Best solutions for biggest Ad Tech challenges

Today information is the most valuable thing in the world. Everybody wants some for their reasons.

When other things roll in and out of fashion and demand — the only thing of value that indeed remains constant is information.

• That is what makes the wheel turning; that is what makes it all tick-tick-tick and Sha-la-la.

And there is no place where it is more apparent than in Ad Tech. But one can’t just take it and sit on it and thus benefit from its supreme radiance just because reasons are firmly in place.

The information on its own is worth little to nothing. It is always there, lying around. It is what you make of it that matters.

• The use of information is the real deal. It is what causes money to happen.

But it is not that simple.

There are more than a few bumps in the road make rather serious challenges that need to be solved.

While it is a natural part of the process — there is a catch. In Ad Tech, you can’t just study past practices and use them for your own good — it doesn’t work that way.

Because of new technologies and ever-increasing competition — you need to develop new solutions tailored to a particular situation.

Value of information in Ad Tech

In the realm of ones and zeroes — everything is data one way or another. It is a rather obvious thing to claim. And yet, not every piece of data is created equal. Some ones and zeroes are more valuable than the others. It is essential to know exactly which ones.

The primary source of information in Ad Tech is a consumer. His contribution is technically straightforward — it consists of merely hanging around on a particular site. However, it involves many types of machinery behind the scenes that gather incoming information in one place, just like the bees collect nectar.

There are two common ways of getting data:

• The third-party — when you buy it from somebody else;
• The first party — when you gather it on your own;

The third party’s case is more straightforward — you pay money and get the information. Then you need to integrate them. The first part involves the development of your systems. And that is where things get complicated. There is more value to the latter because of its uniqueness.

The main challenge of collecting data on consumer activity is the right choice of instrument sets. Believe it or not — that is an actual problem. The majority of failings of the ad campaigns are because of that. The right way of reaping data need to be thought through. In Ad Tech value of information depends on the course said information is further collected and used. It is only as good as it is segmentation.

Not only do you need to define administering categories of information beforehand, but you also need to have an approximate understanding of which tools can collect and utilize it sufficiently. In terms of Safran Foer’s book titles, data segmentation looks like this: it goes from “everything is illuminated” to “extremely loud and incredibly close.”

The data segmentation process is handled by the Data Management Platform (DMP). It organizes, sorts, translates, and subsequently shares analyzed data. It is equivalent to bees turning nectar into honey. The challenge is in building a practical algorithm for breaking down information into data points.

Usual parameters are evergreen:

• Age / Gender / Location / Language;
• Exposed interest or browsing history;
• Gear/ applications / social networks;

But there are also some parameters specific to a particular campaign that need to be thought through.

If not — data is insufficient; thus, it is analyzed backward; therefore, you get bizarro-world stats that will bring your ad campaign increasingly diminishing returns with zero benefits. And you will get the “Marcel Marceao Greatest Hits” record as a consolation prize.

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Another thing to reckon with is a manageable and understandable interface. It is essential to see precisely what is going on. The stats need to be responsive and continuously updated.

If the interface is muddled, overly detailed, or lacking elements, it seriously affects the efficiency of understanding the campaign’s state.

The solution lies in the right balance between informative and convenient. It should be immediately apprehensible and intuitive in its use. In other words — that means “X” to close is in the upper right corner and not otherwise.

Challenges of data gathering

Ad Tech revolves around the concept of user targeting. It is an assorted collection of ways to deliver ads to the consumer based on the information gathered from his activity. It forms the foundation of an ad campaign. Subsequent reaction of the consumer to the campaign leads to further developments. Not only that, but it also helps to calculate the best areas for placing ad content. It all depends on correctly gathered data.

However, the journey of information from the user to the advertiser goes through the dark forest of err and eh with a black beast of arrrggghhh and basher cony of Caerbannog lurking in the shadows. It is a hard way of saying — the algorithm must be worked out and thought through in every possible way before applied and refined in the process.

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Another problem with gathering data is privacy. There are limits, and they are continually narrowing. In 2016 the European Union adopted the General Data Protection Regulation (GDPR) act that severely limits and regulates the scope of things you can do with collected personal data. Among other things, it severely limits the personalization of ad content. The solution might be in a contextual approach, but that is not a panacea. One way or another, most current advertising practices need to be reworked to avoid violating GDPR. That is a thing that needs to be thought about.

Trials of data storage

Maintaining information is a challenging thing, to say the least. When it comes to taking care of substantial amounts of data that comes in the current never-ending stream — the whole thing turns up to eleven.

The reality of a situation is that every user leaves thousands upon thousands of events. Combined, these are millions upon millions of various events every day. It is an enormous, ever-growing amount of information beyond human comprehension. And it needs some serious power to be handled properly. Not only that, but it also needs to be stable and resistant to overloads.

If not — data loss or corruption might occur. And that is something you don’t want to happen.

Maintaining your server framework seems logical. The downside is that it is incredibly cost-ineffective. Not only do you pay for used space, but you are covering the entire maintenance operation. Count in the fact that the amount of used space is increasing exponentially. In other words — these funds can be used elsewhere.

The real viable solution is maintaining the data in cloud storage. It cuts corners and allows you to pay only for the space that you are using. It also enables not only to keep data safe but also eliminates the problem of lacking space. It also pulls double duty of backup storage.

