https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11099966%2F1d8bf9ccdd3cd0939c15e14ac1db67e3%2Fdataset.png?generation=1681933163057358&alt=media" alt=""> The data set below shows the result after the launch of a series of advertising campaigns, the characteristics of each one are described below. Tables descriptions:

“Table 1” contains advertising data for the first platform and has the ads_device level of granularity. Fields: - date (dimension) - join key, date when data was published at the platform. - account_id (dimension) - join key, ID of the advertising account - campaign_id (dimension) - join key, ID for the advertising campaign - campaign_name (property) - advertising campaign name - adset_id (dimension) - join key, ID for the group of ads - adset_name (property) - name of the group of ads - ad_id (dimension) - join key, ID for the ad - ad_name (property) - name of the ad - ad_type (property) - type of ad - device (dimension) - device type where the impression was shown. - spend (metric) - amount of fact budget - clicks (metric) - amount of clicks - impressions (metric) - amount of impressions - conversions (metric) - amount of conversions

Additional part: 1. Table 1 needs to add additional fields provider as text “Platform 1”, network as text “channel 1” - channel of data for first platform. 2. campaign_name field has the following structure: “_CN|{campaign_name_short}_BR|{brand}_FF|{free_field}” need to parse campaign_name_short, brand, free_field properties to include them in the final table. 3. In the final table also should be included field adset_group which can be extracted from adset_name field with the structure: “{adset_group} | {text 1} | {text 2}”

“Table 2” contains advertising data for the second platform and has the same ads_device level of granularity. Fields: - date (dimension) - join key, date when data was published at the platform. - account_id (dimension) - join key, ID of the advertising account - campaign_id (dimension) - join key, ID for the advertising campaign - campaign_name (property) - advertising campaign name - adset_id (dimension) - join key, ID for the group of ads - adset_name (property) - name of the group of ads - ad_id (dimension) - join key, ID for the ads - ad_type (property) - type of ads - device (dimension) - device type where the impression was shown. - spend (metric) - amount of fact budget - clicks (metric) - amount of clicks - impressions (metric) - amount of impressions - conversions (metric) - amount of conversions

Additional part: 1. “Table 2” needs to add additional fields provider as text “Platform 2”, network as text “channel 2” - channel of data for second platform. 2. campaign_name has the following structure: “_CN|{campaign_name_short}_BR|{brand}_FF|{free_field}” need to parse campaign_name_short, brand, free_field properties to include them in the final table. 3. In the final table also should be included field adset_group which can be extracted from adset_name field with the structure: “{adset_group} | {text 1} | {text 2}”.

“Table 3” contains missing properties for the “Table 1” for the first platform. Fields: - date (dimension) - join key, date when data was published at the platform. - account_id (dimension) - join key, ID of the advertising account - campaign_id (dimension) - join key, ID for the advertising campaign - campaign_name (property) - advertising campaign name - adset_id (dimension) - join key, ID for the group of ads - ad_id (dimension) - join key, ID for the ads - headline 1 (property) - the first part of an expanded text ad headline in the ad form - headline 2 (property) - the second part of an expanded text ad headline in the ad form - headline 3 (property) - the third part of an expanded text ad headline in the ad form - description (property) - the descriptive text of an expanded text ad or responsive display ad - final_url (property) - final URLs of the ad - path1 (property) - the text that appears in the ad with the displayed URL for an expanded text ad - path2 (property) - in addition to "Path1", more text that appears in the ad with the displayed URL for an expanded text ad.

“Table 4” contains missing properties for the “Table 2” for the second platform.

Fields: - account_id (dimension) - join key, ID of the advertising account - campaign_id (dimension) - join key, ID for the advertising campaign - adset_id (dimension) - join key, ID for the group of ads - ad_id (dimension) - join key, ID for the ads - headline 1 (property) - the first part of an expanded text ad headline in the ad form - headline 2 (property) - the second part of an expanded text ad headline in the ad form - text (property) - the descriptive text of an expanded text ad or responsive display ad - destination_url (property) - final URLs of the ad

**“Table 5” contains data from the third platform...

In an annual assessment of advertising campaigns, Cadbury's “Shah Rukh Khan-My-Ad” was voted the world’s most effective campaign of 2024, with ***** points. Meanwhile, Whisper's "Keep Girls in School" ranked second with **** points.

