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.
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.
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.
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
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.
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.
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.
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
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.
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?
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
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.
putting aside ad fraud for a moment, what's the elephant in the room? we're talking about your digital marketing. Do you think it is working? How do you know?
marketers assume their digital marketing is working as expected; but if they looked more closely at analytics, they may see that some of those assumptions are not valid -- i.e. they are not getting what they thought they paid for. Take a closer look yourself.
Media dollars spent by advertisers may go wasted due to ad fraud and some dollars may go missing simply because it was not spent; but it also wasn't returned to the advertisers or credited to them for future use.
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.
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
Data from FouAnalytics, on-site measurement and in-ad measurement was compared to DBM exchange data for 26 exchanges, 7.5 trillion impressions (30 day period) to analyze browser market share -- specifically Safari/iOS.
Findings include: 1) bots pretending to be Safari/iOS outnumber real Safari users 5 to 1, and 2) there is a 1.5X average surplus of Safari impressions available on exchanges compared to unique cookies.
Digital ad fraud is as rampant as other forms of fraud in other industries. what are some ways to think about prioritizing solving digital ad fraud, relative to other digital marketing activities that advertisers can spend money on?
Digital ad fraud has not only NOT gone down; it has actually gone up in certain cases. There are some improvements when companies make special efforts. Ad blocking is also emerging as a threat.
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.
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?
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
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.
putting aside ad fraud for a moment, what's the elephant in the room? we're talking about your digital marketing. Do you think it is working? How do you know?
marketers assume their digital marketing is working as expected; but if they looked more closely at analytics, they may see that some of those assumptions are not valid -- i.e. they are not getting what they thought they paid for. Take a closer look yourself.
Media dollars spent by advertisers may go wasted due to ad fraud and some dollars may go missing simply because it was not spent; but it also wasn't returned to the advertisers or credited to them for future use.
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.
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
Data from FouAnalytics, on-site measurement and in-ad measurement was compared to DBM exchange data for 26 exchanges, 7.5 trillion impressions (30 day period) to analyze browser market share -- specifically Safari/iOS.
Findings include: 1) bots pretending to be Safari/iOS outnumber real Safari users 5 to 1, and 2) there is a 1.5X average surplus of Safari impressions available on exchanges compared to unique cookies.
Digital ad fraud is as rampant as other forms of fraud in other industries. what are some ways to think about prioritizing solving digital ad fraud, relative to other digital marketing activities that advertisers can spend money on?
Digital ad fraud has not only NOT gone down; it has actually gone up in certain cases. There are some improvements when companies make special efforts. Ad blocking is also emerging as a threat.
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.
Digital ad fraud continues to increase, as more ad dollars shift into digital. This is a recap of current forms of ad fraud and current techniques and technologies being used to combat it.
presentation on ad fraud and ad blocking, and the intersection with bots -- bots dont use ad blocking and their fraudulent activities mess up measurement and ROI
AD Fraud and AD Blockers have been the biggest threat for Digital Advertising industry. The whitepaper discusses 9mediaOnline's initiatives to tackle the threats.
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.
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.
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.
display ads, video ads, mobile vs desktop, clicks and click through rates, time in view, human session duration, viewability and ad blocking, collected in once place, and compared to other studies for context. Not meant to be complete, and NOT meant to be extrapolated to the entire market.
Advertisers have deployed technology and relied on new industry standards to reduce wasted ad spend due to fraud and low viewability. But have those actually worked to drive up RoAS (return on ad spend)? Research data suggests that there are still high amounts of ad fraud that remains to be cleaned up and that the fake traffic, impressions, and clicks further corrupt the analytics that advertisers use to measure the success of their campaigns. Hear practical recommendations from Dr. Augustine Fou, independent cybersecurity and ad fraud researcher, on how to measure and mitigate ad fraud using high tech tools and low tech techniques.
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.
