Ad fraud is very bad. But no matter how big the number reported, brands often don't think it affects them -- i.e. it's someone elses' problem. Here are 3 case studies of marketers taking a look for themselves and solving ad fraud by putting in place best practices and processes to continuously monitor and reduce fraud, without using fraud detection tech.
When Safari ITP (intelligent tracking prevention) kicked in and stopped 3rd party tracking, the number of real devices that could be targeted should have gone down. The hypothesis is that bots (fake safari) have filled the gap and are not generating a large portion, if not the majority, of programmatic impressions (lower cost)
most buyers who buy in programmatic channels think they are getting enormous "reach" -- i.e. their ads are shown on many sites; but this data shows the exact opposite is true. Their ads are being shown on a small number of sites (less than 1,000); the buyers might as well have bought more direct from good publishers.
This document describes how page redirects are being used to generate fake traffic originating from thin air, allowing fraudulent traffic to go undetected by fraud detection techniques because no bots are required. It provides examples of networks of fake sites that redirect traffic between each other to generate high volumes of impressions and expose major advertisers to fraud. Normal traffic patterns of legitimate sites like CNN and Weather.com are also shown for comparison.
“In addition to the ad fraud itself, bad guys make money by selling the “picks and shovels” too – e.g. bots, traffic, clicks, malware, fake apps, etc. They have an entire ecosystem to extract value. What follows are just a few examples, scratching the surface.”
The "Badtech Industrial Complex" was built on "surveillance marketing" which comes from the misguided notions of the long tail, hypertargeting, and behavioral targeting. Ad tech and supporting services were designed with a singular goal - to extract as much value as possible from the digital marketing supply chain.
A new balance is required for the future of the Internet.
Using Google Analytics to find abnormal traffic and fraud; this is a how-to, to get hourly charts instead of daily rolled-up or averaged data, which hides the fraud.
This document outlines marketing strategies for German companies in 2017/2018. It discusses how everyone is now online and using apps, with implications for the German economy. German companies need to consider performance marketing and deconstruct what makes successful products and brands effective. Specifically, companies should focus on having a great product/market fit, popular stores and events, easy access and returns policies, working with niche influencers, podcast advertising, and marketing on Amazon.
Ad fraud is very bad. But no matter how big the number reported, brands often don't think it affects them -- i.e. it's someone elses' problem. Here are 3 case studies of marketers taking a look for themselves and solving ad fraud by putting in place best practices and processes to continuously monitor and reduce fraud, without using fraud detection tech.
When Safari ITP (intelligent tracking prevention) kicked in and stopped 3rd party tracking, the number of real devices that could be targeted should have gone down. The hypothesis is that bots (fake safari) have filled the gap and are not generating a large portion, if not the majority, of programmatic impressions (lower cost)
most buyers who buy in programmatic channels think they are getting enormous "reach" -- i.e. their ads are shown on many sites; but this data shows the exact opposite is true. Their ads are being shown on a small number of sites (less than 1,000); the buyers might as well have bought more direct from good publishers.
This document describes how page redirects are being used to generate fake traffic originating from thin air, allowing fraudulent traffic to go undetected by fraud detection techniques because no bots are required. It provides examples of networks of fake sites that redirect traffic between each other to generate high volumes of impressions and expose major advertisers to fraud. Normal traffic patterns of legitimate sites like CNN and Weather.com are also shown for comparison.
“In addition to the ad fraud itself, bad guys make money by selling the “picks and shovels” too – e.g. bots, traffic, clicks, malware, fake apps, etc. They have an entire ecosystem to extract value. What follows are just a few examples, scratching the surface.”
The "Badtech Industrial Complex" was built on "surveillance marketing" which comes from the misguided notions of the long tail, hypertargeting, and behavioral targeting. Ad tech and supporting services were designed with a singular goal - to extract as much value as possible from the digital marketing supply chain.
A new balance is required for the future of the Internet.
Using Google Analytics to find abnormal traffic and fraud; this is a how-to, to get hourly charts instead of daily rolled-up or averaged data, which hides the fraud.
This document outlines marketing strategies for German companies in 2017/2018. It discusses how everyone is now online and using apps, with implications for the German economy. German companies need to consider performance marketing and deconstruct what makes successful products and brands effective. Specifically, companies should focus on having a great product/market fit, popular stores and events, easy access and returns policies, working with niche influencers, podcast advertising, and marketing on Amazon.
Do you think fraud detection tech works? Consider this. Bad guys are hackers. They have better tech and are always 1 step ahead of good guys trying to detect and catch them.
