Ad Tech Real-Time Bidding 101

The modern Ad Tech industry is at the cutting edge of technological progress. It uses the most advanced data analysis methods and content delivery to present advertising content to the user in the most precise and effective manner.

Ad Tech is fascinating because of all the things it does to present the most accurate content to the user. All the algorithms are used to analyze user behavior, match it with the current ad inventory, and deliver the content. A lot is going on behind the scenes of the simple ads delivery operation.

What is Real-Time Bidding?

The real-time bidding approach was developed in the late 2000s out of ad networks dire need to utilize those parts of ad space inventory that were left unused for various reasons, most notably lack of demand from the advertisers.

But instead of simple utilization, Real-time bidding became a long-awaited shot in the arm to the ad industry that revitalized it and brought it to another level. Soon enough, it became apparent that the RTB architecture was advantageous not only in filling the remnant inventory but in managing the entire list without much of a fuss.

At the moment, Real-time bidding is handling around 90% of all programmatic buying in digital advertisements, which comprises a third in overall spending on digital ads.

How Real-time bidding works?

Real-time bidding is one of those things that are best described in practical terms.

Let’s take a standard programmatic advertising situation. You know how it goes — you visit some site, watch some stuff and go on elsewhere, but then there are some ads related to the things you’ve been watching on that site following you elsewhere presenting something that might be interesting to you. That’s retargeting in action. However, this is only a part of the story.

There is an entire sequence of events triggered by the users to visit the page that occurs to present ads to the user.

Here’s what happens.

  • The user visit triggers a request from the publisher to the Supply-Side Platform.
  • SSP analyzes the right and transfers it to the Ad Exchange.
  • AE connects with Demand-side platforms (AKA DSP) on behalf of the publishers.
  • Then DSP opens up an ad call bidding request to the available advertisers who enter the bidding process through the Demand-side platforms.

That’s where Real-time bidding occurs.

The process of real-time bidding can be described as a fully automated sequence of bids that works under a couple of pre-set algorithms with very distinct specifications regarding relevant audience segments, content, price ranges, and other elements.

Basically, Real-time bidding is an auction where advertisers try to outbid one another for the specific ad space. The endgame is usual — those who place the highest bid get the spot. The difference between traditional auction and RTB is that it all happens at a lightning-fast speed. We’re talking about thousands of such auctions occurring in the span of milliseconds. Just think about it — a standard transaction in RTB usually takes about 100 milliseconds to happen.

Needless to say, to maintain such high proficiency in this process, one needs some severe scaling capacities.

Benefits of Real-Time Bidding

The introduction of Real-time bidding into digital advertising became a game-changing moment for the industry. It completely transformed the way advertisements are presented to the user and introduced a completely new business model for advertisers and publishers.

Overall, there are four significant contributions of RTB protocol to the ad tech industry.

The most significant innovation of Real-time bidding to ad tech operation is streamlining the process to its bare essentials. Instead of sweep buying of the bunch of impressions, advertisers can do a much more cost-effective per-impression buying process, which makes the most out of ad budget due to increased flexibility of the process.

On the other hand, RTB turned the laborious and often tangled process of placing ads on relevant spaces into the more automated realm.

The whole meticulous process of sorting ad spaces and checking their relevance and credibility is relegated to the automated platforms that do all the dirty jobs within a blink of an eye. Now — all it takes to make an effective ad tech operation is to set the requirements and adjust them according to the incoming results.

Campaign Performance and the ability to adjust the campaign accordingly are strategic priorities in an ad tech operation. RTB algorithm brings additional flexibility to the mix.

The thing is — Real-Time Bidding allows managing the campaign as if it was a real-time strategy — as it goes. This gives a lot of space to maneuver and analyze ads’ efficiency with specific audience segments and ad spaces. This leads to the constant improvement of the strategy to its most effective state. Real-time factor reduces the waste and impact of wrong decisions to a minimum. This also makes the whole advertising turnaround much faster.

Keeping an ad budget under control is one of the biggest challenges of maintaining an ad tech operation. Real-time bidding allows to automate that peculiar aspect and keep things within reasonable boundaries by setting specific price-requirements.

The other important aspect of budget control is price optimization, which can also be automated in correlation with the campaign performance results via real-time analytics. This allows to maximize the revenue and concentrate an effort on the most lucrative audience segments.

Ad Fraud is one of the biggest problems of the Advertising industry. Each year it eats up a significant part of the ad spending of advertisers. Given the lightning-fast nature of RTB operation — it seems like a tailor-made environment for massive fraudulent activity.

However, with the increased automation of the process and continuously adaptable campaigns, ad fraud’s overall influence is minimized. The fact of the matter is — modern fraud detection system cut off the majority of fraudulent sources long before they get into the mix.

In addition to that, campaign analytics can expose any suspicious activity semblance (for example, anomalous click-through rate) and take them out of charging due to fraudulence.

Challenges of Real-Time Bidding

Scalability is one of the biggest challenges of ad tech. Since the whole operation needs to maintain a high-speed reaction and adjustment to the incoming information — the scaling capacities of a DMP must be nothing less than exquisite, which is a challenge considering how much data goes through and how fast it must be processed.

The solution for the scalability challenges lies in cloud computing. For example, such platforms as Google Cloud, AWS, and Azure offer autoscaling features that take a lot of headache out of an equation.

The prediction mechanism is one of the most critical elements of an ad tech operation. As such, it must be tailor-made to the requirements of your process and deliver the results according.

The center of the prediction mechanism is a combination of supervised machine learning algorithms that includes classification and regression process.

These processes sort out and recognize the incoming data and subsequently calculate possible outcomes and their likeness accordingly. These mechanisms allow predicting the possibilities and opportunities for conversions with particular audience segments on specific ad spaces.

In addition to that, there is an additional algorithm involved for user modeling and subsequent that adapts to the campaign’s results.

Overall Real-time Bidding is a much more controlled operation than other digital advertising types. However, its velocity (i.e., too much too fast) makes it much more ad fraud-prone to shut off any semblance of suspicious activity out of the equation and minimize the influence of ad fraud on an ad campaign.

The challenge comes with identifying and preventing ad fraud activity. The thing is — ad fraud is continually evolving, and each day brings new challenges.

However, there are specific patterns that can be easily identified in case of bot activity. With the little help of particular algorithms, bots can be shut off from the system, and their activity will do nothing more than shake the air between the ones and zeroes.

The process of bidding is a balancing act — you need to know the approximate ranges of your spending and plan the whole thing in relatively long terms. The entire bidding process is organized around the campaign goals — how many clicks or conversions are expected and what can be considered a success.

In addition to that, you need to assess the effectiveness of the spending on a particular type of ad and consider different options for various scenarios.

The critical thing for effective bid optimization is predictive analytics. The stats give you the bigger picture of your campaign in real-time. That will help you adjust bidding algorithms accordingly on the go without experiencing the aftermath of past ineffective decisions.

The other important element of RTB optimization is budget control. The fact of the matter is — there must be boundaries of how much can for certain types of ads and over specific periods.

Here’s how it works:

  • if an ad of a specific type delivers good results in a particular ad space — there are more of such ads placed;
  • if a specific ad fails to deliver in a specific ad space — it ceases, and resources are relocated elsewhere.

Conclusion

This article broke down all the significant challenges that come with the implementation of this process.

Real-time bidding is one of those technologies that require a clear understanding of its possibilities and distinct boundaries to work in.

I hope now you understand the basics of the concept better.

Writer, translator.

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