Deep learning vs. machine learning: A marketer’s cheat sheet | Criteo
Deep learning vs. machine learning: A marketer’s cheat sheet
Deep learning? Machine learning? There's a lot of AI chatter right now. Let's cut through the noise to clearly define these two technologies.
Updated on April 9, 2026
Let’s take a step back
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.
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.
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.
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 vs. machine learning: The key differences for marketers
| 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.
Real‑world examples of digital advertising AI in action
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.
- Deep learning in action. Immobiliare.it, Italy’s largest real‑estate portal, paired Criteo’s deep learning engine that let users start filtering properties inside the creative.
TL;DR
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.
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!”