Here are some questions to ask of fraud detection vendors so you can tell if you are getting ripped off and if they can actually do what they claim to be doing.
why do hackers hack? of course, it's fun for them. But they hack to make tons of money with their unique skillset. How does hacking connect to ad fraud? Here are a few examples.
Everyone is paying for fraud detection, but without enough technical knowledge, they don't realize the fraud detection doesn't work or is easily tricked by the bad guys. So what's worse is that the people paying for fraud detection have a false sense of security and take their eyes off of the obvious fraud that is still getting through.
This document is a presentation on digital marketing and fraud from November 2018. It discusses how current fraud detection techniques can be easily blocked or tricked by bad actors. It provides examples of how botnets, fake sites and apps, and domain spoofing are used to generate invalid traffic and are not properly detected. It also notes that advertising dollars can end up funding illegal sites due to these issues with fraud detection technologies.
This document provides an overview of the history and global impact of digital ad fraud. It discusses how fraudsters are able to generate infinite fake ad impressions through virtual ads rather than real billboards. The document outlines how major brands like Chase and P&G cut digital ad spending significantly without negative effects after removing fraudulent sites. It also examines how ad fraud has contributed to the decline of local journalism and thousands of newspaper closures. Throughout, it encourages analyzing your own analytics and reports to identify abnormal patterns that may indicate fraud.
Ellen Pao was right. I have seen and documented the fake stuff since at least 2013. In digital, virtually unlimited fake accounts, fake traffic, fake users, fake ad impressions, etc. can be created. How many of the 50 slides in this deck were you familiar with?
Procurement Should STEP IN to Help Fight Ad Fraud
The document outlines five things procurement can do to help fight ad fraud: 1) Don't buy low cost media and insist on quality publishers with real human audiences; 2) Insist on reports with line item details to know where ads are running; 3) Use continuous fraud detection as a "smoke detector"; 4) Optimize media towards reaching humans; and 5) Focus on business outcomes rather than metrics that can be faked. The author, Dr. Augustine Fou, is an independent ad fraud researcher who earned a PhD from MIT and previously worked at American Express and Omnicom crafting his expertise in digital marketing fraud investigation.
How many of these hidden costs were you aware of?
Without even talking about digital ad fraud, there are other costs in the digital ad supply path that eat up most of every dollar that advertisers spend in digital. There are known costs of 60 - 70% extracted by adtech middlemen.
This document discusses various ways in which digital advertising can be fraudulent, including fake sites pretending to be real domains to get bids, ads running on only a few sites, geotargeting that results in impressions from fake locations, frequency caps and pacing not actually being followed, and viewability metrics not accurately representing what users actually see. It notes that fraud goes beyond invalid traffic and that only around 60 cents of every dollar spent on digital advertising actually goes to media. The document is authored by Augustine Fou, a digital marketer of over 23 years who audits campaigns for fraud.
Fraudsters are able to successfully sell fraudulent ads by tricking fraud detection methods. Current detection tools have limitations and are easily fooled by techniques like stacking ads above the fold or using fake traffic sources. One fraud operation, Methbot, was able to avoid detection for years by disguising fraudulent publishers and simulating human behaviors. Measurement of ad views, viewability, and other metrics can also be plainly incorrect depending on tag placement. Fraud is a significant problem, especially on mobile and through fake inventory sold on ad exchanges.
what are things that performance marketers can do themselves to reduce their exposure to ad fraud? They dont need specialized verification tech; they just need to know where to look in their own analytics and what to look for. Here are some starting points.
This document discusses digital ad fraud and outlines actions that various parties can take to address it. It summarizes that digital ad fraud is a major problem, provides high returns for criminals, and funds other illegal activities. It recommends that marketers reduce digital ad spend until they see a business impact, ad networks enforce policies against common fraud techniques, publishers filter bots to reduce invalid traffic, and governments introduce new laws against fraud and investigate financial records.
The document discusses the issues caused by "badtech" in digital advertising, including how it has harmed local news publishers and stolen ad revenue. Fake local news sites committed ad fraud for years by tricking users and advertisers. Several case studies show large brands like P&G, Chase, and Uber cutting digital ad spending by 80-99% with no negative impact on performance, indicating much of their previous spending was wasted on fraudulent sites and bots. The document argues for advertisers to buy directly from good publishers who have real human audiences, rather than through ad exchanges, to avoid issues like ad fraud, privacy violations, and most of the marketing budget being extracted as fees rather than going to media. Good publishers are identified by
Marketers can look in their own analytics and detailed reports to identify ad fraud and clean it up. There is little need to use 3rd party fraud detection tech that is proven to not work.
