024, digital advertising accounted for approximately ** percent total media advertising spending in Latin America. The share was highest in North America, where it stood at **** percent.

Snapshot img

Programmatic Advertising Spending Market Size 2025-2029

The programmatic advertising spending market size is forecast to increase by USD 892.7 billion, at a CAGR of 38.1% between 2024 and 2029.

The market is experiencing significant shifts, driven by the increasing trend toward online shopping and the high penetration of augmented reality (AR) technology in the advertising sector. Consumers' preference for digital channels has led to a surge in programmatic ad spending, providing businesses with unprecedented opportunities to reach their audiences effectively. AR technology, with its ability to create immersive and interactive ad experiences, is revolutionizing the way brands engage consumers, further fueling market growth. However, the market faces challenges that necessitate strategic navigation.
The low transparency in the programmatic advertising ecosystem poses a significant obstacle for marketers, making it difficult to assess the effectiveness and value of their ad spend. Addressing this challenge through increased transparency and accountability measures will be crucial for businesses looking to optimize their programmatic advertising strategies and maximize their returns.

What will be the Size of the Programmatic Advertising 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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Programmatic advertising spending continues to evolve, driven by the intersection of real-time data, automation, and advanced technologies. Cross-device tracking and performance measurement enable advertisers to reach consumers consistently across various touchpoints. Real-time bidding (RTB) and header bidding allow for efficient and effective campaign management, while frequency capping and demographic targeting ensure brand safety and audience segmentation. Machine learning algorithms and predictive analytics optimize ad creative and audience engagement, driving conversions. Social media advertising and private marketplaces offer new opportunities for reach and transparency. Programmatic direct and first-party data enable data-driven decision making, enhancing agile marketing strategies. Transparency and accountability remain crucial, with ad fraud detection and brand safety measures evolving to address emerging challenges.

Contextual targeting and targeting options expand reach and relevance, while supply-side platforms and ad exchanges facilitate the buying and selling of ad inventory. The programmatic landscape continues to unfold, with digital out-of-home (DOOH), native advertising, and automated optimization shaping the future of programmatic advertising. Artificial intelligence (AI) and real-time data further enhance the capabilities of programmatic platforms, enabling more effective and efficient advertising strategies.

How is this Programmatic Advertising Spending Industry segmented?

The programmatic advertising 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.

Application

  Open auction
  Automated guaranteed
  Invitation-only
  Unreserved fixed-rate


Type

  Mobile
  Desktop


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

.

By Application Insights

The open auction segment is estimated to witness significant growth during the forecast period.

The market is characterized by the integration of various advanced technologies and strategies to deliver targeted and personalized ads in real-time. Cross-device tracking enables advertisers to follow consumers across multiple devices, providing a more comprehensive understanding of their behavior and preferences. Performance measurement tools help advertisers assess the effectiveness of their campaigns, while real-time data fuels real-time bidding (RTB) and automated optimization. Privacy regulations, such as GDPR and CCPA, have brought about stricter data handling practices, necessitating the use of first-party data and data-driven decision making. Demand-side platforms (DSPs) and supply-side platforms (SSPs) facilitate programmatic bidding, allowing advertisers to place bids on ad inventory in real-time.

Frequency capping, demographic targeting, and audience segmentation are crucial targeting options to ensure efficient ad delivery and minimize ad waste. Brand safety and transparency and accountability are essential considerations, with ad fraud detection and predictive analytics playing key roles in maintaining trust and confidence in the digital advertising ecosystem. Native advertising,

Commercial valuation data collected and maintained by the Cook County Assessor's Office, from 2021 to present. The office uses this data primarily for valuation and reporting. This dataset consolidates the individual Excel workbooks available on the Assessor's website into a single shared format. Properties are valued using similar valuation methods within each model group, per township, per year (in the year the township is reassessed). This dataset has been cleaned minimally, only enough to fit the source Excel workbooks together - because models are updated for each township in the year it is reassessed, users should expect inconsistencies within columns across time and townships. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. This data is property-level. Each 14-digit key PIN represents one commercial property. Commercial properties can and often do encompass multiple PINs. Additional notes: Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time. Data will be updated yearly, once the Assessor has finished mailing first pass values. If users need more up-to-date information they can access it through the Assessor's website. The Assessor's Office reassesses roughly one third of the county (a triad) each year. For commercial valuations, this means each year of data only contain the triad that was reassessed that year. Which triads and their constituent townships have been reassessed recently as well the year of their reassessment can be found in the Assessor's assessment calendar. One KeyPIN is one Commercial Entity. Each KeyPIN (entity) can be comprised of one single PIN (parcel), or multiple PINs as designated in the pins column. Additionally, each KeyPIN might have multiple rows if it is associated with different class codes or model groups. This can occur because many of Cook County's parcels have multiple class codes associated with them if they have multiple uses (such as residential and commercial). Users should not expect this data to be unique by any combination of available columns. Commercial properties are calculated by first determining a property’s use (office, retail, apartments, industrial, etc.), then the property is grouped with similar or like-kind property types. Next, income generated by the property such as rent or incidental income streams like parking or advertising signage is examined. Next, market-level vacancy based on location and property type is examined. In addition, new construction that has not yet been leased is also considered. Finally, expenses such as property taxes, insurance, repair and maintenance costs, property management fees, and service expenditures for professional services are examined. Once a snapshot of a property’s income statement is captured based on market data, a standard valuation metric called a “capitalization rate” to convert income to value is applied. This data was used to produce initial valuations mailed to property owners. It does not incorporate any subsequent changes to a property’s class, characteristics, valuation, or assessed value from appeals.Township codes can be found in the legend of this map. For more information on the sourcing of attached data and the preparation of this datase

