The “Buffering” Problem in AdTech Costing Advertisers Millions Per Year

The constantly evolving world of Ad Tech is facing a serious problem… one that is often overlooked, or in this case, simply skipped over.

Nice People At Work, a leader in the digital analytics space, surveyed a pool of industry executives, asking for their most pressing challenges when it comes to the world of advertising. 

Nearly 40% responded with avoiding technical streaming issues such as buffering, start failures, and playing crashes. 

In a world where the videos being played directly correlates to revenue being driven, every second matters. 

The current global spend going into the Digital Video Ad Market is more than $105B, and the fact that the rate of buffering and failed starts is as high as it is, is a serious problem for clients and media buyers. 

Ad Stream Compression

There’s currently only one company in the industry that is directly attacking this global problem costing advertisers millions of dollars a year, EuclidIQ. 

How are they doing it? 

By reducing the compression, or size, of the ad streams being deployed on any given ad platform. 

Through this, EuclidIQ has managed to dramatically decrease the rate of buffering and start failure, and in turn, has been able to drive exponentially more impressions, click-through rates, and generated ad revenue. 

By reducing the size of an ad stream, EIQ is able to increase total impressions generated by over 40%, and total completions by over 30%. 

Solving the ad buffering problem reduces start time, reduces abandonment and increases KPI’s that advertisers are investing billions of dollars into reaching. 

You may be wondering… doesn’t reducing the compression of an adstream decrease video quality? 

“EuclidIQ’s patented video compression technology compresses a video based on only what the human eye finds important and automatically selects the best bitrate for video ad delivery. The result is unparalleled video compression for the ad industry” – Richard Wingard, Founder and CEO

EIQ’s encoding process is highly efficient and provides better visual quality compared to other encoders operating at the same bitrate. EIQ’s algorithms model human visual perception allowing them to transcode at the lowest bit rates possible with no noticeable quality artifacts.  

EIQ pairs machine learning with human perception data in order to reach the optimal bitrate selection for video advertisements, which, as the results can back up, makes serious improvements to the overall performance, quality, and effectiveness of digital ads. 

In Market Testing 

From an outsider’s perspective, this may all sound great, but does it really work? 

Does a lower compression of ad streams really result in higher performance metrics across the board? 

To put their algorithm and models to the test, EIQ set out to run an array of in market testing with large budget advertisers, in order to perform A/B tests with their model versus the industry standard compression and loading algorithms. 

The results truly speak for themselves. 

Let’s take a look at a real-world example of a test performed comparing both impressions and watch duration between EIQ, and Amazon Web Services. 

In the diagram above, AWS is listed first as the control model, with EIQ taking the bottom row. Through roughly the same quantity of bids, EIQ were able to serve a nearly 47% improvement in regards to the number of impressions driven.

Where are all these extra impressions coming from if roughly the same amount of bids were won?

“Winning a bid doesn’t guarantee that a consumer will see an ad. The video ad still needs to be delivered and that is dependent on a consumer’s access to high-speed internet. A smaller video size increases the likelihood a video ad is delivered even if a consumer has a weak internet connection or spotty cell phone reception.” – Joel Camacho, COO

The answer lies in the compression. With a much lower percentage of videos buffering and failing to start for consumers, the more are able to be entered into the watch funnel. 

Taking it one step further to analyzing watch time, the story is very much the same. Across the board, there is a 45-48% improvement on average in total watch time in each of the completion models, with a 45% improvement in fourth-quartile completions. 

Conclusion 

EIQ has taken a relatively simple approach to solving the number one problem facing video advertisement buyers. While the actual execution of the solution is anything but simple, it is clear that the results speak for themselves. 

Are you interested in learning more about how EIQ is solving the buffering problem? Are you ready to try it for yourself? Find out more and take a dive into the case studies and detailed explanations here.