During a 2023 survey carried out among media strategists, planners and buyers from North America who worked on programmatic campaigns, it was found that behavioral and interest/intent data were third-party data types used most in digital advertising campaigns, both named by ** percent of respondents. Demo and lifestyle data followed, mentioned by ** and ** percent, respectively.

The most effective advertising campaign in the United States in 2024 was Dove's "The Cost of Beauty," produced by Ogilvy London, Ogilvy Toronto, Ogilvy New York, Mindshare New York, Mindshare Toronto, and Hogarth London, with an index score of ****. The beauty brand was followed by Oreo and its "OreoCodes" commercial, produced by VML Commerce New York / VML New York.

This dataset captures user engagement with social media advertisements, providing insights into how different demographics interact with online ads. It includes various attributes related to users, ad content, and engagement metrics, making it suitable for machine learning tasks such as ad performance prediction, personalized recommendations, and sentiment analysis.

The target column (engaged) indicates whether a user interacted with an ad (1 for engagement, 0 for no engagement), making it ideal for classification tasks.

Key Features: User Demographics:

user_id: Unique identifier for each user

age: Age of the user

gender: Gender of the user (Male, Female, Other)

location: User’s geographic region

Ad Characteristics:

ad_id: Unique identifier for each ad

ad_type: Type of advertisement (Image, Video, Text, Carousel)

ad_duration: Length of the ad (in seconds, for video ads)

ad_category: Category of the advertisement (e.g., Fashion, Technology, Food)

Engagement Metrics:

clicks: Number of times the user clicked on the ad

likes: Number of likes the ad received from the user

shares: Number of times the ad was shared by the user

view_time: Time spent viewing the ad (in seconds)

Behavioral Attributes:

previous_interactions: Number of past interactions with similar ads

device_type: Device used to view the ad (Mobile, Desktop, Tablet)

time_of_day: Time when the user viewed the ad (Morning, Afternoon, Evening, Night)

Target Column:

engaged: Binary target variable (1 = User engaged, 0 = No engagement)

The general taxonomy contains a default scope of data related topics, based on the user's browser and mobile app activity through last 30 days. There are classical Demographic, purchase interests, intentions.

How you can use our data?

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

Dataset Overview

This dataset provides a detailed analysis of the advertising spending across different media channels and its impact on sales. Designed for marketing analysts, data scientists, and business strategists, this dataset facilitates understanding how different advertising expenditures influence sales performance, aiding in data-driven decision-making for marketing campaigns.

Key Features:

TV: Investment in TV advertising campaigns (in thousands of dollars). Radio: Investment in radio advertising campaigns (in thousands of dollars). Newspaper: Investment in newspaper advertising campaigns (in thousands of dollars). Sales: Revenue generated from sales campaigns (in thousands of dollars).

Usage Recommendations and Limitations:

Recommended Use: Suitable for economic research, marketing analysis, and predictive modeling. Limitations: Results are based on historical data and assumptions; future advertising campaigns may not follow the same trends.

This Data Card aims to provide a clear, comprehensive overview of the dataset and its potential uses in marketing and economic analysis, highlighting the pivotal role of data in strategic decision-making processes.

In 2023, direct mail's return on investment (ROI) outperformed all other presented channels with an average of *** percent. Email and paid search advertising rounded up the top three with ROIs of ** and ** percent, respectively.

The rankings looks at the performance of advertising campaigns. The index helps agencies measure the effectiveness of their campaigns as compared to their rivals. In 2023, the McCann Manchester / UM Manchester-led campaign for Aldi "Kevin versus John: How a humble carrot usurped a national treasure to win the UK’s Christmas Ad crown" was the most effective advertising campaign with an score of **** points. Advertisers continue to spend In 2021, advertisers were forecast to spend almost ***** billion British pounds in the United Kingdom. Digital ad spending has long overtaken the TV expenditures and has a good grip of four of five pounds invested in advertising in the country. The turn away from traditional spending channels and towards more digital areas has seen social media spend reach **** billion British pounds by 2019. Digital spending Digital advertising, also referred to as online, internet or web advertising, allows advertisers to bring promotional content to consumers using online technologies. It includes, among others, advertisements placed on social media platforms and search engine websites, banner ads on desktop as well as mobile websites and promotional messages delivered via email. 2021 has been estimated to see approximately **** billion British pounds in digital advertising spend in the UK.

