Detecting Click Fraud in Online Advertising
Online advertising is a multi-billion dollar industry. It has become an essential part of many businesses marketing strategies and is growing rapidly. Online advertising is the ideal choice for small and large businesses to effectively target the appropriate marketing segments on the fly. The main coordinator in this setting is the ad networks, acting as a broker between advertisers and publishers. An advertiser plans a budget, provides the ad network with advertisements, and agrees on a commission for every customer action (e.g., clicking an ad, filling out a form, bidding in an auction, etc). A publisher contracts with the ad networks to display advertisements on their websites, and gets commissions based on the traffic it drives to the advertisers. However, with the increasing popularity of online advertising, the issue of click fraud has also emerged.
What is Click Fraud?
Click fraud is a fraudulent activity that involves generating false clicks on an online advertisement. Click fraud can be committed by individuals, competitors, or automated bots. These clicks are not genuine, and they do not represent any real interest in the advertised product or service.
Research says, there is one out of four clicks is a fraud.
How Click Fraud Works?
Bots — These are computer programs that are designed to generate clicks on online advertisements. They can be programmed to click on a particular advertisement repeatedly, which inflates the number of clicks and impressions. Bots can also be programmed to mimic real user behaviour, which makes it harder to detect fraud.
Click Farms — Click farms are organizations that pay individuals to generate clicks on online advertisements. These farms usually operate in countries with low labour costs, and they use a large number of low-cost labourers to generate a significant number of clicks.
Hidden ads in 1x1 pixel — Create hidden ads in 1x1 pixel, making them too small to be seen by human eyes. Even if end users do not realize it, they view multiple ads and click on them unknowingly.
Hijacking Ads — This type of click fraud can achieve by inserting an onClick event on the iframe with the ad OR compromise the user’s computer by changing the DNS resolver.
Click Injections — This type of click fraud is targeted at the attribution of an application installation. It is done through the sneak install of a fraud app disguised as a real one. When the other apps are installed, the fraud app overtakes tracking codes and attributes these installs as one that occurred because of it.
Consequences
Wasted Advertising Budget — Click fraud can result in wasted advertising spend since businesses end up paying for clicks that do not convert to genuine leads or sales.
Loss of Credibility — Click fraud can lead to a loss of credibility since businesses may be seen as misleading potential customers.
Reduced Conversion Rates — Click fraud can negatively impact conversion rates since businesses may be targeting individuals who are not genuinely interested in their products or services.
Fraud Click Solution
Click fraud can be extremely harmful to businesses as it drives up advertising costs, wastes resources, and can result in incorrect data analytics. Therefore, detecting and preventing click fraud is of utmost importance for online advertisers. This is where data mining comes into play.
One of the common way to detect fraudulent clicks is by identifying the suspicious IPs in data from where you have received more clicks and filter out those clicks.
Data mining is a process of discovering patterns and trends in large datasets. It involves applying statistical and machine learning techniques to extract useful information from data. In the context of click fraud detection, data mining can help to identify fraudulent clicks by analyzing patterns in click data.
There are several approaches to detecting click fraud using data mining techniques. One common method is to use supervised learning algorithms to train a model to recognize fraudulent clicks. Supervised learning involves providing the algorithm with labelled data (i.e., clicks that are known to be fraudulent or not), and the algorithm then learns to recognize patterns in the data that indicate whether a click is fraudulent or not. Once the model is trained, it can be used to detect click fraud in real-time.
Another approach is to use unsupervised learning algorithms to identify anomalies in click data. Anomaly detection involves identifying patterns that are different from the norm. In the context of click fraud, anomalies could be clicks that are significantly different from the typical behaviour of users. For example, if a user clicks on an ad multiple times within a short period, it could be an indicator of click fraud. Unsupervised learning algorithms can be used to identify such anomalies and flag them for further investigation.
According to research, Click fraud will always exist in every ad network, and it is always recommended to work on them after improving traffic at the campaign level.
If you liked this article and want to read more, don’t forget to follow me.
