For the rest of this article, for brevity, we will refer to a SERP/keyword pair as a keyword, even though a keyword can have multiple SERPs.
Introduction
Search intent is a nuanced topic. And establishing a set of hard and fast rules for figuring out Google’s intent for a given keyword is nearly impossible. Fortunately, developments in artificial armenia whatsapp intelligence and machine learning over the past decade are making new methods possible. We now have the tools to use the massive amounts of SERP data that AccuRanker processes daily to “train” a machine learning model.
As training data for the new search intent model, we used a combination of unlabeled and hand-labeled data. This dataset consists of keyword search intent labeled by human experts and corresponding SERP data. With machine learning techniques, patterns appear. These patterns are translated into a model that can be used to find keyword search intent outside the training dataset.
Using a machine learning model allows us to predict search intent more accurately than rule-based approaches. However, achieving 100% accuracy is impossible for many reasons. Some of these reasons are:
Even humans (up to 40%) looking at SERPs disagree about search intent.
There is not always 100% alignment on the definition of the different categories of search intent.