Let’s Talk About Facebook Again

In-App Click Fraud Part 2

Facebook In-App Click Fraud - Part 2

Let’s talk about Meta's Facebook again.

Last week’s post showed that Oxford Biochronometrics detected large numbers of fraudulent clicks coming from the Android Facebook app. Without a fraud detection tag on your landing pages these fake clicks look like regular clicks and your campaigns running on Facebook would look successful. Again this proves in digital it is easy to fake things and without any form of protection you would think your campaigns get traction. Yeah, by bots, fraudsters and imposters.

Because we all know talk is cheap, this second post will show some hard data and evidence, real results and breakdowns of data. The first example is based on a large company running many campaigns from many sources simultaneously. The second example is a small company (hospitality), they don’t have much budget so they try to spend it wisely.

How to read the charts and data

Before showing charts, it is also important to know how to read the chart and what information is available in the chart. The charts have the same colors as FouAnalytics, because I like that colorscheme and it is also colorblind proof. I’m not colorblind but have shown these charts to two friends who are red-green color blind.

The top section of a chart is a percentage stacked barchart where each color shows the amount of traffic of that kind. At the left upper corner the clientname is listed, which can be anonimized when choosing not to disclose the clientname. The code next to the name is the time frame. Possible time frames are M2, M5, M15, M30 which stands respectively for 2, 5, 15 and 30 minutes. H1, H4, H12 is one hour, 4 hours, 12 hours, and D1 which is a daily barchart. Each bar represents one time frame, which is listed at the left upper corner. Changing time frames allows to zoom in and zoom out when something happened in the data and you want to look at details. The last part of the label is the pivot value and determines how the data is broken down into segments. The pivotvalue can be stacked to create a compound pivot (maximal 4), eg. you could look at Facebook traffic from Canada on Android devices, or Google traffic from Wisconsin using a specific search term.

The green barchart is the volume of the traffic. It follows the same time frame and is vertically automatically scaled. Between these charts some date-time labels are printed. These are rounded down to the time frame.

The legend would be:

Figure 1. The legend of the charts in the Figures below.

The difference between FouAnalytics and Oxford Biochronometrics is that Oxford Biochronometrics has less ‘looks like bot’ and ‘looks like human’ aka human-ish classification. Only a fraction of the visits will fit this ‘gray area’ where the detection is uncertain what it really is.

The juice

First we’ll start with a large client running multiple campaigns concurrently. The chart is based on traffic arriving at their landing pages (after the click). This means that looking at the totals both paid and organic traffic ar included in the data.

FTSE 100 company

The first chart shows all traffic arriving at their landing pages. It shows some levels of fraud, but also a real good portion human traffic.

Figure 2. All data arriving at the landing pages, both organic and all paid traffic are combined in a single view.

Of course our clients are only interested in: How much paid traffic was fraudulent? So, we’ll need to split that combination chart into organic and paid.

This is how the organic traffic looks like. It contains a lot of bot traffic. Though these bots might have some impact on the infrastructure and costs, but that’s a fraction of the attribution costs.

Figure 3. All organic traffic data arriving at the landing pages.

The same chart but now paid traffic. And this looks much quieter than the organic traffic.

Figure 4. All paid traffic from all sources arriving at the landing pages.

As we would like to know how Facebook traffic looks like, we’ll have to zoom in to only paid traffic and where its source is Facebook. The chart below shows exactly that, all paid facebook traffic arriving at the landing pages.

Figure 5. Paid traffic from Facebook arriving at the landing pages.

As you can see it does contain fraud, but as this is still a combination of Mac, Windows, iOS and Android and whatever OSes, we’ll have to zoom in again. Let’s compare the two mobile OSes, as we’re interested in traffic originating from apps.

Figure 6. Paid traffic from Facebook arriving at the landing pages. The top chart contains all iOS traffic, the bottom chart contains all Android traffic.

In the chart above you can see a MEGA-difference between the topchart (iOS) and the bottom one (Android): iOS is almost without fraud, except for that little spike on October 17. The Android traffic is ~40% fraudulent.

You might also notice that during the weekend paid traffic from Facebook is disabled. This makes perfectly sense, as this client runs their lead generation campaigns only during the weekdays and non holidays.

SME company

First step is to look at all data. The chart below contains daily bars (D1 timeframe) and shows what traffic arrived at their landing pages during the last 6 months. The client does have some fraud, but based on this chart you still don’t know anything.

Figure 7. All traffic paid and organic arriving at a small company's landing page.

It is also clear from the bars that the traffic volume is not bound to weekdays and holidays. Let’s zoom in to the paid sources, because that’s what is most relevant. Ah, it seems that they only buy Facebook traffic.

Figure 8. Paid traffic from Facebook arriving at a small company's landing page

This looks not that good. The amount of fraudulent clicks originating from Facebook is about half, even when the traffic volume is large, around 2023-Nov-21 and in April 2024. Of course the question here is again, which Facebook traffic? So, let’s break down the data to operating system and Facebook traffic only:

Figure 9. Paid traffic from Facebook broken down to iOS (top chart) and Android (bottom chart)

What a surprise!? NOT! The iOS traffic looks very clean, but the Android traffic is a marketing bloodbath, as ~90% of your paid visitors arriving on your landing page(s) are flagged as fraud. What a waste of budget!

I can give many more examples of Facebook traffic and the differences in fraud levels between iOS and Android. But, over and over again the pattern is the same. If people like these charts and examples, not restricted to Facebook, let me know, it depends on the amount of traction this post gets.

Takeaway

Your takeaway: Target iOS, try to avoid Android or do it very selectively. It might be more expensive, but better 50 real humans looking at your product than 1000 bots wasting energy, bandwidth and ruining your campaign goals.

#cmo #facebook #adfraud #digitalmarketing