Google Ads Sales Dataset for Data Analytics Campaigns (Raw & Uncleaned) 📝 Dataset Overview This dataset contains raw, uncleaned advertising data from a simulated Google Ads campaign promoting data analytics courses and services. It closely mimics what real digital marketers and analysts would encounter when working with exported campaign data — including typos, formatting issues, missing values, and inconsistencies.

It is ideal for practicing:

Data cleaning

Exploratory Data Analysis (EDA)

Marketing analytics

Campaign performance insights

Dashboard creation using tools like Excel, Python, or Power BI

📁 Columns in the Dataset Column Name ----- -Description Ad_ID --------Unique ID of the ad campaign Campaign_Name ------Name of the campaign (with typos and variations) Clicks --Number of clicks received Impressions --Number of ad impressions Cost --Total cost of the ad (in ₹ or $ format with missing values) Leads ---Number of leads generated Conversions ----Number of actual conversions (signups, sales, etc.) Conversion Rate ---Calculated conversion rate (Conversions ÷ Clicks) Sale_Amount ---Revenue generated from the conversions Ad_Date------ Date of the ad activity (in inconsistent formats like YYYY/MM/DD, DD-MM-YY) Location ------------City where the ad was served (includes spelling/case variations) Device------------ Device type (Mobile, Desktop, Tablet with mixed casing) Keyword ----------Keyword that triggered the ad (with typos)

⚠️ Data Quality Issues (Intentional) This dataset was intentionally left raw and uncleaned to reflect real-world messiness, such as:

Inconsistent date formats

Spelling errors (e.g., "analitics", "anaytics")

Duplicate rows

Mixed units and symbols in cost/revenue columns

Missing values

Irregular casing in categorical fields (e.g., "mobile", "Mobile", "MOBILE")

🎯 Use Cases Data cleaning exercises in Python (Pandas), R, Excel

Data preprocessing for machine learning

Campaign performance analysis

Conversion optimization tracking

Building dashboards in Power BI, Tableau, or Looker

💡 Sample Analysis Ideas Track campaign cost vs. return (ROI)

Analyze click-through rates (CTR) by device or location

Clean and standardize campaign names and keywords

Investigate keyword performance vs. conversions

🔖 Tags Digital Marketing · Google Ads · Marketing Analytics · Data Cleaning · Pandas Practice · Business Analytics · CRM Data

"Pro všechny světské radosti: Itálie" spot for Fio banka was rated as the best advertising campaign of 2024 in Czechia, with a score of ** percent. It was followed by "Nezapomeňte!" for Česká filharmonie at ** percent. Bauhaus was the only brand with two advertisements among the 10 leading advertising campaigns.

Data Description: The variables birth-year, education, income, and so on are related to the first 'P' or 'People' in the tabular data provided to the user. The amount spent on wine, fruits, gold, etc., is related to ‘Product’. The information pertinent to sales channels, like websites, stores, etc., is related to ‘Place’, and the fields which talk about promotions and results of different campaigns are related to ‘Promotion’.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific ta...

Super Bowl Ads (Year-by-Year)

Analyzing Funny, Patriotic, and Product Promotion Ads Year By Year

By data.world's Admin [source]

About this dataset

This dataset contains a comprehensive collection of Super Bowl Ads broadcasted. Our data comes from superbowl-ads.com, providing us with the URL's to watch each ad on YouTube. We have included seven defining characteristics of these advertisements - including funniness, patriotism, celebrity presence, animals featured, and use of sex to sell the product - that will offer unique insights into the cultural trends present in each year's advertising campaigns. Furthermore, this dataset implores us to ask questions about the relationship between popular culture and the kinds of ads companies have used in order to both promote their products as well as better relate with their audience through utilizing images and themes which reflect current society. With so much data available in an easily accessible format than ever before thanks to modern technology; exploring this content could give way to unprecedented opportunities for marketers who want gain an advantage in understanding their target demographic or can provide a fresh perspective for those looking consume something new

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

There are a few different ways you can use this data to uncover America’s secrets through Super Bowl ads. Let’s explore some potential uses!

• Analyze changes in the types of themes across years: By looking at the data for each year separately and trying to identify trends or similarities across years in particular themes (like funny ads or dangerous ad), you can gain an understanding of any changes in how Americans view these aspects of their entertainment. For example, is there a trend towards more funny ads? Or more patriotic ones?

• Utilize Brand Analysis: pull up all of an individual brand’s data from all years and ask what types of messages this brand has been sending throughout its Super Bowl advertising over time– Do they like animals? Are their famous people in most ads? An understanding what type brands put out will allow insight into how Americans perceive them overall.

