Install fraud is a tactic used to imitate real user behavior, although there is no genuine intent
Posted: Mon Dec 23, 2024 5:55 am
Install fraud: fake click, fake user
Click fraud: fake click, real user
Compliance fraud: real click, real user
In this article I will take you through the 'easiest' of the three types: install fraud. In the following articles I will go deeper into click fraud and compliance fraud.
What is install fraud?
After clicking on an ad, you are redirected to the app store to download and install an app. This install is where the money is made. That is why companies try to minimize install fraud first. Install fraud is a tactic used to imitate real user behavior, even though there is no genuine intent. This type of fraud is designed to quickly scale a large user base and drive up costs for the marketer. These users are not really interested in using the app. These are real people with malicious intent or bots that generate this traffic.
When you look at the amount of data to analyze, it is easier from a technological perspective to use the installs. With a tool like Excel you are still able to analyze the traffic of your installs (with a maximum of 1 million rows). With an analysis of the clicks, which can amount to numbers above millions per day, an investment in another BI tool is required. Think for example of visualizing with Tableau.
If you also want to use the necessary algorithms hong kong phone numbers to automate fraud warnings (using machine learning ), then investments in knowledge and tools are also required. This will keep you in control of your advertising traffic. So let's dig a little deeper into the type of install fraud.
How do we identify fake installs?
How do we find install fraud in the data? I always try to imagine the user situation. How would I react to an ad? Not everyone clicks on an ad, downloads and installs the app right away and uses it every day. How many times have you clicked on an ad but never downloaded it? How often do you accidentally click on an ad because the page is loading slowly?
By answering these questions, standards can be identified that create a base/average per advertisement (place). The number of clicks that lead to an install can be presented in a conversion ratio (CR) or a clicks-to-install ratio (CTI). These ratios can be used to define an average for real user behavior. Some companies already have two types of install fraud:
App install factories. This is considered a crime for simulating users to drive traffic to an app. Want to see what this looks like? Here’s an example .
Bots and botnets. Devices can be infected by this malicious software to install apps and imitate in-app activity. Here is an example of this.
So, how do we combat this fraud? Let’s look at four dimensions that we can use to identify suspicious install traffic.
Click fraud: fake click, real user
Compliance fraud: real click, real user
In this article I will take you through the 'easiest' of the three types: install fraud. In the following articles I will go deeper into click fraud and compliance fraud.
What is install fraud?
After clicking on an ad, you are redirected to the app store to download and install an app. This install is where the money is made. That is why companies try to minimize install fraud first. Install fraud is a tactic used to imitate real user behavior, even though there is no genuine intent. This type of fraud is designed to quickly scale a large user base and drive up costs for the marketer. These users are not really interested in using the app. These are real people with malicious intent or bots that generate this traffic.
When you look at the amount of data to analyze, it is easier from a technological perspective to use the installs. With a tool like Excel you are still able to analyze the traffic of your installs (with a maximum of 1 million rows). With an analysis of the clicks, which can amount to numbers above millions per day, an investment in another BI tool is required. Think for example of visualizing with Tableau.
If you also want to use the necessary algorithms hong kong phone numbers to automate fraud warnings (using machine learning ), then investments in knowledge and tools are also required. This will keep you in control of your advertising traffic. So let's dig a little deeper into the type of install fraud.
How do we identify fake installs?
How do we find install fraud in the data? I always try to imagine the user situation. How would I react to an ad? Not everyone clicks on an ad, downloads and installs the app right away and uses it every day. How many times have you clicked on an ad but never downloaded it? How often do you accidentally click on an ad because the page is loading slowly?
By answering these questions, standards can be identified that create a base/average per advertisement (place). The number of clicks that lead to an install can be presented in a conversion ratio (CR) or a clicks-to-install ratio (CTI). These ratios can be used to define an average for real user behavior. Some companies already have two types of install fraud:
App install factories. This is considered a crime for simulating users to drive traffic to an app. Want to see what this looks like? Here’s an example .
Bots and botnets. Devices can be infected by this malicious software to install apps and imitate in-app activity. Here is an example of this.
So, how do we combat this fraud? Let’s look at four dimensions that we can use to identify suspicious install traffic.