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.

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

On a brisk January morning, Sarah, a small business owner in Ohio, launched her very first Facebook ad. With a modest $150 budget, she was hoping to generate a few leads for her new skincare line. Within three days, her ad reached over 12,000 people, and she closed 18 sales,...

In the third quarter of 2020, Facebook stated that 10 million active advertisers were using the social networking platform to promote their products and services, up from seven million advertisers in the first quarter of the previous year. Facebook advertising revenue Facebook generates the vast majority of its revenues through advertising. In 2020, the social network’s ad revenue amounted to over 84.2 billion U.S. dollars, compared to 1.7 billion U.S. dollars in other revenues.

Most of Facebook’s ad sales are generated via mobile, to the tune of 50.6 billion U.S. dollars in 2018. The company stated that mobile accounted for 92 percent of its total advertising revenue. This is hardly surprising when looking at the dominance of Facebook’s mobile presence which includes the namesake platform app but also Facebook Messenger, Instagram, WhatsApp, and various other Facebook properties and products.

As of the second quarter of 2021, Facebook’s advertising revenue in the United States and Canada amounted to roughly 13.3 billion U.S. dollars. Other revenues came to 372 million U.S. dollars. During the same period, global Facebook ad revenues amounted to 28.5 billion U.S. dollars.

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.

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...

Facebook probably needs no introduction; nonetheless, here is a quick history of the company. The world’s biggest and most-famous social network was launched by Mark Zuckerberg while he was a...

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!

In a small café in Austin, Texas, a 68-year-old grandmother shares reels of her garden with her granddaughter, who lives in Tokyo. Meanwhile, a high school student in Nairobi livestreams his gaming tutorial to friends across the world. Behind these everyday moments is Facebook, the digital backbone connecting over 3...

This fileset contains a series of screenshots taken from our facebook advertising account. A few days ago we noticed that some negative "SEO" tactics, for lack of a better term, were having a negative impact on the performance of ads and fan engagement on the facebook page that we've been building.

I developed a custom software package, which utilizes nueural networks I've developed, to identify a target demographic, and suggest advertising content for said target demographic.

After a short training period we were able to create advertisemsents on facebook that averaged a cost of 0.01 cents per like. We also had a fan page engagement of nearly 4 times that of major brands like Wal-Mart.

Shortly after we began to obtain success we started noticing problems with our page. Since we have a stalker issue, we determined that the issues with our page were likely related to him.

We assued this because we had a disproportinately high number of spammy, negative, and inapporpriate comments on our posts. Offline harassment of our staff by the stalker also increased significantly during this time.

Curiously, we believe that the incident with the stalker allowed us to ascertain some interesting observations about Facebook's algorithims, which I've outlined below.

We believe, after reseraching this issue, that Facebook's algorithims suffer from the following issues:

1. They are easily gamed. We think that Facebook's algorithims are hypersensitive to negative comments being made on a post, and conversely likely positive ones as well. If a post is hidden, the comments are negative, or if a user interacts with the post negatively in some way, then Facebook's algorithims will "punish" your page.

2. We think that a series of scripted fake bot accounts would easily cause the issues that we've been expriencing.

As you can see from the data provided, over 90% of our likes come from paid facebook advertisement, therefore we do not have a significant number of fake accounts on our page brought in by third party advertising because we didn't do any of that.

Moreover, we did not send any of our fans obtained via mailing lists, or offline contact to our facebook page, those fans participate with us via email and/or through our private Google+ community.

So it is safe to say that our problems have not been caused by purchasing a large amount of fake likes from any third party vendor.

In addition, because our likes were gained very quickly, at a rate of about 2.5k likes a day, we do not believe that we have suffered from changes in the general demographic of our Facebook fan base over time.

Yet almost immediately after we started expericing trolling issues with our page, we also noticed a dip in the number of fans our posts were shown to by Facebook, and the performance of our ads began to go down, even though the content on our page had not changed.

We attributed this to holes in Facebook's algorithims, and potentially to the excessive use of fake bot accounts by Facebook itself.

