IX Perspectives

An Ad Tech Evolution for Monetization

Adaptation is the key to evolution.

Nowhere is rapid evolution more evident than with technology. Let’s take cars for an example. When cars were first invented, they replaced the horse drawn carriage. Before long, they were breaking distance and time barriers for personal travel that no one had imagined was possible. But soon, others saw how groundbreaking this technology was and began to create their own versions of a car. Quickly, the market became saturated with cars, each very similar to the others. What was once a revolution became a commodity. But those who persevered in the industry — the innovators — still saw that there was more evolution to be had. And today, we’ve entered a new era of innovation for cars with electric, hybrids, hydrogen, and even self-driving.

At Index, we also feel that we are at the cusp of a similar evolution in Ad Tech. We’ve already discussed how header bidding was revolutionary and how the market has quickly become saturated with many header bidding solutions. Now, we seek to usher in a new era of Ad Tech with new Adaptive Machine Learning capabilities such as Adaptive Timeout.

What does this mean for publishers and how can these new Adaptive Machine Learning capabilities drive increased revenue?

The Barrier to Evolution

Previously, publishers who have adopted a header bidding solution could only set a single fixed timeout value. These timeout values could not be optimized for factors such as the individual end user’s network latency or device. This resulted in a large amount of the publisher’s Bidding Adapter’s bids timing out. In essence, a publisher wasn’t seeing a large number of potential bids just because they had a timeout value that wasn’t optimized. This resulted in the publisher not sending as many bids into their ad server, and therefore, losing out on potential revenue from header bidding. This also meant that many of the buyers’ bids were not being considered by the publisher.

All this boils down to one key problem: significant missed revenue for publishers.

By bringing Adaptive Machine Learning capabilities to header bidding, Index has moved publishers beyond the single fixed timeout value. Now, with an adaptive optimized timeout value, publishers have revenue opportunities that previously were not being realized.

In particular, Adaptive Timeout:

Reduces Timeouts for all Bidding Adapters:

  • Adaptive Timeout optimizes wrapper timeout values for each user – page view by page view – based on changing factors such as the user’s device type, geo, current network latency and browsing behavior. In other words, Adaptive Timeout is looking at all factors that affect the user’s experience and adapting the header bidding timeout value to optimize for those current conditions.

Increases Bid Participation for all Bidding Adapters:

  • With the adaptive and optimized timeout value, a publisher reduces the number of Bidding Adapter timeouts. This means that Adaptive Timeout increases the number of bids from header bidding which make it into the publisher’s ad server. In the past, there could have been a higher bid value that didn’t make it into consideration. Now, a publisher can now fully evaluate all bids from advertisers.

Increases Revenue for the Publisher:

  • By increasing the density of bids through reduced timeouts, Adaptive Timeout increases the amount of revenue a Publisher can collect from indirect advertising. This is not only through more winning bids from Bidding Adapters who were timing out, but also through more price pressure on all players in the ecosystem, including Walled Garden demand sources.

As we continue our series on adaptive machine learning, we’ll discuss how we’re bringing this technology to other areas of Ad Tech. In the meantime, to learn more about Adaptive Timeout or other innovations, please visit our Knowledge Base.

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