Global ad spend were expected to reach over $134 billion in 2022. This means that it has increased by over 17% yearly.

Spending on online advertising has surged, and it has become the preferred advertising medium over traditional channels like TV and print. This has been driven by a significant shift in consumer behaviour towards the internet, social media and online shopping, which consumers became more accustomed to during the pandemic. Advertising agencies are navigating increasing privacy concerns and stricter regulations, highlighted by the $60.0 million fine against Google for misleading data practices. Profitability has expanded as companies adopt artificial intelligence, with more than one-quarter of Google's code now being AI-generated and major companies like Facebook reducing labour costs through significant workforce cuts. Industrywide revenue has been climbing at an annualised 8.2% over the past five years and is expected to total $17.1 billion in 2024-25, when revenue will climb by 5.7%. The Online Advertising industry exhibits high market share concentration because of the substantial barriers to entry and the dominance of major players Google and Facebook. Google leads the search engine market, controlling around 95%, largely because it is the default search engine on popular browsers like Chrome and Safari. Access to large user volumes is crucial for online advertisers, as it encourages companies to increase spending on online ads. Extensive user data is also essential for training algorithms to deliver targeted advertising, enabling firms like Google, REA Group and Facebook Australia to charge higher premiums for their services. This data advantage, international firms' larger budgets and fewer regulatory constraints make it challenging for domestic companies to compete. The Online Advertising industry is on track to continue expanding, although at slower rates. Privacy concerns and stricter data usage regulations are set to limit advertisers' access to consumer data, especially with major web browsers' phasing out of third-party cookies. This will compel advertisers to innovate and emphasise first-party data by creating engaging, interactive experiences to encourage users to share information willingly. Adopting artificial intelligence technologies will enable advertisers to optimise ad placements, better understand user behaviour and reduce labour dependence. Industry revenue is forecast to expand at an annualised 6.8% through 2029-30 to total $23.8 billion.

This data set contains pounds and value for all seafood products that are landed and sold by established seafood dealers and brokers in the SE Region of the US mainland. In the US Caribbean, the landings are reported by permitted fishers. These types of data, referred to as the general canvass landings statistics, have been collected by the NOAA Fisheries Service, National Marine Fisheries Service and its predecessor agency, the Bureau of Commercial Fisheries. These data are available on computer since the mid 1920s. The quantities and values that are reported in this data set include monthly landings that were initiated in 1972. Between 1926 and 1971, data were collected annually and not monthly. Mixed annual and monthly data occur from 1972-1976 according to State and year. The general canvass landings include quantities and value for all commercially caught marine species and are identified by species or species group. These data are collected from or reported by every seafood dealer or broker that is licensed by each state in the Southeast Region (North Carolina through Texas). In addition, information on the gear and area of capture is available for most of the landings statistics in the data set except for Florida 1977-1996 and Louisiana 1992-1999. However, because these data are summaries, they do not contain information on the quantities of fishing effort or identifications of the fishermen or vessels that caught the fish or shellfish. In early years, these data were collected by field agents employed by the Southeast Fisheries Science Center and assigned to local fishing ports. These individuals would canvass the seafood dealers and record the quantity and value for each species or species category from the sales receipts maintained by the seafood dealers. Based on their detailed knowledge of the fishing activity in the area, the agents would estimate the type of fishing gear and area where the fishing was likely to have occurred. It should be noted that landings by gear and water body (fishing area) does not reside in the monthly landings data set for Florida for the years, 1977-1995, Louisiana 1990-1999, and Texas (for gear) from 1993 to present (this is subject to change for years 2008 and more recent). Annual landings by gear, water body and distance from shore are available in the Annual General Canvass data for Florida. More detailed information on the caveats associated with these data is provided in the Issues section. In more recent years, the states in the Southeast Region began to implement trip ticket programs that required the licensed seafood dealer/brokers to report the landings of all seafood products. A trip ticket program was initiated in Florida in 1985, in North Carolina in 1994, in Louisiana in 1999, in Alabama in 2000, and in Texas in 2007. In addition to the quantities of these landings, the states require dealers to report the price, the type of gear and the fishing area for each trip. Through cooperative agreements with each of the states, monthly summaries of the states trip ticket programs are provided to the Southeast Fisheries Science Center (SEFSC) and are included in the general canvass landings data set. In addition, summarized data are extracted from the NOAA-SEFSC Gulf Shrimp System for commercial landings of shrimp species that are landed at port in the coastal area of the Gulf of Mexico.