We cannot prove the latter satement, but there have been similar reports before. Reference - http://www.forbes.com/sites/davidthier/2012/08/01/facebook-investigating-claims-that-80-of-ad-clicks-come-from-bots/

This article from Forbes outlines how one startup company repoted that up to 80% of their Facebook likes were fake bot accounts even though they paid for advertising directly through Facebook.

Our reserach suggests that Facebook's advertising platform functions as follows: - An advertiser pays for likes with Facebook, and the quality of the content on their page is initially assessed by those who are liking the page, but once the page obtains a following, we believe that the quality of the content is assessed by how many people like the posts on the page directly after they are posted.

If a post gets hidden, marked as spammed, skipped over, whatever, then we beleive that Facebook kicks that post out of the newsfeeds. If this happens to a significant number of posts on the page, then we believe that Facebook places the page on an advertising black-list.

Once on this black-list ads will begin to perform poorly, and content will drop out of newsfeeds causing even the most active page to go silent.

We tested this by posting pictures of attractive blond women, which with our demographic would have normally obtained a large number of likes and we struggled to get even 10 likes at over 20k page likes when we would have previosuly obtained almost 100 likes without boosting at only 5k page likes.

Why this probably isn't seen more often: In most cases this probably takes a while to occur as pages become old and fans grow bored, but in our case, because we have a stalker trolling our page with what appears to be hundres of scripted bot accounts, the effect was seen immediately.

Our data suggests that it became a tug of war between our stalker's army of fake bot accounts (making spammy comments, hiding our posts from newsfeeds, etc) and the real fans that actually like our page (who were voting our conent up - i.e. liking it, etc).

If you look at the graph of page likes in the figures provided - you can see that the darker purple are the fans we obtained via facebook advertising, well over 90%. We believe that the light purple (the "organic" fans) is mostly comprised of our stalker's fake drone accounts. We have less than 20 family members and friends liking our page, when we began this experiment we asked them not to interact with our page or the content.

In conclusion: We still have a lot more work to do, but it is highly likely that many Facebook likes are either scripted bots, and/or that Facebook's "weighting" algorithims are very suceptible to gaming via negative "SEO" tactics. Conversely, they are likely sensitive to gaming via positive "SEO" tactics as well.

Of course we cannot say for certain where the Facebook accounts that like a page come from without acess to their internal systems, but the evidence does strongly suggest that Facebook might be plagued with a large quantity of bot accounts, and that their algorithim has to be sensitive to actions from live users, so that the quality of the content can be easily ascertained. Otherwise it would be pretty easy for an advertiser to game Facebook's system by paying for, and getting, a large quantity of likes for content that is not appealing to any significant group of people.

Again we have to reiterate that we have no solid proof of this, but our data strongly suggests that this is the case.

We have reported the issues to Facebook, but interestingly, after we made it clear that we were going to analyze and investigate the issues with our page, we have been suddenly and incessently plagued with a never ending stream of "technical difficulties" related to our advertising account.

If you'd like to collaborate on this project, please feel free to email me at Jamie@ITSmoleculardesign.com.

In 2022, Facebook generated nearly *** billion U.S. dollars in advertising revenue. This figure is expected to further grow to exceed *** billion U.S. dollars by 2027. The social platform is responsible for roughly ** percent of the global ad revenue.

The concern with this Dataset is that views and likes are our attributs of interest so they shouldn't be loaded as row values. Instead, they must be displayed as column names. As a result, we have addressed this issue in the related notebook.

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.