• Analyze correlations between themes: Find correlations between different aspects by performing analyses that compare two columns at a time over multiple years; some examples may include correlation between using sex vs using animals in advertising or correlation between having a celebrity spokesperson/actor/actress vs being patriotic with ad content could also be interesting to analyze.

Research Ideas

• Creating an interactive visualization that allows users to explore the different trends surrounding Super Bowl ads over the last two decades. This could include visuals such as bar graphs, line charts and scatter plots that show how often certain characteristics are used in ads, and how these characteristics have evolved over time.

• Running a classifier model to predict which characteristics will be used in an upcoming Super Bowl ad. This could use factors such as past data from similar brands or from the same company over multiple years.

• Using the data to create a machine learning algorithm that recommends which kinds of elements (i.e funny jokes, celebrity appearances, animals ect.) should be included in a new ad based on user input about their desired outcome for the ad (i.e increase brand awareness or position brand image)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: superbowl-ads.csv | Column name | Description | |:------------------------------|:--------------------------------------------------------------| | year | The year the ad was broadcasted. (Integer) | | brand | The brand associated with the ad. (String) | | superbowl_ads_dot_com_url | The URL of the ad on Superbowl-ads.com. (String) | | youtube_url | The URL of the ad on YouTube. (String) ...

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.

Success.ai’s Consumer Marketing Data for Food, Beverage & Consumer Goods Professionals Globally provides a comprehensive dataset tailored for businesses seeking to connect with decision-makers and marketing professionals in these dynamic industries. Covering roles such as brand managers, marketing strategists, and product developers, this dataset offers verified contact details, decision-maker insights, and actionable business data.

With access to over 700 million verified global profiles, Success.ai ensures your marketing, sales, and research efforts are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is essential for businesses aiming to lead in the food, beverage, and consumer goods sectors.

Why Choose Success.ai’s Consumer Marketing Data?

1. Verified Contact Data for Precision Targeting

   • Access verified work emails, phone numbers, and LinkedIn profiles of marketing professionals, brand leaders, and product strategists.
   • AI-driven validation ensures 99% accuracy, minimizing communication errors and maximizing outreach success.

2. Comprehensive Coverage Across Global Markets

   • Includes profiles of professionals from food and beverage companies, consumer goods manufacturers, and marketing agencies in key markets worldwide.
   • Gain insights into regional trends in product marketing, consumer engagement, and purchasing behaviors.

3. Continuously Updated Datasets

   • Real-time updates capture changes in professional roles, company strategies, and market trends.
   • Stay aligned with the fast-evolving consumer goods industry to identify emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with decision-makers, marketers, and product managers in the food, beverage, and consumer goods sectors worldwide.
• Leadership Insights: Gain detailed profiles of brand managers, marketing executives, and product developers shaping consumer trends.
• Contact Details: Access verified phone numbers and work emails for precision outreach.
• Industry Trends: Understand global marketing trends, regional consumer preferences, and market dynamics.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with key professionals managing brand strategies, product launches, and marketing campaigns in the food, beverage, and consumer goods industries.
   • Access data on career histories, certifications, and market expertise for targeted outreach.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (snack foods, beverages, household goods), geographic location, or job function.
   • Tailor campaigns to align with specific needs such as product placement, consumer engagement, or regional expansion.

3. Regional Trends and Consumer Insights

   • Leverage data on consumer preferences, product demand, and spending patterns in key markets.
   • Use these insights to refine product offerings, marketing strategies, and audience targeting.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Outreach

   • Design targeted campaigns for food, beverage, and consumer goods products based on verified data and consumer insights.
   • Leverage multi-channel outreach, including email, phone, and digital advertising, to maximize engagement.

2. Product Development and Launch Strategies

   • Utilize consumer trend data to guide product development and market entry strategies.
   • Collaborate with brand managers and marketing professionals to align offerings with consumer preferences.

3. Sales and Partnership Development

   • Build relationships with distributors, retailers, and marketers in the consumer goods supply chain.
   • Present co-branding opportunities, joint marketing campaigns, or distribution strategies to decision-makers.

4. Market Research and Competitive Analysis

   • Analyze global trends in consumer goods marketing, product innovations, and purchasing behaviors to refine strategies.
   • Benchmark against competitors to identify growth opportunities, underserved markets, and high-demand products.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality consumer marketing data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, marketing platforms, or analytics tools via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Acc...

