Fraudulent advertising traffic could be costing you more than just lost budget. Any budget lost to fraud is a lost opportunity for engagement.
Invalid traffic is any advertising engagement that isn’t out of genuine interest in the advertised offering. Malicious invalid traffic, or ad fraud, is often difficult to detect until it’s too late, and then little can be done. As a result, it can drain a marketing budget quickly, with no reward in return.
Juniper Research’s report on the future of digital advertising found that advertisers’ total fraud losses will rise to USD $100b by 2023. This figure doesn’t include the unseen losses, which can be difficult to quantify. Advertisers lose out on budget as well as lost time, poor campaign performance, and lost opportunities for engagement.
Moving into 2022, businesses have accepted the fact that they need to develop and execute an in-depth marketing strategy to increase market share but may not be achieving maximum return-on-investment (ROI) from their digital marketing budgets. Organizations looking to maximize ROI must build a business case for ad fraud detection to get value for money in the new year and beyond.
Innovation Equals ROI
Ad fraud and the sophistication of perpetrators have evolved at a rapid pace in the last 20+ years. As budgets for online advertising have grown, so has the lucrativeness of ad fraud as a business opportunity, attracting more sophisticated players to the arena. Now ad fraud is an industry in its own right, evolving and adapting to continue to outsmart marketers. This industry will only continue to thrive as time goes on.
For businesses, it’s becoming harder and harder to add to blacklists and manage increasingly complex rule sets, without catching legitimate traffic in the crossfire. There are significant barriers to detecting ad fraud such as difficulty differentiating between a genuine and a fake click, evolving methods used by fraudsters, and a lack of industry standards against it.
Without proactive measures, the consequences of ad fraud will be exacerbated further. In addition to the wasted media spend already mentioned, fraud will inflate volume metrics, making low-quality sources appear to be high performing. The result will be advertisers unknowingly increasing investment in sources of invalid traffic, compounding losses further.
Fighting Ad Fraud
Fraud prevention will be the best way to beat these organizations in 2022. By stopping the fraud, fraudsters don’t get paid and the business of fraud is made less profitable. To build a successful business case for ad fraud detection, you have to weigh up which options work for your business.
Blacklists are an important part of any ad fraud defense because they quickly identify sources that categorically don’t have human traffic such as servers. Blacklists are a very basic first step as fraudsters can easily circumvent them by changing the IP addresses of their traffic. Also, when blacklisting IPs that can have human traffic rather than isolating the fraud itself, blacklists can actually result in high volumes of false positives.
· Rule-based detection and mitigation
Rule-based mitigation involves identifying characteristics and thresholds that, when exceeded, block traffic or a traffic source. Rule-based mitigation is appropriate when you know the characteristics that define a particular fraud tactic, such as an impossibly short click to install time. However, it is near impossible to formulate rules for fraud tactics that you have never encountered before. For a rule to be created, a new fraud type must be observed at scale which means it is impacting your budget and taking up time. Rules are reactive – a new fraud type exists, then a rule is created. It is always fraud first, then rule.
· Machine learning
A subset of artificial intelligence, machine learning extracts patterns and relationships from data and expresses them as a formula that can be applied to new data sets. Over time, as the data changes, new patterns are learned by the model without the need to explicitly program them. Because of the scale of data processed, insights can be more valuable and derived much faster using machine learning than by using human analysis alone.
Machine learning can provide more thorough and efficient analysis. In fact, according to research by TrafficGuard, machine learning, by 2022, could save advertisers over $10b a year in ad spend that would have been wasted on fraud. Instead of reacting to fraud as it evolves with new rules, machine learning can be part of a proactive defense that is tactic-agnostic, more accurate, and able to stop fraud before the fraudster gets paid.
Balancing the Business Case
These key options should all be considered in a valuable business case that accurately weighs up benefits, risks, and costs. As digital transformation evolves, ad fraud will only increase in sophistication and severity, impacting advertisers’ efforts to secure a return on their advertising spend.
Ultimately, machine learning provides a win-win for organizations looking to strike a balance between effectiveness and affordability, as businesses can easily access powerful infrastructure on a scalable subscription model. As we progress into the next year, organizations must prioritize prevention, not just detection, if they are to manage the risks to marketing budgets at such a critical time for businesses.
This article is written by Luke Taylor, chief operating officer of digital ad verification and fraud prevention, TrafficGuard.
The article is published as part of MARKETECH APAC’s thought leadership series What’s NEXT. This features marketing leaders sharing their marketing insights and predictions for the upcoming year. The series aims to equip marketers with actionable insights to future-ready their marketing strategies.
If you are a marketing leader and have insights that you’d like to share with regards to the upcoming trends and practices in marketing, please reach out to [email protected] for an opportunity to have your thought-leadership published on the platform.