Ad Fraud and Machine Learning: Maximizing ROI by Minimizing Invalid Traffic

Ad fraud is becoming increasingly complex to detect as every day goes by. The adaptation of processes to avoid detectability has become so sophisticated that it no longer appears as a bot. What we are seeing today is a façade of real human engagement that is increasingly accurate and increasingly profitable.

According to Statista, it is estimated that the global costs relating to digital advertising fraud would grow exponentially between 2018 and 2023 from $35 billion to $100 billion. Ad fraud can not only have detrimental effects on individual businesses but also on the global economy.

Recommended:

Predictions Series 2022: Crypto’s Role in Deciding Future of Application Security Solutions

Blending Ad Fraud and Machine Learning

The most common approach to ad fraud is invalid traffic (IVT), which refers to any clicks or impressions that may artificially inflate an advertiser’s costs. IVT not only consumes ad spend but also compromises data, the very data used to drive improvements in all advertising and marketing efforts. From time-consuming invoice reconciliation to restricted campaign optimization, the often forgotten impacts of fraud are just as damaging to the success of performance advertising as the ad spend it consumes.

Detecting and Mitigating to Save Ad Spends

Fraudsters are becoming increasingly sophisticated in their efforts to evade detection and trick measurement platforms. The challenge is finding a solution that can be integrated into marketing efforts to prevent ad fraud.

While ad fraud solutions are working hard to mitigate fraud, the perpetrators seem to be working even harder to stay ahead. Vendors are facing a vicious cycle that means as soon as one problem is resolved another, more complex one, seems to appear. Businesses need a solution that can adapt to new approaches to ad fraud in real-time.

Read Latest NFT Updates: Online Blockchain plc: Umbria Expands Reach into the NFT Market

Ad fraud is also a very low risk to those committing the crime. There is no formal legislation that can be enforced on ad criminals, so it is an attractive money-maker. On top of this, there is a lack of industry standards and understanding of the ad fraud space. This makes implementing any sort of regulations against ad crime tough for businesses trying to increase return on investment (ROI).

Businesses need to protect their ad spend but understanding what performance data is clean can be difficult in an ever-evolving landscape.

With more and more cases of invalid traffic appearing, how will enterprises recognize which is genuine engagement? 

Integrating with Your Marketing Strategy

The combination of accessible tech, affordability, and skills development means that machine learning is finally at a point where it can be used to solve critical business challenges. This is now evolving into the ad fraud prevention space.

Blockchain News: Fantom Adds DIA’s Open-Source Oracles To Propel DApps With Customisable Data Feeds

A Pay Per Click (PPC) Protection solution is both the identification and the mitigation for IVT. A platform that proactively stops your ads from showing to sources of IVT, and offers unprecedented levels of visibility across all your traffic, will enable businesses to get the most from their data.

With integration options built specifically for marketing technology companies, a real-time ad fraud prevention solution integrated seamlessly with the customer’s own technologies can enable excellent client advertising performance, deliver high-value user acquisition and maximize engagements.

Innovation with Machine Learning

The way forward is the full utilization of multipoint fraud mitigation tools that detect and block invalid traffic surgically, in real-time. This will protect the integrity of the performance data businesses and their partners use for optimization and get marketing campaign budgets working harder to deliver maximum conversions.

While there is no one correct way to apply machine learning into an ad fraud marketing strategy, many businesses with all levels of expertise should use its innovative technologies to tackle fraudsters.

Getting all of the elements of machine learning solutions to perform reliably takes time and a dedicated experienced data science and DevOps team. By integrating an ad fraud solution that uses machine learning into its marketing mix, businesses in the future will be able to:

• Reduce false positives with precision fraud mitigation.
• Reduce false negatives to catch fraud that other vendors or measurement platforms miss.
• Mitigate fraud from known and unknown tactics.
• Drive relentless pursuit to prevent fraud at the earliest possible opportunity, supporting efforts to stop fraudsters from getting paid at the expense of a business.

With a platform that leverages machine learning, IVT can be blocked at both the attribution and click level. Machine learning technology is now able to detect early indications of emerging fraud tactics enabling them to be blocked earlier and minimize losses to ad fraud. In the long-term, this maximizes Return-on-Assets and elevates all future marketing efforts. 

[To share your insights with us, please write to sghosh@martechseries.com]