In 2024, successful advertising campaigns' median profit-based return on investment (ROI) worldwide reached *** U.S. dollars, meaning global advertisers profited, on average, *** dollars for every dollar they spent on those strategies. Successful ad campaigns' median revenue-based ROI stood at **** dollars that year. ROI: expectation and reality Within the realm of advertising and marketing, ROI measurement is often crucial to justify budget adjustments – not only to lower or raise it but also to determine in which channels to invest. A common formula entails subtracting organic sales growth and marketing costs from revenue growth and dividing it by the marketing costs. Still, multiple campaigns may require different approaches. During a 2024 survey, nearly ********* of global marketing decision-makers listed ROI measurement among the challenges for a data-driven strategy. Reliable ROI measurement rules A late 2022 worldwide study investigated marketers' confidence level in their ROI measurement across multiple ad channels. Social media emerged as number one: Over ** percent of respondents said they felt either extremely or very confident calculating their ROI. In the last quarter of 2024, another survey asked which social media platforms had the highest ROI according to global marketers. Facebook and Instagram – both controlled by Meta – led that ranking, named by ** and ** percent of the interviewees, respectively.

A large company with a substantial user base plans to increase sales through advertisement on its website. However, they are still undecided about whether the ads increase sales or not. In order to determine whether this is the case, 20000 customers were subjected to A/B testing for 31 days.

Columns customerID: unique identifier for the customer test group: composed of 60% 'ad' and 40% 'psa' group. made_purchase: A Boolean value representing whether or not the user made a purchase after seeing an advertisement. days_with_most_add: A day of the month when the user saw the most ads. peak ad hours: An hour of the day when the user saw the most ads. ad_count: total number of ads seen by each user.

Snapshot img

Advertising Services Market Size 2024-2028

The advertising services market size is forecast to increase by USD 156 billion at a CAGR of 4.34% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of in-app advertising and the integration of Augmented Reality (AR) technology into marketing campaigns. In-app advertising has become a preferred choice for businesses looking to reach their audience in a more targeted and engaging way. According to recent studies, mobile app usage has d, with users spending an average of 3 hours and 15 minutes per day on mobile apps. This trend presents a substantial opportunity for advertising services providers, as more businesses look to capitalize on this captive audience. However, the market is not without challenges. The growing adoption of ad-blocker solutions by consumers is a major concern for advertising services providers. Ad-blockers are estimated to reach 700 million users worldwide by 2023, posing a significant threat to the effectiveness of traditional digital advertising. To navigate this challenge, advertising services providers must invest in innovative ad formats and targeting strategies that can bypass ad-blockers and deliver personalized and relevant ads to consumers. Additionally, staying abreast of emerging technologies, such as AR and Artificial Intelligence (AI), and integrating them into advertising campaigns will be crucial for companies seeking to differentiate themselves in a crowded market. By focusing on these key trends and challenges, advertising services providers can capitalize on the growing demand for digital advertising and effectively navigate the evolving market landscape.

What will be the Size of the Advertising Services Market during the forecast period?

Request Free SampleThe market in the US continues to experience growth, fueled by the increasing number of mobile phone users and the expansion of digital media. E-commerce platforms have emerged as significant advertisers, driving demand for search engine advertising and display ads. Internet penetration has reached an all-time high, providing advertisers with a vast audience to target. Video advertising, including video ads, has gained popularity due to the increasing consumption of digital content. Microsoft advertising and other ad platforms have adopted data-driven strategies, leveraging artificial intelligence and data analytics to deliver personalized advertisements. However, challenges such as ad fraud and privacy concerns persist, necessitating the development of advanced technologies and regulations. Emerging economies offer significant growth opportunities, particularly in healthcare and other industries. Demographics continue to influence advertising trends, with social media advertising remaining a key channel for reaching younger audiences. Advertisement channels continue to evolve, with email advertising and other forms of digital marketing maintaining their relevance.

How is this Advertising Services Industry segmented?

