Verify, Quantify, and Protect Advertising Budgets: Adding Anti-Fraud Strategies to Your Marketing Mix

Widely used in the cyber security space already, ad tech seems to only just be catching on to the value of machine learning in ad fraud prevention. This article explains why ad fraud has to be combated and what tools are needed to ensure campaigns deliver maximum ROI.

Verify, Quantify, and Protect Advertising Budgets: Adding Anti-Fraud Strategies to Your Marketing Mix
Photo by Mikael Blomkvist from Pexels

By, Matt Sutton

Fraud is a constantly moving target. In advertising, ignorance on a grand scale has historically allowed fraudsters to make a lot of money for a very long time and gain a substantial head start on anti-fraud providers. This is because sophisticated ad fraud doesn’t look like bots. On the surface, it looks like human engagement. Fraudsters adapt their processes to circumvent rules-based fraud detection and their profitability is driven up by their ability to evolve.

Businesses are now wising up to the fact that ad fraud must be combated with tools that go beyond detection. Juniper Research estimates that an advertiser with no detection or protection in place running a $10 million advertising campaign will, on average, waste $2.6 million of this spend on fraudulent activities.

Anti-fraud solutions have emerged as a critical component of digital campaigns to ensure campaigns deliver maximum return on investment (ROI), especially at a time when the nature of ad fraud is maturing so rapidly.

 

A Scenario Without Protection

Firstly, it’s important to understand the responses to invalid traffic (IVT) businesses have available, and the end result of each. The degree of loss to which advertisers are subject will vary depending on their approach to managing IVT, occurring when interactions with advertising are not from legitimate consumers. These options are:

  • No reporting: When an advertiser chooses to turn a blind eye to IVT.
  • Reporting only: When an advertiser has tools in place to detect and report on IVT. This reactive approach enables the advertiser to take some action after the fraud has occurred, although the damage may already have been done.
  • Single level blocking: Rudimentary fraud prevention, where fraud is blocked at a specific stage in the user journey. When fraud is blocked, many of its impacts are reduced. However, this approach means that fraud can still skew performance data and impact optimization.
  • Multi-level blocking: Proactively blocking fraud as soon as it is detected results in the most comprehensive level of protection.

 

Comprehensive Ad Fraud Coverage

Multipoint fraud prevention tools analyze hundreds of fraud indicators per ad transaction across both the click and attribution levels to detect and block IVT. This is because only blocking can truly stem the flow of ad spending to fraudulent IVT. With a multipoint approach, IVT is blocked as it is detected, as opposed to waiting for the attribution after the fact.

Employing multipoint prevention enables real-time blocking of fraud before spending is attributed to incorrect sources; reducing the need for conversion volume reconciliation and therefore mitigating the risk of litigation. Reporting from a multipoint analysis tool ensures the most accurate and timely understanding of quality for fast and effective optimization.

Many factors contribute to the capabilities of an ad fraud prevention solution, so understanding how to identify a proactive solution is critical in comprehensively defending your campaigns from ad fraud.

Proactive Defense

Widely used in the cyber security space already, ad tech seems to only just be catching on to the value of machine learning (ML) in ad fraud prevention. Juniper Research forecasts that by 2022, machine learning could save advertisers over $10 billion a year in ad spend that would have been wasted on fraud. 

Only ad fraud prevention solutions that both detect and prevent IVT can fully address advertising fraud to reduce the scale of misplaced ad spending. If the correct platform is not deployed, advertising budgets will continue to be subject to fraud in all its evolving forms; leaving advertisers further out of pocket. Instead of reacting to fraud as it evolves with new rules, machine learning can be part of a proactive defence that is tactic-agnostic, more accurate and able to stop fraud before the fraudster gets paid.

Only ML is capable of analyzing the volumes of data required to predict the likelihood of fraud in real-time. The speed, efficiency and accuracy of these solutions mean they are able to handle the vast amounts of data that need to be processed to detect fraudulent activities.

Delivering Campaign Performance

For businesses wanting to best protect their assets, a comprehensive ad fraud solution is necessary to match the evolving threat of malicious invalid traffic. Access to powerful and scalable infrastructure via affordable subscription models has made ML feasible for a broader array of applications. If you’re developing a marketing strategy or budget, considering ML is an absolute must. By blocking IVT, advertisers can optimize high-quality traffic sources in real-time, driving overall campaign performance and ensuring they get the most out of their marketing budget.

Matt Sutton is the Global Chief Revenue Officer at TrafficGuard