Google Display Network (GDN) is one of the biggest online advertising platforms. If you have even dipped your toes into ads, you should have at least heard of it. It is a huge system of millions of websites, apps, and Google-owned properties like YouTube, Gmail, and others. The scale of all of it is huge, and with such scale comes great potential.
There are hundreds of thousands, if not millions, of advertisers competing on GDN every day. Just running ads doesn't cut it anymore, as you need to have something that pushes you and makes your content more engaging than others. For years, the answer was manual labor. Selecting placements, layering demographic data, picking interests, and hoping you land the right targeting. Luckily, with the advances of AI, most of these things can be automated, and due to the automation, they even work better with less effort. But you need to know how to use machine learning and artificial intelligence to target smarter and scale.
In this article, we will try to explain the Google Display ads platform a bit better, and show you how you can use ML (Machine Learning) and smart algorithms to target better and scale harder!
Understanding The Basic Idea
In the last 2-3 years, Google has gone all in on the whole AI craze. They started relatively weak with their initial models like Bard, but slowly and surely, they used their massive resources and knowledge to develop some of the best models out there.
The best one that's available to the public is their Gemini model. By most metrics, it's one of the best, if not the best, AI models out there. And it's only improving more and more every day.
Their advancement in these AI models also reflects their products, as Google slowly introduced advanced functionality into all of their tools and apps. The most recent one is their Display Network.
The goal behind this implementation is simple. Google Search ads are there to offer users ads that they are interested in and that would engage them. These ads should show up just at the time that someone needs whatever the product or service that's being advertised can fulfill. So, imagine you are browsing an article about gardening and you find out that some cool solar lights can make your entire garden look better, and they require no additional wiring, planning, or renovation to work. This would be the perfect time to get an ad for solar lights or garden decorations.
This is where Machine learning (ML) comes in. Google should be able to analyze the content you are engaged in and offer you ads that would fit your current mood or needs. It should be able to make real-time decisions about:
- Who is the right person to see this ad?
- When is the best moment to show it to them?
- Where (on which site or app) will it have the most impact?
- How much should I bid to get their attention?
All of this used to be a full-time job for an online marketer deciding on the best course of action. And there are only so many things a person can analyze and do at a certain time to correct and change options on the fly. But for an AI, this is not a complex task at all. You can see how powerful this can be in the right hands.
How Did Google’s AI Get So Smart?
To be honest, there is nothing magic about it. Google has incredible resources and data, and those two combined can allow them to make whatever they want.
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User Behavior and History
Google has had its tracking solutions following users around for over a decade. In all that time, they have developed ways to recognize user behavior and track their history. This includes long-term activity across apps and websites, as well as search history, watched videos, content you interact with, and much more.
All of this on its own doesn't make a huge difference, but together, it can build a report card on any user that shows exactly what they are interested in and how to best approach them. With ML models, this data can be even more useful.
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Context
The algorithms are smart. They don’t only analyze who saw the ad, they also determine where you saw it and how you responded to it. The ML models also analyze the context and content of the page you saw the ad.
It searches for keywords, topics, language, links, structure, and placement to determine exactly what is relevant ot you and what is not.
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Your Data
The best kept secret of Google’s systems is that it doesn't only focus on its data, it uses yours as well! With every conversion and interaction on your page, you unwillingly tell Google exactly what it needs to know. The better you connect your content and ads with Google's tracking solutions, the more data it can extract from them.
Google can indirectly track things like:
- Remarketing Lists: Determines the audiences of people who have already visited your website and idnt convert. These are the prime subjects to remarket and target again.
- Customer Match: Finding similar users to the ones you are currently targeting and reaching successfully.
- Conversion Data: history of people that completed your conversion goals (purchases, lead forms, etc).
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Lookalike Audience
Once the machine algorithm understands what your converters look like, it can go out and search for similar people that might be interested in what you have to offer. This is the base of features like “Optimized Targeting” and others.
Using Machine Learning Properly
Knowing how it works is great, but using it is much better!
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Smart Bidding
Manual bids are a thing of the past. Smart Bidding lets you optimize your bids for specific outcomes, and after you select some initial parameters, the ML algorithm does the rest. With the right setup, it can even adjust bids for each individual auction based on the likelihood of conversion. The key strategies include:
- Maximize conversions within your budget.
- Target Cost per Action is based on a specific goal you set.
- Target ROAS, aim for a specific return on every dollar you spend on advertising.
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Optimized Targeting
This is now the default setting for Display campaigns, but you shouldn't forget about it. It provides a targeting signal that you want to optimize for, and then the algorithm makes all of the decisions itself. With this, the system also sometimes goes slightly outside its bounds if it thinks it has found the ideal customer base for your audience.
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Responsive Display Ads (RDAs)
Gemini and similar AI have slowly taken over image generation, and for the old-school affiliates, this might be problematic. Nowadays, you can pump out good-quality static ad images through the AI workflow of most companies. With the right setup, the model can create assets of varying images, headlines, descriptions, logos, and landing offers.
Why is all of this important to Affiliate Marketing?
The biggest thing is that it makes everything easier, quicker, and often better than doing everything manually.
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Efficient Scaling
Scaling is hard as it is, but without help, it's difficult to analyze so many data points for every single ad auction and then optimize for each one of them without losing too much money.
Let Google do the heavy lifting. Try organizing stuff to easily launch new content and ads without being stuck in the same place over and over again. The AI will do a better job of adjusting and improving your scaling on the fly.
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Uncovered Hidden Audiences
We all think that we know our ideal audience, but in most cases, we can be off by a big margin. Sometimes the audience that responds best to certain things isn't the one we expect, and this is where machine learning can make a difference. Smart bidding and optimized targeting can find niche audiences of high-converting users that you would have never thought to target manually.
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Improved ROI
We all know that ROI and revenue are the metrics that mean the most. With the help of AI, you can focus your budget on audiences that are engaged and convert, and see much better results overall! Waste less money and make each dollar count!
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Time Savings
Time is money, and with automation, you can save a lot of time. With everything becoming more automated, you need to do less for the same or better results. You can test creatives, split test targeting, and adjust bidding fully automatically. This also means you can focus on things that humans are still a lot better at, like strategy, market research, and making better creatives and offers.
Conclusion
Google’s machine learning has transformed Display advertising from a manual-only thing to an almost fully automated tool that you can use. No longer do you have to analyze every single detail on your own and make changes all of the time to keep results improving. You can set goals and let the supercomputer on Google’s servers do the work for you.
Just remember, don’t fully give away every part of your control. Some things are still not perfect with AI and ML, and it can make some mistakes. Combining human and tech is the best way to go, and it's a surefire way of improving results!
The automation is quite good as is, and it's only going to get better with more time, training, resources, and development poured into the algorithms. If you still haven't started using it, it's about time, as it's already improving campaigns and getting better results for users!
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