The advertising services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeDigital advertisingTV advertisingPrint advertisingOOH advertisingOthersGeographyNorth AmericaUSAPACChinaJapanEuropeGermanyUKSouth AmericaMiddle East and Africa

By Type Insights

The digital advertising segment is estimated to witness significant growth during the forecast period.Digital advertising encompasses the utilization of the Internet and advanced digital technologies, including search engine optimization (), pay-per-click, email advertisements, and various digital media and platforms, to promote products or services. The global advertising market is experiencing significant growth during the forecast period, driven by several factors. The increasing global Internet penetration, expanding mobile phone user base, and growing number of user searches are primary contributors to the digital advertisement spending segment. Additionally, the ongoing digital transformation across industries necessitates businesses to enhance their online presence. Programmatic advertising, a data-driven strategy, is gaining popularity due to its efficiency and ability to target specific audience demographics. Microsoft Advertising and other ad platforms employ programmatic advertising, enabling businesses to reach their desired audience more effectively. Digital media, including social media, television, and e-commerce platforms, are increasingly becoming essential advertising channels. Artificial Intelligence (AI) is revolutionizing the advertising industry by enabling personalized and sustainable advertising. AI-driven ad formats, such as smart ads and video ads, cater to individual consumer preferences and enhan

Snapshot img

US Digital Advertising Market Size 2025-2029

The US digital advertising market size is forecast to increase by USD 218.3 billion, at a CAGR of 15.2% between 2024 and 2029.

The digital advertising market is experiencing significant growth, driven primarily by the increasing popularity of in-app advertising. Brands are recognizing the value of reaching consumers through mobile applications, as users spend an average of 3 hours and 15 minutes per day on mobile devices. Artificial intelligence (AI) and machine learning algorithms enable customized advertisements and recommendation systems, enhancing the user experience and driving ad effectiveness. 
However, the market faces challenges as well. The growing adoption of ad-blocker solutions poses a threat to revenue generation for digital advertisers. To navigate this challenge, advertisers must focus on delivering valuable and non-intrusive content to maintain user engagement and circumvent ad-blockers. By staying attuned to these market dynamics and adapting to consumer preferences, companies can capitalize on opportunities and effectively address challenges in the digital advertising market. Digital Advertising Services provide Campaign management, Creative design, and Optimization services to help businesses maximize their online presence and customer engagement.

What will be the size of the US Digital Advertising Market during the forecast period?

Request Free Sample

In the dynamic digital advertising market, cross-channel marketing and omnichannel strategies are increasingly prevalent, allowing businesses to reach consumers seamlessly across various platforms. Dynamic creative optimization and marketing dashboards enable real-time content customization, enhancing personalized advertising experiences. Digital marketing trends lean towards mobile-first strategies, predictive analytics, and data-driven marketing. Brands prioritize social media strategy, sentiment analysis, and social listening for effective brand reputation management. Marketing mix modeling and marketing automation tools streamline campaign management, while PPC strategy and interactive advertising offer measurable results. Ad agency services and marketing technology stacks provide valuable insights, but privacy concerns and data security remain critical issues.
Customer journey mapping and performance reporting are essential for optimizing marketing operations and measuring success. Digital marketing ethics demand transparency and accountability, with brands focusing on ethical data collection, usage, and privacy policies.

How is this market segmented?

The market 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.

Channel

  Mobile
  Desktop/laptop
  Connected TV


Type

  Search advertising
  Social media advertising
  Banner advertising
  Others


End-user

  Retail
  Media and entertainment
  BFSI
  Healthcare and pharmaceuticals
  Others


Geography

  North America

    US

By Channel Insights

The mobile segment is estimated to witness significant growth during the forecast period. In the dynamic US digital advertising market, mobile advertising holds a substantial share due to the increasing penetration of smartphones and tablets. Mobile devices, particularly smartphones, dominate the landscape, with mobile advertising accounting for a significant portion of overall digital advertising expenditure. With over 80% smartphone penetration in the country as of 2023, mobile platforms offer advertisers access to a vast user base. This flexibility enables advertisers to engage users through targeted ad strategies based on user behavior and preferences. Consequently, mobile applications (apps) and games are integrating in-app ads, contributing to the segment's significant growth. Brand awareness is another crucial aspect of digital advertising, with businesses investing heavily to reach their audiences effectively. Digital transformation has led to the adoption of various digital advertising technologies, such as programmatic advertising, data management platforms, and ad serving.
These technologies facilitate real-time bidding, audience targeting, and conversion rate optimization. Artificial intelligence and machine learning play a pivotal role in ad optimization, enabling advertisers to analyze consumer behavior and tailor their campaigns accordingly. Behavioral targeting, contextual targeting, and audience targeting are essential strategies for maximizing user engagement and click-through rates. Brand safety and fraud detection are critical concerns for businesses, with digital advertising technology ensuring secure transactions and protecting against malicious activities. Digital signage and content marketing are also popular channe