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Can Machine Learning Solve Our Viewability Challenges?

No advertiser wants to waste their media spend on ads that nobody is seeing, or ads that only bots are seeing. Campaign success is dependent on advertiser media spend being allocated towards highly viewable quality inventory — precisely why viewability is one of the features demanded most by agencies and advertisers.

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This demand created a cottage industry of viewability companies like IAS, Moat, and DoubleVerify that exist in order to help marketers measure the effectiveness of their digital ads. But the demand for viewability also created a double-edged sword. Given the limited availability of quality inventory, as more advertisers want viewable impressions, competition increases and price move up.

Increasing the scale of this pool is one of the biggest challenges facing advertisers today. So how do we solve for these issues?

Applying Machine Learning to Viewability

Advertisers looking to expand a particular type of inventory can apply machine learning to the truth sets provided by viewability partners.

By using machine learning to identify near identical types of inventory, advertisers can determine how many more bid requests qualify as viewable, after winning the auction.

At the end of the day, the determination for viewable inventory is based on probability. Traders may find it efficient to check a box for pre-determined viewable inventory, but if an advertiser is looking to scale their campaign, using machine learning in conjunction with a viewability partner’s truth set can dramatically increase how many consumers a brand engages with.

The next question is:

What about fraud? Bots can generate viewable inventory too. How do you solve for that?

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Fraud-Free Impressions

We know bot-generated traffic exists. And with third-party verification, advertisers can understand whether an impression is fraudulent or not. But when an advertiser looks to increase scale, machine learning can be applied to predict whether or not an impression could turn out to be fraudulent. By disregarding impressions opportunities that a machine learning considers fraudulent with 90% accuracy, an advertiser can dramatically increase the amount of quality inventory available while mitigating the risk of wasting spend.

The combination of machine learning technology to observe patterns along with viewability technology to provide a targeting solution can dramatically increase the scale of high-value impressions.

Increasing ROI (Return on Investment) through Multi-Touch Attribution

One of the biggest challenges for an Advertiser is attribution and allocating spend to the most valuable channel that delivers the highest return on ad spend. With all the different channels and formats available, from search, email, TV, mobile and desktop, video and display, understanding what channel drives the highest return, and which is really the best for a specific advertiser, is complicated. Historically, unsophisticated models like last click attribution dominated because of their ease of use, but they are far from accurate.

Now, nearly 60% of marketers are expected to engage in cross-channel measurement and attribution, according to a recent poll by the Interactive Advertising Bureau and the Winterberry Group.

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Through machine learning, marketers can shift their focus from media to measurement and gain a better understanding of ad performance and the cost to reach their target audience. For example, if a marketer were to line up every touchpoint and event from search to email to display and others, machine learning algorithms are able to figure out which events drive the most conversions. From there, marketers can hyper-refine their strategies and better optimize campaigns.

In a holistic system, when people spend money on hundreds of channels, this information can be fed back into a campaign at any point. For marketers who have massive campaigns and want to know if their ad spend should be allocated to email or mobile video, machine learning can assist in those key decisions.

When advertisers ask how machine learning can be applied to digital advertising campaigns to improve their effectiveness, viewability is one of the biggest practical applications. When used strategically, it dramatically increases the scale of a campaign and helps you know when, and on what channels, your campaign will be most successful.

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