The problem: in Google AdWords, advertisers buy keywords in broad match. Google then triggers ads on search queries it considers similar, but which often have nothing to do with the campaign's commercial intent. An advertiser selling "professional IT supplies" could see their budget consumed by visitors searching for "free computer courses" - paid clicks that will never convert.

The ML solution: the system scraped Google search results (SERP) for each purchased keyword, analyzed the type of displayed results and extracted the thematic classification and search intent (transactional, informational, navigational). These signals were vectorized via TF-IDF and fed into a supervised classification model (gradient boosting on Azure ML).

The result: the model classified each query as relevant or irrelevant. Keywords identified as false positives were automatically suggested as negative keywords to be excluded.

This is just one example among the many optimization algorithms built into the platform.