This database, updated daily, contains ads that ran on Facebook and were submitted by thousands of ProPublica users from around the world. We asked our readers to install browser extensions that automatically collected advertisements on their Facebook pages and sent them to our servers. We then used a machine learning classifier to identify which ads were likely political and included them in this dataset.

Context

This dataset contains campaign data for both Facebook Ads and AdWords, offering a side-by-side comparison of performance metrics, costs, and conversions. It's an ideal resource for A/B testing in marketing, especially for analyzing the effectiveness of ad campaigns across platforms.

Source

This dataset was created from scratch using Mockaroo, ensuring it is tailored for practical use.

Inspiration

While watching a YouTube tutorial 👉 [https://youtu.be/iCj4lT5KvJk?si=FijILsrbxBrcE3pw])(url), I noticed that the tutorial lacked an uploaded dataset, and many viewers in the comment section requested one. To help others follow along and practice, I decided to create a mock dataset from scratch. Now, you can easily replicate the tutorial and enhance your skills!

Analysis Ideas

Platform Performance Comparison: Compare key metrics like CTR, conversion rate, and cost per click between Facebook Ads and AdWords.

Trend Over Time: Analyze changes in ad performance metrics across different years.

A/B Testing Insights: Assess simultaneous campaigns to identify the better-performing platform.

Cost Efficiency: Identify campaigns with low costs but high conversions on each platform.

Visualization of Metrics: Create charts to visually compare campaign performance. Statistical Insights: Perform hypothesis testing to check for significant differences in performance metrics. Recommendations for Marketing Strategy: Provide actionable suggestions based on the data analysis. # Enjoy exploring and testing this dataset for your marketing analyses!

Simple Dataset from different marketing campaigns.

The total conversion number shows the total number of signups or installs for instance while approved conversions tells how many became actual active users.

Courtesy of Bunq.

Context

Cluster Analysis for Ad Conversions Data

Content

The data used in this project is from an anonymous organisation’s social media ad campaign. The data file can be downloaded from here. The file conversion_data.csv contains 1143 observations in 11 variables. Below are the descriptions of the variables.

1.) ad_id: an unique ID for each ad.

2.) xyz_campaign_id: an ID associated with each ad campaign of XYZ company.

3.) fb_campaign_id: an ID associated with how Facebook tracks each campaign.

4.) age: age of the person to whom the ad is shown.

5.) gender: gender of the person to whim the add is shown

6.) interest: a code specifying the category to which the person’s interest belongs (interests are as mentioned in the person’s Facebook public profile).

7.) Impressions: the number of times the ad was shown.

8.) Clicks: number of clicks on for that ad.

9.) Spent: Amount paid by company xyz to Facebook, to show that ad.

10.) Total conversion: Total number of people who enquired about the product after seeing the ad.

11.) Approved conversion: Total number of people who bought the product after seeing the ad.

Acknowledgements

Thanks to the Anonymous data depositor

Inspiration

Social Media Ad Campaign marketing is a leading source of Sales Conversion and i have made this data available for the benefit of Businesses using Google Adwords to track Conversions

"Unleashing Social Sentiments: A Twitter Analysis" appears to be a study or analysis that uses a Twitter dataset to explore the sentiment and opinions of Twitter users towards a particular topic or set of topics. Without more information about the study, it is difficult to provide a detailed analysis. However, based on the title and the use of a Twitter dataset, it is likely that the study involves the use of sentiment analysis techniques to analyze the opinions and sentiment expressed in the dataset. https://camo.githubusercontent.com/7bf6f8c804cf1ec62e2cbbc7c85ea7dfd65b4848df48be4218e24012c6eb3430/68747470733a2f2f692e6d6f72696f682e636f6d2f323032302f30322f30342f6265656633366664373037642e6a7067">

The use of Twitter data for sentiment analysis has become increasingly popular in recent years due to the massive volume of data available and the ease with which opinions and sentiment can be expressed on the platform. By analyzing Twitter data, researchers can gain insights into public opinion and sentiment on a wide range of topics, from politics to consumer products to social issues.

To conduct a Twitter analysis, researchers typically collect a dataset of tweets related to a particular topic or set of topics. This dataset may include features such as the Twitter username, the tweet content, the time and date of the tweet, and any associated metadata such as hashtags or mentions. The dataset can then be processed using NLP or sentiment analysis techniques to classify the sentiment expressed in each tweet as positive, negative, or neutral.

The dataset contains tweets from the Twitter API that were scraped for seven hashtags:

#Messi: This hashtag refers to the Argentine soccer superstar Lionel Messi, and is commonly used by fans and followers to discuss his performances, accomplishments, and news related to his career.

#FIFAWorldCup: This hashtag is used during the FIFA World Cup, a quadrennial international soccer tournament. Tweets with this hashtag may discuss news, scores, or analysis related to the tournament.

#DeleteFacebook: This hashtag is used by people who advocate for deleting or boycotting Facebook, often in response to controversies related to data privacy, political advertising, or other issues related to the social media giant.

#MeToo: This hashtag is used in the context of the Me Too movement, a social movement against sexual harassment and assault, particularly in the workplace. Tweets with this hashtag may share personal stories, express support for the movement, or discuss related news and events.

#BlackLivesMatter: This hashtag is used in the context of the Black Lives Matter movement, a movement against police brutality and systemic racism towards Black people. Tweets with this hashtag may express support for the movement, share news and updates, or discuss related issues.

#NeverAgain: This hashtag is used in the context of the Never Again movement, which advocates for gun control and other measures to prevent school shootings and other acts of gun violence.

#BarCamp: This hashtag refers to BarCamp, an international network of unconferences - participant-driven conferences that are open and free to attend. Tweets with this hashtag may discuss upcoming BarCamp events, share insights or learnings from past events, or express support for the BarCamp community.

The sentiment score was generated using a pre-trained sentiment analysis model, and represents the overall sentiment of the tweet (positive, negative, or neutral).

The data can be used to gain insights into how people are discussing and reacting to these topics on Twitter, and how the sentiment towards these hashtags may have evolved over time. Researchers and analysts can use this dataset for sentiment analysis, natural language processing, and machine learning applications.

Some potential analyses that can be performed on the data include sentiment trend analysis over time, geographical distribution of sentiments, and topic modeling to identify themes and topics that emerge from the tweets.

Overall, the dataset provides a rich resource for researchers and analysts interested in studying social and political issues on social media.