Cloud storage is a cost-effective solution. But it also doesn’t come cheap. Hosting an ever-growing amount of information takes a lot of resources. And overspending is not the thing one can afford systematically. Making data storage cost-effective is one of the biggest challenges in Ad Tech.

This problem can be resolved by multifaceted managing of data storing. Information needs to be divided according to its value and relevance. It can go as far as using the services of different providers for storing various kinds of data.

For example, you have data collected from a specific period. It is being processed and put into action. That data is hosted on primary storage. Then there is already used data that might come in handy — it is being kept on secondary storage. Then there is data that passed the point of relevance and is no longer valuable for the company. This one can be deleted.

It is essential to define balanced time windows for every stage. They can’t be too short or too long.

There also might be issues with transferring of information. Since the amounts of data and the load of the systems are enormous, they need to be safe from lags and glitches. The transferring process needs to include a few transit points when the data can be checked and compared before fully transmitted.

Ad Tech Security

Data protection is another extremely important thing. Security is probably the biggest challenge the ad tech industry ever faced. Nowadays — you can’t be sure about anything. One can always find a breach and turn it into a crack where Death Star can swing in and out.

One of the advantages of cloud platforms (AWS, Google, Azure) is that it provides a decent amount of security measures. Regarding infrastructure, cloud service can provide controlled network access and firewalls combined with encryption. It also helps to deal with DDoS attacks by reducing the impact and providing resilient deflection.

While there is a slight variation of options, it all boils down to three primary methods:

• Encryption makes data eerily beyond comprehension (although some folks like it that way) from the outside. To access it — the user needs to have the key or specifically assigned platform;
• TLS / HTTPS protocols — for safe connections;
• Identity management includes authentication and authorization, i.e., login username and password, strict limitations of IPs, and customizable options. It is regulated by IAM (identity and access management) policies.

Challenges of monetizing the revenue

Profit is the end goal of any ad campaign — no point in arguing about that. However, many factors can turn to monetize the revenue tangled and ultimately ineffective and even damaging to the company.

The major challenge for monetization lies in the choice of the right revenue models. There is no formula that works in any case. It takes a lot of research to define which models are useful in which case and which sequence will maximize the revenue. None of these models are helpful on their own. There must be a balance. Hopefully, it all can be easily calculated.

The most common and effective models are:

• Cost per impression — often tied with cost per mile (CPM). Preferred by ad publishers more than advertisers. Usually combined with cost per click;
• Cost per click (CPC) — splits the risk between the publisher and the advertiser. Best used for contextual-based content;
• Click-through rate (CTR) — revenue based on the number of clicks divided by the total number of impressions served throughout the campaign;
• Cost per action (CPA) — when user more directly interacts with ad content, i.e., cause conversion;
• Can be furthered into cost per install (CPI), which directly deals with consuming the product;
• Cost per lead (CPL) — when ad content brings contacts with consumers;

The more significant challenge is to project the sizes of the cuts involved parties will take in a particular scenario. This aspect allows us to adapt the campaign and minimize possible losses quickly. Usually, the revenue is split between three parties:

• Advertisers — the ones with the adverts.
• Publishers — the ones with spaces for adverts, i.e., ad inventory.
• Ad Platforms / Ad Agencies / Ad Exchanges — who serve as a mediator between advertisers and publishers.

The big challenge is integration with payment systems (such as PayPal or Payoneer). It also needs to be secure, transparent and done according to the laws. Another possible issue happens upon dealing with multiple currencies. In this case — there is a need for implementing another plugin that will perform conversion according to updated exchange rates continuously.

The biggest challenge that might occur in splitting the revenue is the correctness of the assessment. To eliminate the possibility of misunderstanding between parties, there must be transparency.

One of the solutions is to use the tried and tested system to calculate that. But one can’t be sure from one source, so it is recommended to use one more tool to double-check the information.

Another big thing is how to deal with fraud. It is extremely easy to get fooled in the ad biz.

Among the most common forms of fraud are simulated activity — when your ad content is being interacted with, but it is manipulated to maximize the parties’ revenue.

• It is done either by bots or by clickers and websurfers. The purpose is as usual — to rip off the unwitting client. Such activity is utterly useless for the company as it brings zero leads and feedback from the customers. It is dead weight.

One of the ways of dealing with it is by analyzing the user activity on the advertised product. It takes time, but it is a proven method of exposing the rot.

• This process can be automated or manually performed, depending on the case. If there are suspicious patterns or just a lack of activity — the advertiser can significantly reduce the responsible party’s cut due to a lack of positive outcomes.

In conclusion

These are all of the significant issues that might occur upon implementing Ad Tech systems to your operation.

It is not like Ad Tech brought law and order to the increasingly erratic Advertising Industry — I guess it would fair to say that at the very least, the rise put the industry upside down.

• Despite that, in retrospect — it made things much more comfortable or at least easier to follow.

Ad Tech opened up many new opportunities and even more challenges that allow companies to stay relevant, evolve, and improve their business strategies in a way unimaginable even a couple of years before.

• Nonetheless, Ad Tech is often a puzzling thing to write about. It is not that it is complicated. It is just too tangled to get through with gently.

Even the name of the game is a bit confusing. It is a vaguely defined umbrella term that covers a whole lot of stuff.

• While it merely involves broad subjects of administering, delivery, and analysis of information for advertising purposes and, at the same time, sounds like cleanly shaved abracadabra.

It is still better than the German way of doing terms — that would be all that and more mashed together into an elaborate pwoermd. Try not to think about it.