Explore the dynamics of advertising impact on product sales with this synthesized dataset. Created using Python programming language, the dataset comprises seven columns representing advertising costs on various platforms — TV, Billboards, Google Ads, Social Media, Influencer Marketing, and Affiliate Marketing. The last column, 'Product_Sold' quantifies the corresponding number of units sold. This dataset is designed for analysis and experimentation, allowing you to delve into the relationships between different advertising channels and the resulting product sales. Gain insights into marketing strategies and optimize your approach using this comprehensive, yet user-friendly dataset.

Introduction

Mobile Advertising Statistics: It is amazing how rapidly mobile advertisements have transitioned from mere "banner ads on applications" to a billion-dollar industry consuming a significant portion of marketing budgets. If you scroll through a feed for just 5 seconds, you will encounter at least three advertisements attempting to sell something that seems to understand you remarkably well.

The reality is that no one openly expected mobile ads to become this effective. Brands are investing heavily, and frankly, this is understandable; everyone is fixated on their smartphones. In 2020, global mobile ad expenditure reached approximately $276 billion, indicating a substantial shift from traditional media to mobile-centric strategies. In 2023, mobile ad spending had increased to an estimated $362 billion, illustrating the rapid transition to smartphone reliance.

This significant increase was propelled by a combination of short-form video content, social commerce, and app-driven user engagement.

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.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of all NAICS under advertising, public relations, and related services (NAICS 5418), annual, for five years of data.

Advertisement Sales Dataset

The Advertisement Sales dataset is a collection of data points used to analyze the impact of advertising on sales. This dataset consists of 200 entries, each representing a unique observation with data on various types of media advertising and corresponding sales figures.

Key Features: ID: A unique identifier for each observation. TV: The amount of money spent on TV advertising (in thousands of dollars). Radio: The amount of money spent on Radio advertising (in thousands of dollars). Newspaper: The amount of money spent on Newspaper advertising (in thousands of dollars). Sales: The sales figures for the product (in thousands of units).

Summary Statistics: TV advertising: Ranges from $0.7k to $296.4k, with an average spend of $147.03k. Radio advertising: Ranges from $0k to $49.6k, with an average spend of $23.29k. Newspaper advertising: Ranges from $0.3k to $114k, with an average spend of $30.55k. Sales: Ranges from 1.6k to 27k units, with an average of 14.04k units.

Use Cases: Advertising Strategy: Businesses can use this dataset to understand the effectiveness of different advertising channels (TV, Radio, Newspaper) on sales performance. Predictive Modeling: Analysts can build predictive models to forecast sales based on advertising spend across different media.

ROI Analysis: Marketers can calculate the return on investment (ROI) for each advertising channel to optimize their budgets. Correlation Studies: Researchers can study the correlation between advertising spend and sales to derive insights on consumer behavior.

Potential Analyses: Regression Analysis: Determine how changes in advertising budgets influence sales. Comparative Analysis: Compare the effectiveness of different advertising mediums. Trend Analysis: Identify trends in advertising spending and sales performance over time.

This dataset provides a robust foundation for exploring the relationships between advertising expenditures and sales outcomes, enabling data-driven decision-making for marketing strategies. ​

This dataset provides a comprehensive view of the online advertising performance for "Company X" over a three-month period in 2020. Here's an overview of its components and potential analyses you can perform:

Dataset Components: Day: Date of the advertising campaign. Campaign: Specific group targeting variable set by Company X. User Engagement: Level of user interaction with the ads. Banner: Ad size served by "Advert Firm A". Placement: Publisher space where ads are served (websites/apps). Displays: Number of ads shown by "Advert Firm A". Cost: Price paid to serve the ads to the publisher. Clicks: Number of times users clicked on the ads. Revenue: Amount

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

As of early 2025, among the presented social networks, advertising on YouTube had the highest reach in the United States, both among women and men, with **** and **** percent, respectively. On average, YouTube advertising reached **** percent of U.S. adults.

Introduction In 2010, a small coffee shop in Portland decided to run its first Facebook ad. The owner spent just $20, targeting locals within a 5-mile radius. The result? A weekend rush that nearly tripled their regular traffic. Fast forward to 2025, and stories like this are no longer the...

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.

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

Introduction

Snapchat Advertising Statistics: Snapchat's shifting brand strategy is taking place alongside a new advertising campaign that highlights the platform's distinctiveness compared to other social media applications. This initiative is also accompanied by a range of new advertising features aimed at attracting advertisers following several difficult quarters.

Additionally, the company has introduced improved Dynamic Ads, generative AR and AI tools, along with other advertising features tailored for advertisers and brands. The platform has revamped its Dynamic Ads offerings, which will include a new four-tile layout set to launch this year. "As we continue to evaluate...the Dynamic Ad format, we will begin offering it to retailers and e-commerce advertisers," Harris informed us.

