You need to set a budget, run a campaign and check back after an hour, only to find out that the platform has changed your bids, altered your audience and moved your money around. You have not done anything.
This seamless job is done by Machine learning in digital advertising , and it is responsible for most of the big advertising platforms.
Sound either magical or hype? A little bit of both indeed. In this guide, we explain everything you need to know about it and that is needed from you. Facts from official pages are marked. The rest is my own view. I’m not tied to any ad platform, and I haven’t tested any.
What machine learning in digital advertising means
This involves a machine program being able to analyze historical information to make predictions. In advertising, the predictions are normally things like “What is the probability of the individual clicking?”
Traditionally, this would be based on guessing. You choose the target group, set your bid and tweak after a few days. Software can now check thousands of small signals for every ad auction.
Bidding is where you’ll see it first.
The most obvious case of machine learning application in digital marketing is automatic bidding. According to Google’s help page on Smart Bidding, these are a variety of strategies that “use Google AI to optimize for conversions or conversion value in every auction.” It says machine learning algorithms “train on data at a vast scale” to predict how different bids might affect results and that they weigh more factors than “a single person or team could compute.”
What factors?In this page, they refer to signals as information regarding an individual and his/her condition when the auction is taking place. These signals may be things such as device, location, day of the week, time of the day, remarketing list membership, and for the search, the search query.This system doesn’t read minds; it makes use of patterns from the context of each particular auction.
It needs data, and a lot of it
Many small advertisers get stuck here. The same Google page says, “To use Google Ads Smart Bidding, you need to have conversion tracking enabled.” “A conversion is whatever you count as success, like a purchase or a sign-up. No tracking, no learning.
The page also notes that some strategies need minimum history. Google advises running tests over longer periods, having at least 30 or even 50 conversions, depending on the strategy. This is advice for Google services specifically, but not some general rule to apply on all platforms. Nevertheless, the lack of data means the model will have nothing to learn from, which means that in case of several purchases per month, automation will act weirdly. The webpage further mentions that new campaigns can use data from existing campaigns, while results need to be evaluated over time.
Where else does machine learning in digital advertising show up?
Bidding is one spot. These common uses come from general industry knowledge, not one source.
Audience targeting. Platforms can find people who resemble your past customers, so you don’t have to guess who to reach.
Bidding is another example. All of these examples are from my experience in the advertising industry, not a single source.
Audience Targeting. Advertising platforms are able to target users similar to your previous clients, so you won’t need to speculate about who to target.
Creative Testing. There are tools which combine headlines with images and increase budget for the most successful combinations.
Budget Allocation. Advertising platforms may automatically reallocate budget between campaigns or placements based on the current results.
Forecasting & Reporting. Platforms may be able to predict your results or notify about any unusual changes in metrics.
Detection of suspicious traffic. Some platforms detect suspicious behavior, such as fake clicks.
Advertising platforms are different, so you should consult help pages of each one individually.
What it can’t do for you
Machine learning in digital advertising will chase a goal, but it can’t pick one. Tell it to find cheap sign-ups, and it will happily do that, even if none become customers. Bad goals give bad results, only faster.
It also can’t fix a weak offer or a confusing landing page. And it only learns from what you track, so broken tracking means it will confidently optimize the wrong thing. Someone has to set the goal, check the numbers, and ask whether the results make business sense. That’s my own view and the most important point here.
Privacy and rules still apply.
Using machine learning in digital advertising doesn’t remove your responsibility. Google’s page says advertisers are responsible for complying with legal requirements, its ad policies, and its personalized advertising policies. It also advises understanding the signals used before deciding whether smart bidding suits your business.Data and ad rules vary by state and country and keep changing. I haven’t researched them here, so check with a qualified professional if you handle sensitive data.
Watch out for big AI claims.
Plenty of vendors say their tool is “AI-powered,” and some of it is just marketing. A 2023 Federal Trade Commission business blog, summarized by the law firm Kelley Drye in an article dated March 9, 2023, warned that advertisers may overuse and misuse terms like “AI.” The summary says the FTC asked whether companies are exaggerating what their AI product can do, whether they can prove claims that it beats a non-AI product, and whether the product uses AI at all. You can read that summary of the FTC’s guidance for the details.
That advice is aimed at sellers, but buyers can borrow it. Ask vendors for proof and ask what the tool can’t do. Our post on how to create brand awareness online is a good companion if you’re still working out your goals.
A simple way to start
You don’t need to hand over everything at once. This path is my own, not an official one. Set up conversion tracking and make sure it counts correctly. Pick one clear goal, like leads or sales. Run one automated strategy on one campaign, and leave it alone long enough to judge fairly. Then compare it with what you did before, over the same time window.
Common mistakes
The biggest mistake is turning on automation before tracking works. Others are changing settings constantly, judging results too soon, and trusting vendor claims without proof.
Your next step
Open your ad account and look at your conversion tracking. Is it on? Is it counting the right thing? If yes, pick one campaign to test automated bidding on. If no, fix that first.
For more on tools like these, browse our AI Tools section. Machine learning in digital advertising can save you time and sharpen your results, but it works best when you give it good data, a clear goal, and a person who’s paying attention.
FAQ
How does machine learning in digital advertising work?
It is software which uses previous information on ads in order to make predictions on future outcomes and makes changes to bids or targeting accordingly.
Do I need conversion tracking?
For Google’s Smart Bidding, yes. Its help page says conversion tracking must be enabled.
How much data does it need?
It varies by platform. Google’s guidance is at least 30 conversions for some strategies and 50 for one of them.
Can machine learning replace a marketer?
Not really. Machine learning is great at crunching numbers, spotting patterns, and automating repetitive tasks, but it can’t decide what a brand stands for or which goals matter most.
