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

The average monthly cost-per-click (CPC) in Google Ads search advertising in Israel in December 2024 was **** US dollars, the highest among the ** countries represented in the data set. UAE and Bahrain in second and third.  TV advertisement in MENA  The increase in TV advertisement spend in the Middle East and North Africa in 2020 was significantly lower than the previous year. In 2020, the average time spent watching television in Saudi Arabia was about **** hours, compared to a lower figure in Kuwait. In 2020, total advertising spending in the Middle East and North Africa (MENA) region fell. In comparison, TV advertising spending in the MENA region is expected to exceed *** billion U.S. dollars in 2022.  Advertising spending worldwide       Newspaper ad spending in the region fell significantly in 2020, while internet ad spending increased during the same period in the MENA region. The global advertising market, on the other hand, went through some rough patches between 2000 and 2010. However, the situation has stabilized since 2011, and advertising spending growth has also remained stable. The coronavirus outbreak in 2020 resulted in a significant drop in ad spend. According to projections, the industry's expenditure growth will return to around *** percent by 2024, with the Internet serving as the primary advertising medium.

In December 2024, the average monthly cost-per-click (CPC) in Google Ads search advertising for the insurance industry in the United States reached ***** U.S. dollars and was the highest among the presented industries. At the same time, the lowest result was for the electronics sector, with CPC valued at ** U.S. cents.

Auto-generated structured data of Google Ads Field Reference from table Fields

This dataset contains two tables: creative_stats and removed_creative_stats. The creative_stats table contains information about advertisers that served ads in the European Economic Area or Turkey: their legal name, verification status, disclosed name, and location. It also includes ad specific information: impression ranges per region (including aggregate impressions for the European Economic Area), first shown and last shown dates, which criteria were used in audience selection, the format of the ad, the ad topic and whether the ad is funded by Google Ad Grants program. A link to the ad in the Google Ads Transparency Center is also provided. The removed_creative_stats table contains information about ads that served in the European Economic Area that Google removed: where and why they were removed and per-region information on when they served. The removed_creative_stats table also contains a link to the Google Ads Transparency Center for the removed ad. Data for both tables updates periodically and may be delayed from what appears on the Google Ads Transparency Center website. About BigQuery This data is hosted in Google BigQuery for users to easily query using SQL. Note that to use BigQuery, users must have a Google account and create a GCP project. This public dataset is included in BigQuery's 1TB/mo of free tier processing. Each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . Download Dataset This public dataset is also hosted in Google Cloud Storage here and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. We provide the raw data in JSON format, sharded across multiple files to support easier download of the large dataset. A README file which describes the data structure and our Terms of Service (also listed below) is included with the dataset. You can also download the results from a custom query. See here for options and instructions. Signed out users can download the full dataset by using the gCloud CLI. Follow the instructions here to download and install the gCloud CLI. To remove the login requirement, run "$ gcloud config set auth/disable_credentials True" To download the dataset, run "$ gcloud storage cp gs://ads-transparency-center/* . -R" This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

In December 2024, the average monthly cost-per-click (CPC) in Google Ads search advertising for the insurance industry in Mexico amounted to **** U.S. dollars and was the highest among the industries presented in the statistic.Search advertisingMexico is the second-largest search advertising market in Latin America, with a 2022 spending of **** billion U.S. dollars, trailing behind Brazil with **** billion. In Mexico, search advertising was expected to account for ** percent of digital advertising, which would make it the second most important digital ad format after online video, trumping banner and social media spending, among others.Digital advertisingInternet is the most popular ad medium in Mexico, accounting for roughly ** percent of the country's total ad spending. In total, digital advertising was expected to generate **** billion U.S. dollars in expenditure in Mexico in 2024, which would translate into approximately **** U.S. dollars of spending per internet user. In June 2024, industries investing most in digital adverting in the North American country were onlien retail, transportation, travel, and tourism, as well as telecommunications.

This dataset contains information on how much money is spent by verified advertisers on political advertising across Google Ad Services. In addition, insights on demographic targeting used in political ad campaigns by these advertisers are also provided. Finally, links to the actual political ad in the Google Transparency Report (https://adstransparency.google.com) are provided. Data for an election expires 7 years after the election. After this point, the data are removed from the dataset and are no longer available.

Update frequency: Daily

Dataset source: Transparency Report: Political Advertising on Google

Terms of use:

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/transparency-report/google-political-ads

For more information see: The Political Advertising on Google Transparency Report at https://adstransparency.google.com

The supporting Frequently Asked Questions at https://support.google.com/transparencyreport/answer/9575640?hl=en&ref_topic=7295796

This dataset contains information on how much money is spent by verified advertisers on political advertising across Google Ad Services. In addition, insights on demographic targeting used in political ad campaigns by these advertisers are also provided. Finally, links to the actual political ad in the Google Transparency Report are provided. Data for an election expires 7 years after the election. After this point, the data are removed from the dataset and are no longer available. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Auto-validation metrics for post ID 720784

In 2022, Adobe Inc. was the software-as-a-service (SaaS) company with the highest Google Ads spend globally at approximately *** million U.S. dollars. The International Business Machines Corporation (IBM) and WordStream Inc. followed with **** million dollars and **** million dollars, respectively.

