MMI Insights

MediaSnack Podcast Hosts Method Media Intelligence

Method Media Intelligence’s Shailin Dhar was recently hosted by Tom Denford of ID COMMS on the MediaSnack podcast. On the podcast, he discussed Ad Fraud, how it happens, and what advertisers can do about it. Shailin covered how he first developed an interest in addressing challenges in the digital advertising industry and decided to found Method with Praneet Sharma. Finally, he covered his outlook on the future of digital advertising and improvements that marketers can make to their practices.

Ad Fraud Engineering - Software Engineering Daily Podcast

In this episode, Praneet and Shailin return to the show to discuss how advertising fraud is getting worse–not better. Praneet and Shailin worked with BuzzFeed reporter Craig Silverman, who was a previous guest on the show to talk about his remarkable findings about mobile advertising fraud, which accounts for hundreds of millions of dollars in theft every year.

Data Center Traffic Demonstration

We have released a demonstration of how web traffic can rapidly be generated using AWS EC2. In just 5 minutes and a few mouse clicks, we generated over 1000 concurrent visitors from 290 cities around the world.

In this case, the traffic was sent to a site maintained by Method Media Intelligence with no advertisements. But everyday, data center traffic is directed around the web to consume advertising and causes financial harm to advertisers.

Method Media Intelligence offers Proactive Auditing as a solution to this scenario. Proactive Auditing is a real-time auditing service which prevents ads from being rendered (and paid for) if a site is visited by a data center. Contact info@methodmi to learn more.

Why Ad-Verification Solutions Should Not Sample Impressions

Introduction - How Sampling is Currently Used in Ad Verification

Sampling is used to estimate a characteristic of a larger population. Sampling is an appropriate method when direct measurement of the entire population is overly burdensome or impossible to do within the required time. Sampling is not appropriate in cases where every item in a data set can be measured quickly and cheaply.

Ad-verification vendors sample impressions for two reasons:

  1. The length of the verification process is longer than the auction cycle of selling an ad-impression. In this case, analyzing 100% of impressions would significantly hinder or prevent ad-delivery by causing timeouts* and non-renders.

  2. Verification methods rely on computationally expensive behavioral checks. To reduce costs, vendors measure a subset of supply to provide acceptable pricing to clients.

*In all cases of time-outs, advertisers lose opportunities to reach consumers. If an advertiser is not on ad-server billing, they will be paying for each non-render event.

Types of Users

Internet users can be divided into three categories, human users (48%), “good” bots (23%), and “bad” bots (29%). Publishers create value and generate revenue by selling their online real estate to advertisers, who pay for access to human users’ eyes. Advertisers lose when those ads are displayed to bots, both good and bad.

“Good” bots have numerous legitimate uses. For example, good bots include search engine crawlers that are critical for keeping the internet running smoothly. Good bots primarily operate from data centers, as opposed to human-operable machines.

If websites blocked search engine bots, their content would disappear from search results and the website would lose many human users overnight. If search engine crawlers stopped visiting websites, the search engine would lose its value. If your website shows up in a Google search, it is only because the Googlebot has visited your site. Therefore, it is necessary for these bots to visit websites and the digital media industry must adapt to find a way to prevent ads from being served to them.

“Bad” bots include content scrapers, headless browsers, botnets, and other unwanted visitors to a site. Like good bots, they consume advertising and server resources, but they offer no benefit in return. Bad bot traffic can originate from data centers or human-operable devices. Effective ad-tech partners prevent their clients’ media spend from being consumed by “bad” bots.

In summary, advertisers, agencies and publishers must accept that both good and bad bots will be visiting their websites. Those serving the interests of advertisers must focus on how to measure and prevent the delivery of ads to both good and bad bots. Each ad viewed by a bot is a waste of advertiser funds and publisher resources. Advertisers must not be billed for impressions viewed by good or bad bots.

The Problem: How Advertisers Lose When Using Sampled Data

Imagine the following scenario:

  1. An advertiser’s agency enlists a verification vendor to monitor its digital media spend. The vendor monitors a $1M campaign and samples 10% of impressions. Of those 10%, the advertiser measured 25% bot traffic (fraudulent).

  2. The advertiser extrapolates the data and determines that 25% of their $1M campaign was spent on waste, and asks for a refund of $250k from its DSP.

  3. The DSP refuses to refund the advertiser until it speaks with the SSP’s and Exchanges in its supply chain. The SSP’s states that the verification vendor’s sample cannot be guaranteed to be representative of the entire campaign.

  4. The DSP also tells the advertiser that impressions are not all the same cost. 25% of impressions being fraudulent does not mean 25% of spend was wasted. The 25% waste could be on low CPM impressions, and the 75% on high CPM impressions. Therefore, a 25% refund could be excessive.

  5. The advertiser does not have the data to refute these two points, and accepts the waste as “the cost of doing business”.

The Solution

Method believes the following must be true to protect the interests of advertisers and avoid the above scenario:

  1. Verification vendors only provide actionable analytics when verifying every impression.

  2. Advertisers must have rapid access to full receipts of campaign spend (data for every impression).

As shown by the example above, only complete monitoring can be used to recover funds spent on waste. Analytics on every impression (cost, domain, IP, human/bot) are required to calculate the exact amount wasted advertiser spend. But before advertisers can be refunded for previous waste and prevent waste in future campaigns, they must first obtain this data.

Brands And Agencies Are ‘Resistant’ To Solving Viewability And Ad Fraud, Say Researchers

Ad fraud and invalid traffic are the topics of this piece from Andrew Birmingham of Which 50.

MMI's Shailin Dhar stated that "for all the promises that digital data promotes, there still remains a gap in understanding the systems that generate them. The open, complex systems of the internet seem to be shrouded behind abstractions of what is marketed and what is actually possible."

Praneet Sharma added that “as the funders of this system, it’s ironic that advertisers have so little share of this data ‘currency’. For this system to make good on all its offerings and capabilities, the brands must be given full transparency of each and every penny spent, and gain a full awareness of its limitations". 

Method Media Intelligence has created Proactive Auditing to address current challenges in digital advertising. Proactive Auditing provides full receipts of digital campaigns by tracking every impression. A dashboard provides clients with real time access to campaign metrics to identify wasted spend and enable clawbacks and rapid optimization.

 

 

Lessons on Preventing Ad Fraud and How to Seal the Cracks - The Drum

Read an interview between Shailin Dhar and Danielle Gibson of The Drum. In this interview, Shailin describes how current industry practices allow ad fraud to continue.

In addition, Shailin details MMI's "exoneration method", which starts with the assumption that traffic is invalid and seeks to validate, using a "guilty until proven innocent" approach to detection. In addition, MMI's traffic quality metrics are determined by measuring 100% of impression events as opposed to sampling only a fraction of a whole data set. 

For more, read the full interview.