Start with your email: http://eepurl.com/gNoJBL
Programmatic ad fraud is over 20 billion U.S. dollars. Projections show that it will reach 44 billion by 2022. The display channel has no regulation and offers no transparency. Most of the advertisers lack in skills to detect display fraud.
This article was originally published on https://www.ergoseo.com/programmatic-ad-fraud.html
We've come a long way in terms of getting the industry educated about ad fraud. And now there are even case studies of advertisers and publishers taking measurable actions that reduce bots and fraud. There is still a lot of work ahead and the first step is to realize we can't let our guard down now; we must be ever more vigilant and aggressive in protecting the digital ad ecosystem.
“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.”
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
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
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.
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.
When marketers buy from good publishers and pay HIGHER CPMs, they get better outcomes and marketing efficiency, despite the higher CPM. This is because there are human audiences that visit those good publishers' sites.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Adjusting primitives for graph : SHORT REPORT / NOTES
Digital Ad Fraud FAQ Question 1
1. May 2020 / Page 0marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
Digital Ad Fraud
FAQ #1
May 2020
Augustine Fou, PhD.
acfou [at] mktsci.com
212. 203 .7239
2. May 2020 / Page 1marketing.scienceconsulting group, inc.
linkedin.com/in/augustinefou
FAQ #1
“Publishers claim to only have 0.2-1%
IVT on their sites. Can it be possible?”
3. May 2020 / Page 2marketing.scienceconsulting group, inc.
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Answer
• Yes. That is possible for “good publishers” (ones that don’t
source traffic); this is because fraud bots won’t waste time
loading pages on sites that don’t pay them for traffic. Fraud
bots will load pages on sites that pay them for traffic
• Good publishers will still have normal search engine crawlers
and other “honest” bots that declare themselves (bot tells
you it is “moatbot, Googlebot, facebookbot,” etc.
• Be sure to also confirm for humans, because “not invalid” or
“not bots” does not automatically mean “human.” (see dark
blue in the next 3 slides)
4. May 2020 / Page 3marketing.scienceconsulting group, inc.
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Definitions (for charts below)
What each of the labels means
• Humans - 3 or more blue flags to confirm
• Some blue flags but not 3 or more
• Can’t label it either red or blue
• Tag was called, but no data was sent back (blocked)
• Tag was not called (not measurable)
• Bot – Search crawler
• Bot – Says its name honestly, (14,000 bot names)
• Some red flags, but not 3 or more
• Bots - 3 or more red flags to confirm
5. May 2020 / Page 4marketing.scienceconsulting group, inc.
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Good publisher 1
Great consistency in the data; lots of humans (blue), low bots
6. May 2020 / Page 5marketing.scienceconsulting group, inc.
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Good publisher 2
Declared (orange), search (yellow), other bots can be identified
7. May 2020 / Page 6marketing.scienceconsulting group, inc.
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Good publisher 3
Search engine crawlers (yellow) can account for 5-10% of traffic
8. May 2020 / Page 7marketing.scienceconsulting group, inc.
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Answer
• Some good publishers also filter for bots (datacenter,
declared) – this means when the visitor is from a data center,
or a declared bot, the ad calls are NOT made
• When the publisher filters for data center and declared bots,
the resulting bot % can indeed be sub-1% - in the charts
above, they would filter out the yellow (search crawlers) and
orange (declared bots)
9. May 2020 / Page 8marketing.scienceconsulting group, inc.
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Good publisher, filter datacenter/bots
10% red
3% red
“Filter for GIVT and data center; don’t call ads”
