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The Biggest Ad Fraud Cases and What We Can Learn From Them

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The Biggest Ad Fraud Cases and What We Can Learn From Them

Ad fraud is showing no signs of slowing down. In fact, the latest data indicates that it will cost businesses a colossal €120 billion by 2023. But even more worrying is that fraudsters’ tactics are becoming so sophisticated that even big-name companies such as Uber, Procter & Gamble, and Verizon have been victims of ad fraud in recent years. 

So what does this mean for the rest of the industry? The answer is simple: every ad company, no matter their size or budget is just as at risk as the big guns – if not more. 

In this article, I summarize some of the biggest and most shocking cases of ad fraud we’ve witnessed over recent years and notably, what vital lessons marketers and advertisers can learn from them to avoid wasting their own budgets. 

The biggest ad fraud cases in recent years 

From fake clicks and click flooding to bad bots and fake ad impressions, fraudsters have and will go to any lengths to siphon critical dollars from your ad budgets.

Let’s take a look at some of the most high-profile and harmful ad fraud cases of recent years that have impacted some of the most well-known brands around the world. 

Methbot: $5 million a day lost through fake video views 

In 2016, Aleksandr Zhukov, the self-proclaimed “King of Fraud”, and his group of fraudsters were discovered to have been making between $3 and $5 million a day by executing fake clicks on video advertisements. 

Oft-cited as the biggest digital ad fraud operation ever uncovered, “Methbot” was a sophisticated botnet scheme that involved defrauding brands by enabling countless bots to watch 300 million video ads per day on over 6000 spoofed websites. 

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Due to the relatively high cost-per-mille (CPM) for video ads, Aleksandr and his group were able to steal millions of dollars a day by targeting high-value marketplaces. Some of the victims of the Methbot fraud ring include The New York Times, The New York Post, Comcast, and Nestle.

In late 2021, Aleksandr Zhukov was sentenced to 10 years in prison and ordered to pay over $3.8 million in restitution. 

Uber: $100 million wasted in ad spend 

In another high-profile case, transportation giant Uber filed a lawsuit against five ad networks in 2019 – Fetch, BidMotion, Taptica, YouAppi, and AdAction Interactive – and won. 

Uber claimed that its ads were not converting, and ultimately discovered that roughly two-thirds of its ad budget ($100 million) wasn’t needed. This was on account of ad retargeting companies that were abusing the system by creating fraudulent traffic. 

The extent of the ad fraud was discovered when the company cut $100 million in ad spend and saw no change in the number of rider app installs. 

In 2020, Uber also won another lawsuit against Phunware Inc. when they discovered that the majority of Uber app installations that the company claimed to have delivered were produced by the act of click flooding. 

Criteo: Claims sues competitor for allegedly running a damaging counterfeit click fraud scheme 

In 2016, Criteo, a retargeting and display advertising network, claimed that competitor Steelhouse (now known as MNTM) ran a click fraud scheme against Criteo in a bid to damage the company’s reputation and to fraudulently take credit for user visits to retailers’ web pages. 

Criteo filed a lawsuit claiming that due to Steelhouse’s alleged actions — the use of bots and other automated methods to generate fake clicks on shoe retailer TOMS’ ads — Criteo ultimately lost TOMS as a client. Criteo has accused Steelhouse of carrying out this type of ad fraud in a bid to prove that Steelhouse provided a more effective service than its own. 

Twitter: Elon Musk claims that the platform hosts a high number of inauthentic accounts 

In one of the biggest and most tangled tech deals in recent history, the Elon Musk and Twitter saga doesn’t end with Twitter taking Musk to court for backing out of an agreement to buy the social media giant for $44 billion.

In yet another twist, Musk has also claimed that Twitter hid the real number of bots and fake accounts on its platform. He has also accused the company of fraud by alleging that these accounts make up around 10% of Twitter’s daily active users who see ads, essentially meaning that 65 million of Twitter’s 229 million daily active users are not seeing them at all. 

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6 Lessons marketers can learn from these high-profile ad fraud cases 

All of these cases demonstrate that ad fraud is a pervasive and ubiquitous practice that has incredibly damaging and long-lasting effects on even the most well-known brands around the world. 

The bottom line is this: Marketers and advertisers can no longer afford to ignore ad fraud if they’re serious about reaching their goals and objectives. Here are some of the most important lessons and takeaways from these high-profile cases. 

  1. No one is safe from ad fraud 

Everyone — from small businesses to large corporations like Uber — is affected by ad fraud. Plus, fraudsters have no qualms over location: no matter where in the world you operate, you are susceptible to the consequences of ad fraud. 

  1. Ad fraud is incredibly hard to detect using manual methods

Fraudsters use a huge variety of sneaky techniques and channels to scam and defraud advertisers, which means ad fraud is incredibly difficult to detect manually. This is especially true if organizations don’t have the right suggestions and individuals dedicated to tracking and monitoring the presence of ad fraud. 

Even worse, when organizations do have teams in place monitoring ad fraud, they are rarely experts, and cannot properly pore through the sheer amount of data that each campaign produces to accurately pinpoint it.

  1. Ad fraud wastes your budget, distorts your data, and prevents you from reaching your goals

Ad fraud drains your budget significantly, which is a huge burden for any company. However, there are also other ways it impacts your ability to deliver results. 

For example, fake clicks and click bots lead to skewed analytics, which means that when you assess advertising channels and campaigns based on the traffic and engagement they receive, you’re actually relying on flawed data to make future strategic decisions. 

Finally – and as a result of stolen budgets and a reliance on flawed data – your ability to reach your goals is highly compromised. 

  1. You’re likely being affected by ad fraud already, even if you don’t know it yet

As seen in many of these cases, massive amounts of damage were caused because the brands weren’t aware that they were being targeted by fraudsters. Plus, due to the lack of awareness surrounding ad fraud in general, it’s highly likely that you’re being affected by ad fraud already. 

  1. You have options to fight the effects of ad fraud  

Luckily, as demonstrated by these cases, there are some options available to counteract the impact and losses caused by ad fraud, such as requesting a refund or even making a case to sue. In such cases, ad fraud detection solutions are extremely useful to uncover ad fraud and gather evidence. 

  1. But the best option is to prevent ad fraud from the get-go

The best ad fraud protection is ad fraud prevention. The only surefire way to stop fraudsters from employing sophisticated fraud schemes and attacking your campaigns is by implementing equally sophisticated solutions. Anti-ad fraud software solutions that use machine learning and artificial intelligence help you keep fraud at bay, enabling you to focus on what matters: optimizing your campaigns and hitting your goals. 


The Biggest Ad Fraud Cases and What We Can Learn

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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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