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Technology and tracking

Machine learning

Also called machine learning, ML.

Short answer

Machine learning is a branch of artificial intelligence in which a model learns patterns from data instead of following hand-written rules. In affiliate marketing, machine learning is used above all for fraud detection, bid automation in ad platforms and product recommendations.

Definition of Machine learning.

Classic software follows rules a developer has written: 'reject clicks from this user agent'. A machine learning model works the other way round – it is given thousands of examples of, say, legitimate and fake traffic and finds the patterns that separate them by itself. The model improves as it sees more data, but its decisions can be hard to explain afterwards.

In affiliate marketing you meet machine learning in several places: the automatic bidding of ad platforms (Smart Bidding in Google Ads), web shops' product recommendations and fraud detection at large platforms and payment providers, where click patterns, time intervals and device data are analysed for anomalies. AI answer engines such as ChatGPT and Google's AI Overviews are also built on machine learning – and they are shifting where affiliate traffic comes from.

Machine learning and AI are often used interchangeably, but AI is the umbrella term – machine learning is the technique that powers most of it in practice. A large language model is machine learning trained on text; a simple model that scores a click as bot or human is too.

Rules or model – two ways to catch bots

Bot protection can be built in two ways. A rule-based filter recognises bots by their signature – user agents, known IP ranges, headless browsers – and rejects them deterministically: the same input always gives the same decision, and the decision can be explained. A machine learning model instead scores each click on patterns and can catch new botnets the rules do not know yet – but false positives come with it, and whoever is hit can rarely be told why.

How it goes wrong

  • 01

    A black box with no appeal

    When a model rejects traffic or blocks an account without giving a reason, the affiliate has nothing to fix. The test for any automated safeguard: can the decision be explained, and can it be checked by the person it affects?

  • 02

    The model inherits the data's biases

    A model trained on historical traffic learns the patterns of history. Seasonal peaks, campaign pushes and new traffic sources can look like anomalies – the result is false positives exactly where an affiliate is trying something new.

At Affilyflow

Machine learning in practice

Affilyflow's bot filter is rule-based: known bots are identified by their signature and get neither click registration nor attribution. That is a deliberate choice – a rejection must be explainable and checkable. Every tracking event is logged, including those that did not become a sale, so there is a trail to look up.

Frequently asked questions

What is the difference between AI and machine learning?

AI is the broad term for systems that solve tasks that normally require human intelligence. Machine learning is the underlying technique in which a model learns patterns from data instead of following fixed rules. Most of what is called AI today – from language models to bid automation – is machine learning.

How is machine learning used in affiliate marketing?

Mostly in three roles: automatic bidding in ad platforms, product recommendations in web shops and fraud detection, where click patterns are analysed for anomalies. Affiliates also feel it indirectly, because search engine rankings and AI answer engines are built on machine learning and shift where traffic goes.

Can machine learning catch click fraud?

Yes, to a degree – a model can learn what legitimate traffic looks like and flag anomalies as suspicious. The drawbacks are false positives and decisions that are hard to explain. A rule-based filter against known bots is more predictable, and the two approaches are not mutually exclusive.

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