Blockchain data dashboard: Google Search Ads Phishing Stats

The functional linear regression model with points of impact (PoI) is a recent augmentation of the classical functional linear model with many practically important applications. In this article, however, we demonstrate that the existing data-driven procedure for estimating the parameters of this regression model can be very instable and inaccurate. The tendency to omit relevant PoI is a particularly problematic aspect resulting in omitted-variable biases. We explain the theoretical reason for this problem and propose a new sequential estimation algorithm that leads to significantly improved estimation results. Our estimation algorithm is compared with the existing estimation procedure using an in-depth simulation study. The applicability is demonstrated using data from Google AdWords, today’s most important platform for online advertisements. The R-package FunRegPoI and additional R-codes are provided in the online supplementary materials.

The online search advertising market is a dynamic and rapidly evolving landscape, projected to experience substantial growth over the forecast period (2025-2033). While precise figures for market size and CAGR are unavailable, considering the consistent growth in digital advertising and the dominance of search engines like Google, a reasonable estimate for the 2025 market size would be around $300 billion USD. A conservative Compound Annual Growth Rate (CAGR) of 8% seems plausible given historical trends and projected increases in internet usage, mobile penetration, and sophisticated targeting technologies. Key drivers include the rising adoption of mobile devices, increasing e-commerce activity, the growing sophistication of ad targeting (including AI-driven approaches), and the continuous refinement of search engine algorithms to optimize ad relevance and user experience. Emerging trends, such as voice search optimization, personalized advertising experiences, and the integration of augmented reality (AR) and virtual reality (VR) technologies into ad campaigns, will further shape the market's trajectory. Potential restraints could include increasing user concerns about data privacy and regulatory scrutiny, the escalating cost of ad campaigns driven by competition, and the potential for ad fatigue among users. The segmentation of the market is crucial for understanding its intricacies. While specific segment data isn't provided, key segments likely include mobile search ads (a rapidly expanding sector), desktop search ads, video search ads, and search ads targeting specific demographics or interests. Major players such as Amazon, Google, Facebook, Microsoft, and Baidu dominate the market, each wielding significant influence over ad placement, pricing, and technological innovation. Regional variations are also important, with North America and Europe likely maintaining significant market shares due to high internet penetration and sophisticated advertising ecosystems. However, the Asia-Pacific region displays strong potential for future growth, driven by rapidly increasing internet adoption in developing economies. Analyzing these segments and geographical distributions provides valuable insights into the market's structure and future growth potential.

The programmatic advertising market is experiencing robust growth, driven by the increasing adoption of automation in digital advertising, the rise of data-driven targeting, and the expansion of connected devices. The market's value, while not explicitly stated, can be reasonably estimated based on industry trends and comparable markets. Considering the significant investment and rapid advancements in this sector, a 2025 market size of approximately $150 billion appears plausible, given the substantial growth observed in previous years. A Compound Annual Growth Rate (CAGR) of around 15% during the forecast period (2025-2033) is also a reasonable assumption reflecting continued innovation and market penetration. Key growth drivers include the increasing sophistication of ad targeting capabilities, the growing preference for real-time bidding (RTB) among advertisers, and the emergence of new advertising formats and channels. Trends such as the increasing use of artificial intelligence (AI) and machine learning (ML) for campaign optimization, the growing demand for transparency and accountability in programmatic advertising, and the increasing focus on cross-device targeting are shaping the market landscape. However, challenges such as ad fraud, brand safety concerns, and data privacy regulations continue to pose restraints to market growth, requiring continuous innovation and regulatory adaptation within the industry. The market is segmented by application (Retail, Recreation, Banking, Transportation, Other) and type (Cloud-based, On-Premise), with cloud-based solutions gaining significant traction due to their scalability and cost-effectiveness. North America currently holds a substantial market share, but regions like Asia-Pacific are witnessing rapid growth fueled by increasing internet penetration and digital advertising adoption. The competitive landscape is dynamic, with established players like Facebook Business, Google AdWords, and The Trade Desk competing alongside a range of specialized programmatic advertising platforms. The ongoing consolidation and strategic partnerships within the industry highlight the competitive intensity and the importance of continuous innovation to maintain market share. The future of programmatic advertising will likely be shaped by the convergence of data, technology, and evolving consumer preferences, necessitating a focus on delivering personalized and relevant advertising experiences while adhering to increasingly stringent data privacy regulations. The industry’s success hinges on addressing issues such as transparency, fraud prevention, and user privacy to build trust and sustain long-term growth. This will involve collaboration between platforms, advertisers, and regulatory bodies to create a more responsible and effective programmatic advertising ecosystem.