This document discusses the state of digital ad fraud. It finds that ad fraud is extremely lucrative and scalable, with profit margins of 80-99% for fraudsters. Fraud operations are also massively scalable through techniques like using thousands of fake websites and bots. Digital ad fraud is now one of the largest forms of crime, estimated at $31 billion annually in the US alone. The document examines how fraud harms the digital ad ecosystem and good publishers through stolen ad revenue and bad measurements. It finds current bot and fraud detection capabilities still limited despite the scale of the problem.
Ad fraud steals ad budgets and negatively impacts the class action notice industry - ads are not put in front of humans, but instead are shown to bots (software programs that load webpages). Bot don't complete claim forms; humans do.
Despite the use of fraud detection technologies, notice providers should use "best practicable" actions to verify the campaign analytics to see if ad fraud still gets through.
This document discusses forensic auditing of digital media using FouAnalytics. It describes analyzing discrepancies between bids won, ads served, and ads displayed. Drop-offs between these metrics indicate issues. The document also discusses evaluating accuracy of ad delivery in terms of location targeting, ad sizes, and frequency caps. Additionally, it covers analyzing when and where ads are served, including dayparting and reach across sites. Finally, the document examines ad quality factors like context, exposure to humans vs bots, and viewability.
This document provides a summary of digital ad fraud trends in Q1 2022. It discusses how ad fraud goes beyond just bots (IVT) to include other forms of fraud by sites and apps that are underreported. While industry associations only report on IVT rates around 1-3%, the document argues overall fraud rates are much higher. It also discusses the negative impacts of ad fraud on publishers, consumers, and advertisers. For publishers, it leads to lower revenues and CPMs. It also discusses how ad fraud has harmed local news organizations. For advertisers, it discusses examples like P&G and Chase cutting digital ad spend with no impact on performance. The document provides recommendations for publishers to sell direct inventory,
Do you think fraud detection tech works? Consider this. Bad guys are hackers. They have better tech and are always 1 step ahead of good guys trying to detect and catch them.
Here are some questions to ask of fraud detection vendors so you can tell if you are getting ripped off and if they can actually do what they claim to be doing.
why do hackers hack? of course, it's fun for them. But they hack to make tons of money with their unique skillset. How does hacking connect to ad fraud? Here are a few examples.
Everyone is paying for fraud detection, but without enough technical knowledge, they don't realize the fraud detection doesn't work or is easily tricked by the bad guys. So what's worse is that the people paying for fraud detection have a false sense of security and take their eyes off of the obvious fraud that is still getting through.
This document is a presentation on digital marketing and fraud from November 2018. It discusses how current fraud detection techniques can be easily blocked or tricked by bad actors. It provides examples of how botnets, fake sites and apps, and domain spoofing are used to generate invalid traffic and are not properly detected. It also notes that advertising dollars can end up funding illegal sites due to these issues with fraud detection technologies.
This document provides an overview of the history and global impact of digital ad fraud. It discusses how fraudsters are able to generate infinite fake ad impressions through virtual ads rather than real billboards. The document outlines how major brands like Chase and P&G cut digital ad spending significantly without negative effects after removing fraudulent sites. It also examines how ad fraud has contributed to the decline of local journalism and thousands of newspaper closures. Throughout, it encourages analyzing your own analytics and reports to identify abnormal patterns that may indicate fraud.
Ellen Pao was right. I have seen and documented the fake stuff since at least 2013. In digital, virtually unlimited fake accounts, fake traffic, fake users, fake ad impressions, etc. can be created. How many of the 50 slides in this deck were you familiar with?
Procurement Should STEP IN to Help Fight Ad Fraud
The document outlines five things procurement can do to help fight ad fraud: 1) Don't buy low cost media and insist on quality publishers with real human audiences; 2) Insist on reports with line item details to know where ads are running; 3) Use continuous fraud detection as a "smoke detector"; 4) Optimize media towards reaching humans; and 5) Focus on business outcomes rather than metrics that can be faked. The author, Dr. Augustine Fou, is an independent ad fraud researcher who earned a PhD from MIT and previously worked at American Express and Omnicom crafting his expertise in digital marketing fraud investigation.
How many of these hidden costs were you aware of?
Without even talking about digital ad fraud, there are other costs in the digital ad supply path that eat up most of every dollar that advertisers spend in digital. There are known costs of 60 - 70% extracted by adtech middlemen.
