What is machine learning in marketing?
Machine learning is software that improves by example. Instead of a person writing rules for who should see an ad, the system studies millions of past outcomes (who clicked, who bought, who ignored) and learns the patterns that predict each one. In marketing that learning runs constantly in the background: in the ad platforms deciding which impressions to bid on, in the online store deciding which products to recommend, in the email tool deciding when to send.
Most marketers use machine learning without building any of it. The biggest ad platforms, Google's and Meta's in particular, are machine learning products, and their automated campaign types ask the marketer for goals, budgets and creative, then make most of the targeting and bidding decisions themselves.
Why it matters
Why machine learning in marketing matters
It has changed what the marketer controls. A decade ago, targeting was a craft of audiences and keywords. Now the algorithm finds the audience, and the main levers left are the signal you feed it (clean conversion data, a clear goal) and the creative you give it. Creative has become a form of targeting: the people who respond to an ad teach the system whom to look for next.
That raises the value of variety. A system that learns from responses needs genuinely different ideas to test, so a steady supply of distinct creative now matters as much as the media budget.
In practice
How machine learning in marketing works in practice
- 01
The setup work is mostly data. Conversion tracking has to be accurate, events have to mean what they say, and the goal has to be one the business really cares about, because the system will optimize hard for whatever it is told, including the wrong thing. Then the platform gets a budget and a learning period to find its footing.
- 02
After that, the work is creative. Teams produce a range of concepts, formats and hooks, watch which ones the system favors, retire the ones that fatigue and feed in new ones. The best results come from treating the algorithm as a fast, literal-minded media buyer: give it good inputs and clear goals, and keep a person deciding what the brand stands for.
At RIOT
Machine learning at RIOT
From February to April 2025 we ran a Meta ad engine for Homebase, producing a steady flow of ad creative for Meta's delivery system, whose machine learning decides which ad each person sees. Our side of that equation is the part the algorithm cannot do: the ideas, the range between them and keeping every variation on brand.
Watch out
What people get wrong about machine learning in marketing
Expecting it to fix weak creative. The algorithm can find the best audience for an ad, but it cannot make a dull ad interesting; it will simply spend money learning that nobody cares. The other mistake we see is changing settings constantly. Major edits can send the system back into learning, and teams that tinker daily never let it learn at all.