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.

As per newly released data by Future Market Insights (FMI), the global salеs of the nativе advеrtising industry is еstimatеd to be worth USD 103,229.3 million in 2025 and anticipatеd to reach a value of USD 733,315.8 million by 2035. Salеs arе projеctеd to risе at a CAGR of 21.7% ovеr thе forеcast pеriod bеtwееn 2025 and 2035. Share of native advertising market in its parent market (global advertising market) is 20% to 25%.

Semi-Annual Industry Update

This dataset is about books. It has 226 rows and is filtered where the book subjects is Commercial statistics. It features 9 columns including author, publication date, language, and book publisher.

Advertising agencies have benefitted from rising consumer spending, corporate profit and per capita disposable income. Despite the outbreak of COVID-19 and falling advertising expenditure in 2020, as corporate profit returned in the following years, agencies were able to capitalize on an explosion of pent-up demand as businesses targeted a consumer base with growing disposable income. As traditional media advertising expenditures decline, digital ad spending has captured the spotlight. Consumer behavior, increasingly leaning towards online platforms and mobile devices, has reshaped advertising strategies, pushing agencies to focus on digital-first approaches. Industry-wide revenue has been growing at a CAGR of 3.6% over the past five years and is expected to total $78.2 billion in 2025, when revenue will jump by an estimated 1.6% and profit will inch forward to 8.7%. The industry has benefited from the growth of digital media, encouraging investment in online ads. The pivot towards digital, hastened by the pandemic, saw businesses diverting budgets from traditional media to more agile and nuanced digital platforms. The rising demand for digital services motivated more companies to invest in advertising since audiences are more fragmented now than ever. A more fragmented audience requires clients to purchase advertising space on an increasing number and types of platforms to achieve a wide-reaching message. Increased data analytics and programmatic buying proficiency enable agencies to craft more targeted, measurable campaigns. Companies like Omnicom have adopted aggressive acquisition strategies to fortify their digital capabilities. In the future, advertising agencies will continue to enjoy growth, driven by solid increases in advertising expenditure. As companies adapt to benefits from the development of digital platforms, clients will seek integrated marketing solutions that combine multiple media platforms, resulting in a greater need for advertising agencies. New forms of advertising will continue to promote growth. With the dominance of mobile advertising and the prominence of connected TV due to cord-cutting trends, agencies are set to delve deeper into emerging video formats and artificial intelligence. Viral marketing will keep profit stable for advertising agencies since there are minimal costs once the advertisement is online. Privacy concerns and regulatory shifts, such as third-party cookie deprecation, will push agencies towards more privacy-centric ad models, with first-party data becoming crucial. Despite these challenges, the industry is poised for growth, driven by burgeoning corporate ad budgets and per capita disposable income. Industry revenue is forecast to mount at a CAGR of 2.6% through the end of 2029 to total $88.9 billion.

Graph and download economic data for Producer Price Index by Industry: Commercial Printing, Except Screen and Books: Advertising Printing (Lithographic) (PCU32311K32311K09) from Jun 1982 to May 2025 about advertisement, book, printing, commercial, PPI, industry, inflation, price index, indexes, price, and USA.

Japan CSPI: Advertising: TV and Ratio Advertising: TV Commercials data was reported at 112.100 1990=100 in Oct 1999. This records an increase from the previous number of 106.200 1990=100 for Sep 1999. Japan CSPI: Advertising: TV and Ratio Advertising: TV Commercials data is updated monthly, averaging 103.800 1990=100 from Jan 1990 (Median) to Oct 1999, with 118 observations. The data reached an all-time high of 115.900 1990=100 in Mar 1999 and a record low of 89.300 1990=100 in Aug 1993. Japan CSPI: Advertising: TV and Ratio Advertising: TV Commercials data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.I046: Corporate Service Price Index: 1990=100.

This table contains 138 series, with data for years 1961 - 2001 (not all combinations necessarily have data for all years), and was last released on 2007-03-06. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Employment measures (2 items: Number of employee jobs; Hours worked for employee jobs ...), Non-business sector industries, by aggregation (69 items: Non-commercial sector industries; special aggregate; Non-commercial sector - goods; special aggregate; Non-commercial sector - services; special aggregate; Agricultural and related service industries; S-level aggregation ...).

This table contains information about commercial properties including number of stories, elevators, exterior wall type, floor type and roof type for commercial properties within Fairfax County. There is a one to many relationship to the parcel data. Refer to this document for descriptions of the data in the table.