This document discusses various ways in which digital advertising can be fraudulent, including fake sites pretending to be real domains to get bids, ads running on only a few sites, geotargeting that results in impressions from fake locations, frequency caps and pacing not actually being followed, and viewability metrics not accurately representing what users actually see. It notes that fraud goes beyond invalid traffic and that only around 60 cents of every dollar spent on digital advertising actually goes to media. The document is authored by Augustine Fou, a digital marketer of over 23 years who audits campaigns for fraud.
Fraudsters are able to successfully sell fraudulent ads by tricking fraud detection methods. Current detection tools have limitations and are easily fooled by techniques like stacking ads above the fold or using fake traffic sources. One fraud operation, Methbot, was able to avoid detection for years by disguising fraudulent publishers and simulating human behaviors. Measurement of ad views, viewability, and other metrics can also be plainly incorrect depending on tag placement. Fraud is a significant problem, especially on mobile and through fake inventory sold on ad exchanges.
what are things that performance marketers can do themselves to reduce their exposure to ad fraud? They dont need specialized verification tech; they just need to know where to look in their own analytics and what to look for. Here are some starting points.
This document discusses digital ad fraud and outlines actions that various parties can take to address it. It summarizes that digital ad fraud is a major problem, provides high returns for criminals, and funds other illegal activities. It recommends that marketers reduce digital ad spend until they see a business impact, ad networks enforce policies against common fraud techniques, publishers filter bots to reduce invalid traffic, and governments introduce new laws against fraud and investigate financial records.
The document discusses the issues caused by "badtech" in digital advertising, including how it has harmed local news publishers and stolen ad revenue. Fake local news sites committed ad fraud for years by tricking users and advertisers. Several case studies show large brands like P&G, Chase, and Uber cutting digital ad spending by 80-99% with no negative impact on performance, indicating much of their previous spending was wasted on fraudulent sites and bots. The document argues for advertisers to buy directly from good publishers who have real human audiences, rather than through ad exchanges, to avoid issues like ad fraud, privacy violations, and most of the marketing budget being extracted as fees rather than going to media. Good publishers are identified by
Marketers can look in their own analytics and detailed reports to identify ad fraud and clean it up. There is little need to use 3rd party fraud detection tech that is proven to not work.
This document discusses the state of digital ad fraud. It finds that ad fraud is extremely lucrative and scalable, with profit margins of 80-99% for fraudsters. Fraud operations are also massively scalable through techniques like using thousands of fake websites and bots. Digital ad fraud is now one of the largest forms of crime, estimated at $31 billion annually in the US alone. The document examines how fraud harms the digital ad ecosystem and good publishers through stolen ad revenue and bad measurements. It finds current bot and fraud detection capabilities still limited despite the scale of the problem.
Ad fraud steals ad budgets and negatively impacts the class action notice industry - ads are not put in front of humans, but instead are shown to bots (software programs that load webpages). Bot don't complete claim forms; humans do.
Despite the use of fraud detection technologies, notice providers should use "best practicable" actions to verify the campaign analytics to see if ad fraud still gets through.
Similar to Diy CTV and OTT Ad Fraud Prevention (20)
This document discusses forensic auditing of digital media using FouAnalytics. It describes analyzing discrepancies between bids won, ads served, and ads displayed. Drop-offs between these metrics indicate issues. The document also discusses evaluating accuracy of ad delivery in terms of location targeting, ad sizes, and frequency caps. Additionally, it covers analyzing when and where ads are served, including dayparting and reach across sites. Finally, the document examines ad quality factors like context, exposure to humans vs bots, and viewability.
This document provides a summary of digital ad fraud trends in Q1 2022. It discusses how ad fraud goes beyond just bots (IVT) to include other forms of fraud by sites and apps that are underreported. While industry associations only report on IVT rates around 1-3%, the document argues overall fraud rates are much higher. It also discusses the negative impacts of ad fraud on publishers, consumers, and advertisers. For publishers, it leads to lower revenues and CPMs. It also discusses how ad fraud has harmed local news organizations. For advertisers, it discusses examples like P&G and Chase cutting digital ad spend with no impact on performance. The document provides recommendations for publishers to sell direct inventory,
Desktop ad blocking rates on business sites range from 9-16% of traffic share, which is 50-70% of total traffic. Mobile business site ad blocking is much lower at 1-2% of their 30-50% traffic share. For consumer sites, desktop ad blocking is 3-10% of their 15-30% traffic, while mobile consumer site ad blocking is even lower at 0.8-1% with mobile traffic being 70-85% of total consumer traffic.
