Click fraud has been a problem since the earliest days of PPC marketing. The digital marketing industry is enormous, making the potential gains for fraudsters tough to ignore. Unfortunately, while click fraud has been around for a long time now, it is only recently that we have become serious about fighting back.
Every business today that spends a portion of its budget on digital marketing needs to watch for ad fraud. If you aren’t actively fighting back, you can be sure that at least some of your money will enrich fraudsters.
Of all the tools now available to us in the fight against ad fraud, machine learning is one of the most promising. Using machine learning has enabled us to detect ad fraud, even when the fraudsters are going to great lengths to hide their activities.
However, there are many factors that come into play when combatting click fraud, and ML technology can only take businesses so far. We explore the benefits of this solution and how it can help in the fight against click fraud, but only if it is fed transparent data and used by experienced experts.
Defining Machine Learning
Machine learning is a specific application of artificial intelligence. Many people use the terms interchangeably, but they are two different things. All machine learning is artificial intelligence in action. However, there are lots of applications of artificial intelligence that are nothing to do with machine learning.
As the name implies, machine learning focuses on producing computer programs that can grow, learn, and evolve. Researchers achieve this by giving machine learning algorithms vast databases of data. The researchers then train the algorithm to identify patterns within the data. Because computers can pick up on patterns that a human would never be able to locate, machine learning can achieve things that previously seemed prohibitively difficult, if not impossible.
For example, a decade or two ago, it would have seemed virtually impossible to have a computer accurately identify whether it is looking at an apple or not. But with machine learning, we can feed an algorithm millions of pictures of apples and millions of things that aren’t apples. We can then show the algorithm any image, and it will decide whether it is an apple.
The example above is a simple implementation of machine learning. However, self-driving vehicle designers use the same technology to train self-driving vehicles to identify obstacles and road signage around them reliably enough for us to trust them to drive for us.
What Is Click Fraud?
Click fraud refers to a range of techniques that malicious actors can use to manipulate PPC campaigns. These techniques include generating fake clicks and pageviews, loading multiple ads simultaneously in a single frame without the website’s knowledge, or even generating fake reports to make a campaign look more effective than it really was.
Anyone involved in the PPC marketing chain can be involved in click fraud. Because of this, digital marketers and the businesses that engage their services must be vigilant when it comes to click fraud.
For example, a competitor could write a simple script to keep clicking on your ads, thereby driving up the cost of bidding for keywords. Publishers can easily inflate the clicks and page views that ads they place on their website generate. They can then charge a higher premium for ad space on their platform.
Even customers with no ill-intentions at all can end up contributing to ad fraud. Some people regularly click on the paid search ads in Google to get what they want, especially when the user searches for a website that appears in the sponsored results but doesn’t rank highly otherwise.
How Can We Combat Click Fraud?
Click fraud isn’t exactly a new problem. For as long as people have been paying to advertise online, there have been people taking advantage of them. The fact that click fraud continues to be a problem is a testament to how lucrative it can be for fraudsters. Fortunately, there are numerous simple things you can do to combat click fraud.
You can counteract much of the click fraud that would otherwise harm you by implementing simple IP-blocking. Lazy fraudsters don’t bother using a proxy service, and you can detect their activity because you will see many requests coming from the same IP address. Smarter fraudsters will take steps to disguise their activity. But while it is easy to trick a person, it is much harder to fool a machine learning algorithm.
What Role Does Machine Learning Have to Play?
Machine learning is proving to be one of the most powerful weapons that we have in the fight against click fraud, as long as it is fed transparent data. As we mentioned earlier, one of the things that machine learning is particularly good at is detecting patterns. Within the world of PPC marketing, we can use machine learning algorithms to analyze user behavior and intentions.
Machine learning algorithms won’t just analyze user behavior. They will also generate detailed reports for a human overseer to review. There are lots of ad fraud detection software options on the market now.
The Human Touch
Machine learning has a role to play in combatting ad fraud, but machine learning alone is not enough. You should be suspicious of any business claiming to combat PPC click fraud using machine learning alone. As Clickguard explains, machine learning for click fraud is essential, but you have to have accurate data to ensure that the tools operate correctly. Without proper management, these tools won’t benefit you; on the contrary, they could cause harm. That’s why the company, unlike its competitors, doesn’t use machine learning and AI for the prevention of click fraud; and instead focuses on reliable data. The firm is then able to offer a superior service that is data-driven and achieves the results that clients expect.
Machine learning has come a very long way in recent years. Some of its current applications are utterly mind-blowing. But even the most advanced artificial intelligence is imperfect. There are certain types of tasks that artificial intelligence and machine learning algorithms can tackle far more efficiently than a human ever could. But there remain many puzzles that a computer finds very difficult, despite being relatively simple for a person.
An excellent example of this is Foldit, a game about protein folding. Medical researchers have struggled to produce an algorithm or software solution that can fold proteins properly. However, they have managed to create a puzzle game where the players have to identify the correct way of folding proteins. A person can do this with minimal instruction, whereas a computer struggles no matter what.
When it comes to combatting click fraud effectively, a combination of artificial intelligence and human intelligence is the best approach. Technology can help in the fight against click fraud, but only to an extent.
Machine learning is undoubtedly one of the most important emerging technologies of our time, but it needs to be used correctly. We are only just beginning to scratch the surface of what machine learning is capable of; who knows where we will be in another decade? The digital marketing industry is vast. Billions of dollars are spent every year on digital marketing services. Ad fraud harms everyone except the fraudsters. Website and businesses suffer from having to pay money for fraudulent views and clicks. Fraudsters harm marketers by making their work less effective. Finally, fraudsters harm us all when they force a frame to load more ads than it is supposed to, whether we see them or not.
As your business grows, so will your digital marketing spend. If you are spending a substantial amount on PPC advertising, you need to also invest in fraud detection and prevention. If you don’t, you can all but guarantee that a portion of your digital marketing spend will be enriching fraudsters. A machine learning-driven approach, coupled with human oversight, is the most effective solution.
Ultimately, machine learning relies on quality data and insightful management to help businesses to prevent click fraud. AI and ML tools are great, but they only get you so far; if they’re used incorrectly or fed unreliable data, then they could cause more harm than good. Collaborate with click fraud experts to ensure that you use the best tools on the market in the right way. When combating click fraud, ML can help, but it relies on other factors such as quality data and consistent updates. As such, you need to make sure that you don’t focus exclusively on ML tech when trying to reduce the impact of click fraud on your organization. If in doubt, focus on data, so that you can use this information to drive your approach to combating click fraud.