27% red
17% red
-7%
-10%
On-Site measurement
In-Ad measurement
Filter applied Stopped buying traffic
10. May 2020 / Page 9marketing.scienceconsulting group, inc.
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Answer
• On-site measurement is most accurate; but fake sites won’t
allow measurement tags to be added to the site. So most
marketers will only have in-ad measurement
• Most fraud sites buy traffic that is well-disguised; that means
standard IVT verification tech is not detecting it as “invalid” so
ad impressions get marked as “valid” even though they are
not
• By analyzing for other forms of fraud (e.g. mobile apps that
load webpages) we can catch a lot more fraud
11. May 2020 / Page 10marketing.scienceconsulting group, inc.
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Places to buy “valid” traffic
12. May 2020 / Page 11marketing.scienceconsulting group, inc.
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IVT only catches bots, misses other
Sites and apps that cheat may look fine in bot detection reports
1.3% + 57% = 58%IVT site/app fraud overall fraud
bot detection sees this
bot detection misses this
13. May 2020 / Page 12marketing.scienceconsulting group, inc.
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Campaign example
Not Measurable 0.0%
No Client-Side Data 6.9%
DESKTOP GIVT/SIVT Humans Other
Disguised
Traffic
Device
Error
App
Fraud
49% 11.3% 0.1% 88.6%
0.2% 0.3% 10.1%
MOBILE GIVT/SIVT Humans Other
44% 1.2% 14.5% 84.3%
DEFINITIONS
• Not measurable – no tags sent (this should be zero, ads are called by JS)
• No Client-Side Data – no data sent back, ad blocker or browser block
• Other – not enough blue or red labels to confirm
• Disguised Traffic – fake traffic, bounced through residential proxies
• Device Error – one or more factors indicating fake device
• App Fraud – apps loading webpages and other non-IVT fraud
Mobile apps using hidden webview browsers to load webpages;
those appear to be mobile devices loading webpages;
NOT detected by IVT verification.
14. May 2020 / Page 13marketing.scienceconsulting group, inc.
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Campaign Examples
Filtered versus not filtered campaigns – basic G-IVT
3% IVT 25 - 40% IVT
well managed NOT well managed
15. May 2020 / Page 14marketing.scienceconsulting group, inc.
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High bot/fraud examples
16. May 2020 / Page 15marketing.scienceconsulting group, inc.
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Answer
• Industry-reported benchmarks are in the 1 – 3% range for this
reason; they are only reporting IVT, and missing other forms
of fraud, which could be many times higher
17. May 2020 / Page 16marketing.scienceconsulting group, inc.
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IAS – 1.9% (desktop display)
Source: IAS Media Quality Report H1 2019
18. May 2020 / Page 17marketing.scienceconsulting group, inc.
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IAS – 0.9% (mobile display)
Source: IAS Media Quality Report H1 2019
19. May 2020 / Page 18marketing.scienceconsulting group, inc.
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IAS – 1.1% (desktop video)
Source: IAS Media Quality Report H1 2019
20. May 2020 / Page 19marketing.scienceconsulting group, inc.
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WhiteOps - 3% IVT
Source: WhiteOps Bot Baseline, May 2019
21. May 2020 / Page 20marketing.scienceconsulting group, inc.
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Answer
• It is important to actually detect what sites the ads actually
loaded on, instead of just assume the domain from the bid
request (fake sites pass legit domains in bid request, in order
to get bids)
• Legit sites that are spoofed get falsely accused of high IVT;
but none of the bots or fake traffic were actually on the real
legit publisher’s site
22. May 2020 / Page 21marketing.scienceconsulting group, inc.
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Good pubs, wrongly accused
Domain spoofing causes legit pubs to get accused of high IVT
Domain (spoofed) % SIVT
esquire.com 77%
travelchannel.com 76%
foodnetwork.com 76%
popularmechanics.com 74%
latimes.com 72%
reuters.com 71%
to get bids
fakesite123.com
esquire.compasses blacklist passes whitelist✅ ✅
declares to be
1. fakesite123.com has to pretend to be
esquire.com to get bids;
2. fraud measurement shows high IVT
b/c it is measuring the fake site with
fake traffic
3. Fake esquire.com gets mixed with
real so average fraud rates appear
high.
4. Real esquire.com gets backlisted; bad
guy moves on to another domain.