The Search Engine Advertising (SEA) services market is experiencing robust growth, driven by the increasing reliance of businesses on digital marketing strategies to reach target audiences. The market's expansion is fueled by several key factors, including the rising adoption of mobile devices and e-commerce, the increasing sophistication of advertising technologies like AI-powered targeting and automation, and the growing demand for measurable and performance-driven marketing solutions. This has resulted in a competitive landscape with established players like Google Ads and Bing Ads dominating, alongside a diverse range of specialized platforms catering to different business needs and scales. While precise market sizing data is unavailable, a reasonable estimation based on industry trends suggests a 2025 market size of approximately $150 billion, growing at a compound annual growth rate (CAGR) of 12% throughout the forecast period (2025-2033). This growth is projected to continue, driven by ongoing advancements in technology and the increasing integration of SEA into broader marketing strategies. However, the market also faces some challenges. Increasing data privacy regulations and concerns around ad fraud are leading to stricter guidelines and increased costs for advertisers. Furthermore, the ever-evolving algorithms of major search engines require continuous adaptation and optimization strategies from both advertisers and the service providers. The competitive landscape itself presents a challenge, demanding continuous innovation and value-added services to differentiate oneself and maintain market share. Segmentation within the market is apparent, with providers focusing on various niches such as small business solutions, enterprise-level platforms, specific industry verticals, or specialized functionalities like reporting and analytics. This segmentation underscores the varied needs within the market and the ongoing need for specialized tools and expertise.

Between 2017 and 2022, the share of Google and Meta in digital advertising spending in the United States fell from ** percent to ** percent. Meta's share reained stable, whereas Google's share lost *** percentage points.

The digital advertising market, encompassing giants like Google, Facebook, Amazon, and Microsoft, alongside specialized platforms such as Celtra and Adform, is experiencing robust growth. While precise figures for market size and CAGR are unavailable, industry trends suggest a substantial market exceeding $500 billion in 2025, projected to experience a Compound Annual Growth Rate (CAGR) of approximately 10-15% between 2025 and 2033. This expansion is driven by several key factors: the increasing adoption of mobile devices and internet penetration globally; the rise of sophisticated programmatic advertising techniques enabling highly targeted campaigns; and the growing importance of data-driven insights for optimizing ad spending and performance. Furthermore, the expansion of e-commerce and the increasing sophistication of social media platforms continue to fuel demand for effective digital advertising strategies. The market is segmented by advertising type (e.g., display, video, search, social media), device (mobile, desktop), and industry vertical. While restraints exist, such as ad fraud and increasing regulatory scrutiny regarding data privacy (like GDPR and CCPA), these are largely offset by the overall positive market dynamics and continuous innovation within the sector. The competitive landscape is intensely dynamic, with established tech giants constantly battling smaller, more agile companies focusing on specific niches or innovative advertising technologies. The forecast period from 2025-2033 presents significant opportunities for both established and emerging players who can adapt to the evolving technological and regulatory landscape while delivering superior campaign performance for their clients.

You can check the fields description in the documentation: current Full database: https://docs.dataforseo.com/v3/databases/google/full/?bash; Historical Full database: https://docs.dataforseo.com/v3/databases/google/history/full/?bash.

Full Google Database is a combination of the Advanced Google SERP Database and Google Keyword Database.

Google SERP Database offers millions of SERPs collected in 67 regions with most of Google’s advanced SERP features, including featured snippets, knowledge graphs, people also ask sections, top stories, and more.

Google Keyword Database encompasses billions of search terms enriched with related Google Ads data: search volume trends, CPC, competition, and more.

This database is available in JSON format only.

You don’t have to download fresh data dumps in JSON – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

The global online advertising market, valued at $847.93 billion in 2025, is poised for substantial growth. Driven by the increasing penetration of internet and mobile devices, coupled with the evolving preferences of consumers towards digital media consumption, this sector demonstrates a strong upward trajectory. Key growth drivers include the rise of programmatic advertising, the expanding use of data analytics for targeted campaigns, and the increasing sophistication of ad formats such as video and interactive ads. Furthermore, the growth of social media platforms and the emergence of new technologies like artificial intelligence and machine learning are further fueling this expansion. The market is segmented across various applications, with automotive, BFSI (Banking, Financial Services, and Insurance), education, healthcare, retail, and ITES sectors significantly contributing to the overall revenue. Leading players such as Amazon, Google, Facebook, and Microsoft are continuously innovating and expanding their advertising platforms to maintain their market dominance. However, challenges remain, including increasing ad blocking, concerns regarding data privacy and regulations like GDPR, and the need for effective measurement and attribution of advertising ROI. Looking ahead, the market is expected to exhibit a consistent growth rate, although the precise CAGR will depend on macroeconomic factors and technological advancements. The regional distribution of the market reflects the varying levels of internet penetration and economic development across different regions. North America and Asia Pacific currently represent significant portions of the market, but growth is anticipated in developing economies in regions such as Africa and South America as internet access expands. The competitive landscape is fiercely contested, with established giants and emerging technology companies vying for market share through innovation in targeting, ad formats, and measurement techniques. The continued evolution of consumer behavior, along with the technological advancements within the digital space, will fundamentally shape the future of online advertising.