how the money flows from the advertisers through the ad tech intermediaries to longtail, fraud, and fake sites, with the help of botnets and traffic sellers
In 2021 some marketers are still asking whether ad fraud is real and whether it is pervasive. This serves as a simple reminder of some of the evidence collected over the years.
bad guys started with fake websites, then moved to loading ads only to save time and bandwidth; now they are simply faking bid requests and flooding exchanges
Previous studies that addressed the impact of losing third party (“3P”) cookies on ad revenue did not clearly differentiate between the impact on ad tech intermediaries versus on publishers. Instead of “advertiser CPMs” (what advertisers pay) this study uses “media CPMs” (what the publishers get) to better isolate the impact of tracking vs no tracking on publishers.
The original idea of the digital media trust collaborative is was sharing threat intelligence to more quickly remove fraudulent domains and apps from media buys.
digital ad fraud is as rampant as ever; new ripples caused by privacy regulations are starting to affect the market. and more BS from trade associations pretending to be doing something
U.S. smartphone browser share mirrors the mobile operating system market share, with Android users predominantly using Chrome and iOS users using Safari exclusively. On desktop computers, which are mostly Windows-based, Windows users primarily browse with Chrome. The majority of iOS device users have iOS 13 and Safari 13 installed, with only 6% using browsers other than Safari.
from the IAB FY 2019 advertising revenue report, we show that CPM and CPC ads represent 92% of all digital spend; these are the favorite targets of fraudters
FouAnalytics is an alternative to Google Analytics, but with fraud and bot detection baked in. Marketers can use FouAnalytics to look at their own campaigns, find the domains and apps that are eating up their budgets fraudulently, and turn them off, while the campaign is still running. How does that compare to your blackbox fraud detection that just gives you a percent IVT number?
FouAnalytics - site analytics and media analytics for practitioners to detect fraud and take action themselves - on-site tags and in-ad tags measure sites and ad impressions, respectively
Fraud filters were found to increase the rate of fraud when enabled across multiple browser types and ad exchanges, with costs 44-67% higher when using fraud filters. Simple blacklists of sites and apps were more effective at reducing fraud. Exchangewide win rates should typically be between 5-15%, and sites with win rates over 70% could be flagged for potential fraud.
When advertisers spend money on digital ads, only a small portion goes to the original creators of the content. The majority is kept by intermediaries like ad exchanges, traffic brokers, and media agencies. On average, only 15% of total spending on programmatic ads makes it to publishers, with the remaining 85% going to these middlemen in the form of fees and commissions.
The document compares marketing outcomes between a legacy whitelist of publisher sites and a new hand-picked whitelist. It found that the new whitelist achieved a 70% increase in marketing efficiency as measured by conversions per click. Additionally, the new whitelist resulted in both higher eCPM prices for publishers and better marketing results for advertisers in the form of lower cost per start.
More from Dr. Augustine Fou - Independent Ad Fraud Researcher (20)
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
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1. November 2018 / Page 0marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
DIY CTV/OTT
Fraud Prevention
November 2018
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239
2. November 2018 / Page 1marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
If you look at the data
yourself, you can pick off
the CTV/OTT ad fraud.
3. November 2018 / Page 2marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Spelling Errors, Old Versions
Bad guys are greedy and lazy – misspelling, old versions
“Do you, or any people you know, surf a ton of
webpages on their smart TVs? Using the remote
which doesn’t have a keyboard?”
4. November 2018 / Page 3marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Repeatedly loading ads/pages
Humans don’t surf exactly the same number of times
Also note that the
geolocations are
varied so it is not
too obvious.
But the
correlations and
exact same
quantities are not
human-like.
5. November 2018 / Page 4marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Geolocations of CTV Viewers
Which geolocation pattern reflects humans watching CTV
Top is a plot of
the purported
geolocations of
CTV/OTT viewers.
Does that look
normal to you?
(bottom is plot of
visitors to good
publishers’ sites)
6. November 2018 / Page 5marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Each vertical gray line is midnight; most volume 12 – 4a
When do humans watch Roku?
7. November 2018 / Page 6marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
About the Author
Augustine Fou, PhD.
acfou [@] mktsci.com
212. 203 .7239
8. November 2018 / Page 7marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Dr. Augustine Fou – Independent Ad Fraud Researcher
2013
2014
Published slide decks and posts:
http://www.slideshare.net/augustinefou/presentations
https://www.linkedin.com/today/author/augustinefou
2016
2015
2017