Facebook is a web traffic powerhouse: in March 2024 approximately 16.6 billion visits were measured to the Facebook.com, making it one of the most-visited websites online. In the third quarter of 2023, Facebook had nearly three billion monthly active users.

In the six months ending March 2024, the United States accounted for 22.7 percent of traffic to the social network Facebook.com. Second-ranked Vietnam accounted for over five percent of traffic to the website.

Traffic analytics, rankings, and competitive metrics for facebook.com as of September 2025

As of March 2024, Google.com accounted for over 7.35 percent of referral traffic to Facebook.com. Facebook's second-largest referral traffic driver was msn.com, which generated 4.83 percent of referral traffic to the social media platform. Overall, referrals accounted for slightly less than two percent of traffic to Facebook.

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!

facebook.com is ranked #5 in US with 9.67B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

As of March 2024, Youtube.com accounted for nearly 43 of social media referral traffic to Facebook.com. Facebook's second, third and fourth-largest social media traffic driver were messenger.com, web.whatsapp.com, and instagram.com, each generating over 12 percent of social media traffic to the social network. Overall, social media accounted for 1.95 percent of traffic referrals to Facebook.com.

facebook.de is ranked #1297 in DE with 1.69M Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

Facebook referral traffic fell substantially to news sites between March 2021 and March 2024. Unilad.com had the highest share of social page views coming from Facebook, but at ** percent in 2024, this was half the share three years earlier. Most news sites got less than ** percent of their page views from Facebook.

Dataset

This dataset was created by Matheus Santos

Contents

facebook.com.br is ranked #2033 in BR with 1.14M Traffic. Categories: Information Technology. Learn more about website traffic, market share, and more!

Auto-generated structured data of Windsor.ai Documentation - Facebook Ads (Meta) Field Reference from table Available options

Facebook Ads PixelPro9 Elements

## Overview

Facebook Ads PixelPro9 Elements is a dataset for object detection tasks - it contains Words LVq8 VzTm annotations for 1,663 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

36.8% of the entire world’s population uses Facebook at least once per month.

Real e-commerce dataset from the online shop and cost of advertising campaigns in Google and Facebook/Meta Ads. Website traffic data from Google Analytics. All data in daily basis.

Auto-generated structured data of Facebook Lead Ads (Meta) Field Reference from table Fields

Facebook is becoming an essential tool for more than just family and friends. Discover how Cheltenham Township (USA), a diverse community just outside of Philadelphia, deals with major issues such as the Bill Cosby trial, everyday traffic issues, sewer I/I problems and lost cats and dogs. And yes, theft.

Communities work when they're connected and exchanging information. What and who are the essential forces making a positive impact, and when and how do conversational threads get directed or misdirected?

Use Any Facebook Public Group

You can leverage the examples here for any public Facebook group. For an example of the source code used to collect this data, and a quick start docker image, take a look at the following project: facebook-group-scrape.

Data Sources

There are 4 csv files in the dataset, with data from the following 5 public Facebook groups:

• Unofficial Cheltenham Township
• Elkins Park Happenings!
• Free Speech Zone
• Cheltenham Lateral Solutions
• Cheltenham Township Residents

post.csv

These are the main posts you will see on the page. It might help to take a quick look at the page. Commas in the msg field have been replaced with {COMMA}, and apostrophes have been replaced with {APOST}.

• gid Group id (5 different Facebook groups)
• pid Main Post id
• id Id of the user posting
• name User's name
• timeStamp
• shares
• url
• msg Text of the message posted.
• likes Number of likes

comment.csv

These are comments to the main post. Note, Facebook postings have comments, and comments on comments.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Comment Id.
• timeStamp
• id Id of user commenting
• name Name of user commenting
• rid Id of user responding to first comment
• msg Message

like.csv

These are likes and responses. The two keys in this file (pid,cid) will join to post and comment respectively.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Matches Comments id.
• response Response such as LIKE, ANGRY etc.
• id The id of user responding
• name Name of the user responding

member.csv

These are all the members in the group. Some members never, or rarely, post or comment. You may find multiple entries in this table for the same person. The name of the individual never changes, but they change their profile picture. Each profile picture change is captured in this table. Facebook gives users a new id in this table when they change their profile picture.

• gid Group id
• id Id of the member
• name Name of the member
• url URL of the member

Among the industries presented in the data set, health and fitness had the highest cost-per-action (CPA) for Facebook ads as of February 2025, with ***U.S. dollars. The lowest value belonged to the real estate industry, with ** U.S. dollars.

Dataset Summary

UltraLAMBDAis a large-scale dataset of ads sourced from brand videos on platforms such as YouTube and Facebook Ads, as well as from CommonCrawl. The memorability scores for the ads are assigned by our model Henry.

  Dataset Structure

from datasets import load_dataset ds = load_dataset("behavior-in-the-wild/UltraLAMBDA") ds

DatasetDict({ train: Dataset({ features: ['id', 'memorability'], num_rows: 1964 })

})

  Data… See the full description on the dataset page: https://huggingface.co/datasets/behavior-in-the-wild/UltraLAMBDA.

Nymblr offers the only solution for custom and verified B2B audiences that allow you to activate your sales and marketing campaigns via numerous channels like email, Facebook Ads, Google Ads, and even LinkedIn Ads.

Increase conversion by targeting your ideal customers via the channels they prefer. Our platform and data allow you to build B2B audiences based on job titles, seniority, department, industry, SIC & NAICS, specific company, skills, and location down to the ZIP code.