In 2024, Meta (formerly Facebook Inc) generated over 160 billion U.S. dollars in ad revenues. Advertising accounts for the vast majority of the social network's revenue. Facebook advertising revenue – additional information Facebook’s business model heavily relies on ads, as the majority of social network’s revenue comes from advertising. In 2020, about 97.9 percent of Facebook's global revenue was generated from advertising, whereas only around two percent was generated by payments and other fees revenue. Facebook ad revenue stood at close to 86 billion U.S. dollars in 2020, a new record for the company and a significant increase in comparison to the previous years. For instance, the social network generated almost seven billion U.S. dollars in ad revenue in 2013, about 10 billion less than the 2015 figure. Facebook's average revenue per user also significantly increased in the same time span, going from 6.81 U.S. dollars in 2013 to 32.03 U.S. dollars in 2020. The U.S. and Canada are important markets for Facebook, considering the average revenue per user (ARPU) in these two countries is far above the global average. Facebook’s ARPU in the U.S. and Canada was 41.41 U.S. dollars in the last quarter of 2019, while the global average was 8.52 U.S. dollars. In Europe, Facebook’s average revenue per user was 13.21 U.S. dollars during the same time period. In terms of segments, mobile is the most promising advertising form for the company. In 2018, Facebook’s mobile advertising revenue already accounted for 92 percent of the social network’s total advertising revenue. Facebook’s mobile advertising revenue grew from an estimate of 13 billion U.S. dollars in 2015 to 50.6 billion U.S. dollars in 2018.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16190527%2F53f929ff911508241fa2b6808c9d885f%2FFacebookAdsBidding-1200x630-1.jpg?generation=1696942877786519&alt=media" alt="">

Subject:

Facebook recently introduced a new bidding system called "average bidding"(test group) alongside the existing "maximum bidding"(control group) system. These bidding systems determine which ads get displayed to users based on how much advertisers are willing to pay.

With "maximum bidding," advertisers specify the maximum amount they are willing to pay for each impression. For example, an advertiser might say, "I'm willing to pay a maximum of $10 for each impression."

With "average bidding," advertisers specify an average amount they are willing to pay for impressions. For instance, they might say, "On average, I'm willing to pay $6 for each impression."

Here's the key point: In this dataset, we've gathered the results of these two bidding strategies over the last 40 days to see which one is more effective at getting their ads displayed to the target audience.

Business Challenge: Unleashing the Power of Facebook Advertising

As a forward-thinking company poised to make waves in the realm of Facebook advertising, we're on a mission to unearth the most advantageous approach for our brand. Our burning questions:

1. Maximize Clicks and Purchases: Which strategy will propel us toward our goal of skyrocketing clicks and driving purchases?
2. Battle of Titans: Is there a discernible and statistically significant difference between the two bidding options?

Enter the AB Test: Today, we embark on a journey of data-driven discovery, where the clash of titans—Average Bidding versus Maximum Bidding—will be meticulously dissected and evaluated. We're on a quest for insights that will define our Facebook advertising strategy's future, using data as our compass and innovation as our weapon.

The outcome of this AB Test will not just answer our questions but will be the harbinger of a glorious era in our Facebook advertising endeavors. Stay tuned for a transformation that will leave a mark on the digital advertising landscape.

Features:

• Impression: Number of impressions per ad.
• Click: Numbers of clicks per ad.
• Purchase: The number of products purchased after the click.
• Earnings: Earning after purchase.

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

According to the source's analysis based on more than ** thousand advertising account and more than *** thousand ad campaigns, interactions were the most used Facebook ad objective globally in January and February 2024. Traffic came second, with **** percent.

The ability to run targeted advertisements on popular social media platforms, such as Facebook, has enabled researchers to obtain quickly demographically and geographically diverse samples in response to research opportunities, at a very low cost. A shortcoming of using Facebook is that it employs algorithms to optimize ad delivery by favouring users who are most likely to click on them and, as a result, the characteristics of the end samples are likely to differ from the underlying population. The paper provides a short guide for those interested in recruiting survey participants through Facebook ads and suggestions on improving the characteristics of the end samples, especially on variables for which the ad targeting is less efficient. Empirically, the paper compares a Facebook ads sample to a gold standard (the European Social Survey) and provides open access to the replication data. The paper concludes with suggestions about the suitability of Facebook ads data for different types of research projects.

This dataset contains detailed Facebook advertising data for the political party Sumar and its leader Yolanda Díaz for the period from 9 January to 21 July 2023. This timeframe includes the campaign and pre-campaign periods of the 2023 Spanish general election. The dataset provides a comprehensive overview of the party's advertising strategies on Facebook, including unique ad IDs from Facebook's ad library, average cost per ad, dates, ad text, links, ad categories and estimated reach segmented by age, gender and geography. The dataset also includes information on the languages used in the ads, providing insights into the party's targeting and communication approaches during this crucial election period.

Survey of 620 businesses on social media advertising pricing