How AI Works for Marketers: Understanding Machine Learning Behind Marketing Tools

Definition

AI for marketing combines different machine learning approaches (neural networks, statistical updating, logical rules, and pattern matching) to automate decisions, optimize campaigns, and predict customer behavior through probabilistic rather than deterministic computing.

The Four Tribes of Machine Learning

As Patrick Gilbert argues in Never Always, Never Never, understanding how AI works starts with recognizing that not all machine learning operates the same way. Pedro Domingos identified four distinct "tribes" of machine learning in his 2015 book The Master Algorithm, each with different philosophies about how machines should learn. Modern marketing AI combines elements from all four approaches, making it essential for marketers to understand these fundamentals.

Connectionists learn like a brain. Neural networks strengthen connections based on feedback, adjusting millions of internal settings to recognize patterns in data. This powers Google's search results, Meta's news feed, and most image recognition systems. The key insight is that the machine doesn't need explicit rules. Instead, it discovers patterns from data through a process called backpropagation, where the network adjusts its internal weights based on whether predictions were right or wrong.

Bayesians learn like statisticians. They start with a belief about how likely something is, then update that belief as new evidence arrives. A classic example involves predicting whether the sun will rise tomorrow. Starting with no data, you might assume 50/50 odds. After one sunrise, you update. After thousands of consecutive sunrises, your confidence approaches certainty. This foundation powers much ad platform AI, where Google and Meta's automated bidding systems use Bayesian principles to get smarter over time.

Symbolists learn like philosophers. They deduce rules from examples using logic. If Socrates is human, and all humans are mortal, then Socrates is mortal. Once the machine learns that rule, it can apply it elsewhere. Decision trees fall into this category. Unlike neural networks, symbolist systems excel at explaining their reasoning, but they need clean, structured inputs to work effectively.

Analogizers learn like lazy students. They find the closest match to a new problem and assume the answer is the same. Meta's Lookalike Audiences exemplify analogizer logic. You provide a list of your best customers, and the system finds other users who "look like" those customers across hundreds of behavioral and demographic signals. It's not deducing rules or updating probabilities. It's finding the nearest match and betting the pattern holds.

How Modern AI Combines All Four Approaches

In 2017, Google researchers published "Attention Is All You Need," laying the foundation for the Transformer architecture that powers modern large language models. This breakthrough combined multiple learning approaches in ways that seemed impossible when Domingos was speculating about a unified "master algorithm."

The Transformer architecture uses connectionist neural networks as its foundation, learning through backpropagation and storing patterns in billions of connection weights. It applies analogist principles to understand context through self-attention mechanisms that ask, "What other parts of this input are most relevant to understanding this particular word?" And it relies on Bayesian probability to generate responses, calculating the likelihood of the next word in a sequence.

The underlying technologies have existed for decades. It took until 2017 for engineers to creatively determine how to combine these approaches in a way that achieved something Domingos had speculated was nearly impossible just two years earlier.

Patrick Gilbert, Never Always, Never Never

Deterministic vs. Probabilistic Computing in Marketing

Understanding the difference between deterministic and probabilistic computing is crucial for working effectively with AI marketing tools. Deterministic systems follow strict rules and deliver precise outcomes every time. A calculator is deterministic: 2+2 always equals 4. When you click "Add to Cart," the JavaScript follows exact steps to update your total.

Probabilistic systems work differently. Instead of following fixed rules, they use patterns to generate likely responses without knowing whether those responses are correct. Most modern AI falls into this category, and occasional mistakes are a feature, not a bug. As Sam Tomlinson explains, "LLMs generate responses using probabilistic models, not deterministic models. These output a 'correct' or 'right' answer only in a probabilistic sense, not a binary sense."

Why Ad Platforms Show Strange Results

Many marketers abandon AI-driven campaign settings after seeing what look like "kooky" results. Broad Match targeting might show ads for seemingly irrelevant search terms. Your first instinct might be to conclude the system is broken. But those odd results don't necessarily mean failure. They could be part of the learning process.

The success of AI-driven advertising doesn't hinge on every individual search term being relevant. It hinges on whether the overall system delivers conversions at the efficiency you need. A few strange queries shouldn't cause panic. The system is designed to learn over time, identifying which auctions drive quality traffic and which don't.

• AI advertising systems are probabilistic by design, not deterministic
• Odd search terms or targeting choices may be part of the learning process
• Overall performance matters more than individual query relevance
• Platforms have reduced data access partly because advertisers misinterpret probabilistic results
• Success comes from partnership with AI systems, not perfect control over them

The Shift from Deterministic to Probabilistic Advertising

Meta's evolution illustrates this industry-wide shift clearly. In the early days, Meta's advertising platform was largely deterministic. You could target users based on explicit data points with high accuracy. The system tracked behavior across websites and apps, feeding precise data back into algorithms.

Then came GDPR and Apple's App Tracking Transparency. Meta lost access to much of the granular user data it relied on. Deterministic models depend on consistent, reliable data, and that data suddenly became unavailable. Meta pivoted toward probabilistic models that make educated guesses about user behavior based on patterns and inferences.

Pulling content from anywhere on the Facebook or Instagram networks, though, is a fundamentally different problem, that requires fundamentally different approaches; these approaches are probabilistic in nature and built on machine learning.

Ben Thompson, Stratechery

For advertisers, this transition has tradeoffs. Probabilistic models allow Meta to maintain ad performance even with reduced access to individual-level data. But it also means less visibility and control over how ads are served and measured. Marketers who understand how to work within probabilistic systems will outperform those expecting deterministic precision from tools not designed to provide it.

Data Quality and Learning Systems

Analogizer systems like Lookalike Audiences are particularly susceptible to data quality issues. The learner will only be as good as the data you feed it. If your conversion data includes problematic patterns, the AI will amplify those patterns.

Consider a retailer of women's dresses where 70% of orders result in returns. If they upload all customer data to build lookalike audiences, Meta's algorithms would optimize for people likely to buy many dresses with high conversion rates. But the data would be misleading, generating target audiences even more likely to buy and return items, compounding the original problem and reducing profitability.

Key People & Works

Practical Applications

• Understanding why Google Ads shows seemingly irrelevant search terms during learning phases
• Working effectively with Meta's Lookalike Audiences by providing clean conversion data
• Setting realistic expectations for AI-powered campaign optimization
• Interpreting probabilistic results from marketing AI tools
• Choosing between speed-focused and reasoning-focused AI tools for different marketing tasks