Snap is focused on expanding its advertiser base in tandem with the growth of its user base. According to the company's latest earnings report, the platform experienced an 85% each year in its small and medium advertiser base in the first quarter, and it recently announced 422 million daily active users.

This competition involves advertisement data provided by BuzzCity Pte. Ltd. BuzzCity is a global mobile advertising network that has millions of consumers around the world on mobile phones and devices. In Q1 2012, over 45 billion ad banners were delivered across the BuzzCity network consisting of more than 10,000 publisher sites which reach an average of over 300 million unique users per month. The number of smartphones active on the network has also grown significantly. Smartphones now account for more than 32% phones that are served advertisements across the BuzzCity network. The "raw" data used in this competition has two types: publisher database and click database, both provided in CSV format. The publisher database records the publisher's (aka partner's) profile and comprises several fields:

publisherid - Unique identifier of a publisher. Bankaccount - Bank account associated with a publisher (may be empty) address - Mailing address of a publisher (obfuscated; may be empty) status - Label of a publisher, which can be the following: "OK" - Publishers whom BuzzCity deems as having healthy traffic (or those who slipped their detection mechanisms) "Observation" - Publishers who may have just started their traffic or their traffic statistics deviates from system wide average. BuzzCity does not have any conclusive stand with these publishers yet "Fraud" - Publishers who are deemed as fraudulent with clear proof. Buzzcity suspends their accounts and their earnings will not be paid

On the other hand, the click database records the click traffics and has several fields:

id - Unique identifier of a particular click numericip - Public IP address of a clicker/visitor deviceua - Phone model used by a clicker/visitor publisherid - Unique identifier of a publisher adscampaignid - Unique identifier of a given advertisement campaign usercountry - Country from which the surfer is clicktime - Timestamp of a given click (in YYYY-MM-DD format) publisherchannel - Publisher's channel type, which can be the following: ad - Adult sites co - Community es - Entertainment and lifestyle gd - Glamour and dating in - Information mc - Mobile content pp - Premium portal se - Search, portal, services referredurl - URL where the ad banners were clicked (obfuscated; may be empty). More details about the HTTP Referer protocol can be found in this article. Related Publication: R. J. Oentaryo, E.-P. Lim, M. Finegold, D. Lo, F.-D. Zhu, C. Phua, E.-Y. Cheu, G.-E. Yap, K. Sim, M. N. Nguyen, K. Perera, B. Neupane, M. Faisal, Z.-Y. Aung, W. L. Woon, W. Chen, D. Patel, and D. Berrar. (2014). Detecting click fraud in online advertising: A data mining approach, Journal of Machine Learning Research, 15, 99-140.

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)

This dataset shows the Principal statistics of advertising services, 1983 - 2017. Footnote: No census/ survey was conducted for the years 1993, 1995, 1997, 1999, 2001, 2006, 2008, 2011, 2013, 2014 and 2016. Value of gross output, value of intermediate input, and value added are only available from the year 2003 onwards. Source: Department of Statistics, Malaysia No. of Views : 36

Introduction

LinkedIn Advertising Statistics: LinkedIn is the leading platform for B2B marketing. A global survey revealed that 44% of B2B professionals consider it the most significant social media platform. One reason for its top position is that LinkedIn enables access to decision-makers. The platform states that four out of five members influence business decisions. Additionally, LinkedIn's audience possesses twice the purchasing power of the average online audience.

LinkedIn Ads serve as paid promotions for businesses and are effective in converting key decision-makers on the platform, as evidenced by the following statistics. Consequently, some Digital Marketing agencies may recommend that B2B brands use advertising on this platform.

Therefore, it is not surprising that 57% of marketers intend to improve their organic marketing efforts on LinkedIn. Given that LinkedIn Dynamic Ads customize each advertisement with details from individual users, you can expect excellent Click-Through rates for each ad, thereby increasing traffic to the targeted URL.

In 2024, magazine advertising spending accounted for an estimated 2.5 percent of global ad revenues. This was the lowest share within the presented period.

When Emma launched her direct-to-consumer skincare brand, she thought a strong website would do the trick. But soon, her traffic stagnated, and sales didn’t budge. Then she tried something different: mobile marketing. Within six months, mobile-driven traffic doubled, and over 60% of conversions came from phones. Her story isn’t unique....

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...

14 Datasets used in experiments contain user data of the day of online advertisements from a cross-border e-commerce enterprise from September 1st (9.01) to September 14th (9.14), 2018. Table 3 summarizes the 14 datasets. Each instance of the datasets represents the corresponding online advertisement and is described by 22 attributes.