It wasn’t too long ago that artificial intelligence was the reserve of science fiction.
Fast-forward to 2025 and nearly 90% of U.S. marketers are already using generative AI tools at work – with 19% doing so on a daily basis. If you have a browser tab permanently tuned to ChatGPT, that might not surprise you – but the money behind it could.
According to eMarketer, AI-powered search ad spend expected to rocket from just over $1 billion this year to almost $26 billion by 2029.
That spike points to a world where AI powers every aspect of digital advertising from better targeting to scroll-stopping creative. In other words, embracing AI is no longer a nice-to-have – and it could even be your competitive moat.
Let’s take a step back
Everyone and their dog knows that AI is already making a huge impact on almost every industry, but how many of us really understand the fundamentals?
The two core technologies that paved the way to generative AI are machine learning and deep learning. You’ve probably heard those terms many times, but if you’ve ever wondered what they really mean, we’ve got you covered:
- Machine learning refers to algorithms that learn from historical, structured data – like tidy rows of customer attributes, clicks, and prices – to predict outcomes or automate decisions by spotting patterns on their own.
- Deep learning is a specialized branch of machine learning that thrives on unstructured data like images, audio, and free‑form text, stacking many neural‑network layers to untangle complex relationships and give computers a more human‑like sense of perception.
Still with us? Excellent. Now, let’s look at how each of these technologies work in the context of digital advertising.
What is machine learning in digital advertising?
Machine learning applies statistical algorithms to historical campaign data to predict which ad decisions will deliver the best results – no hand‑coded rules or guesswork required.
Once it’s been trained on impressions, clicks, and conversions, a machine learning model keeps learning in (almost) real time, adjusting bids, budgets, and audiences as more fresh data pours in.
So how does machine learning help marketers today?
- Predictive bidding that recalculates a user’s likelihood to convert and adjusts CPMs on a per-impression basis.
- Audience clustering that reshapes segments in real time as shoppers browse, buy, or churn.
- Budget reallocation that forecasts ROAS and reroutes spend before ads plateau.
- Propensity scoring that flags high‑LTV prospects for loyalty nudges and upsells.
So, machine learning in marketing is all about scalable automation, but it needs clean, layered, structured data – and can quickly come unstuck on messy inputs like raw images or video.
But there’s a solution for that, too.
What is deep learning in digital advertising?
Deep learning stacks multiple neural‑network layers to interpret complex signals like images, text, and behavior, enabling more nuanced predictions and richer personalization.
Its multi‑layer architecture digests pixels, copy, and behavioral breadcrumbs all at once, spotting relationships which are too subtle for traditional machine learning.
Why do today’s marketers reach for deep learning?
- Visual recognition that tags every SKU image and pairs it with real‑time shopper cues for hyper‑relevant recommendations.
- Large language models (LLMs) that read through reviews and social media feeds to adjust copy on the fly.
- Dynamic Creative Optimization (DCO) that rebuilds ad layouts frame by frame based on context.
- Lookalike modeling in vector space that uncovers as yet untapped audiences who look like your best customers.
- Cross‑device identity stitching that links hashed signals across phones, TVs, and desktop environments without shared IDs.
Deep learning in marketing unlocks rich personalization – perfect for unstructured data – but demands hefty compute power, vast training sets, and can be opaque in its decision‑making.
Deep learning vs. machine learning: The key differences for marketers
Before we dive into examples, let’s put the tech to one side for a moment and look at practical trade‑offs.
| Feature | Machine learning | Deep learning |
|---|---|---|
| Data requirements | Thousands of labelled rows | Millions of multi-modal signals |
| Typical inputs | Tabular campaign metrics (clicks, impressions, etc.) | Images, video, text, behavioral signals |
| Training time | Minutes to hours | Hours to weeks |
| Hardware | CPU or light GPU | GPU / TPU clusters |
| Best for | Scalable automation, bidding, segmentation | Complex personalization, creative optimization |
So what does this all mean? First, use machine learning in marketing to automate the bulk of your bidding and attribution. When you need hyper-granular personalization or real-time creative optimization, deep learning is the way to go. Or, of course, you could choose a platform that does both (and plenty more).
Real‑world examples of digital advertising AI in action
So that’s the theory – but what about the real world?
How do these algorithms fare when real KPIs (and real budgets) are on the line? Here are two campaigns – one powered by machine learning, the other by deep learning – that show the tech working in the wild.
- Machine learning in action. Career marketplace Reed.co.uk extended its always‑on retargeting to Meta’s authenticated environments by plugging Criteo’s machine learning‑powered predictive bidding into Facebook and Instagram inventory. The model scored every session for apply likelihood and auto‑tuned bids and creative in real time, netting a 9% lift in job applications and an 8% drop in cost per application.
- Deep learning in action. Immobiliare.it, Italy’s largest real‑estate portal, paired Criteo’s deep learning engine with an interactive search‑widget display ad that let users start filtering properties inside the creative. Conversions jumped 246% while cost per valuable interaction fell 28.5%.
Together, these case studies show how machine learning drives efficient incremental gains while deep learning in marketing unlocks richer personalization – two sides of the same AI coin that keep campaigns improving long after launch.
TL;DR
There’s been a lot of technical detail in this article, so here’s the most important distinction to take away from this topic:
- Machine learning loves tidy, column-friendly data – bids, budgets, conversion logs, and product feeds. It spots patterns in the numbers, then makes decisions like: “Shift $500 from this ad set to that one” or “Score these leads higher than those”.
- Deep learning thrives on messy, sensory inputs – images, text, audio, etc. Layers of digital neurons identify context that humans miss, enabling AI to remix creative elements on the fly. One model can swap in the sneaker color that matches a shopper’s past purchases, then rewrite the headline to appeal more to them, and size the image for whatever screen they’re on – all before the page loads.
Both of these technologies matter, but knowing when (and how) to deploy each is the difference between results that make you say “meh” and those that make you say “yeah!” Luckily, if you’re working with Criteo, we take care of all the heavy lifting for you.
To learn more about the power of our commerce AI solutions, get in touch with our team today.
