Data mining using machine learning (ML) is a field that always fascinated me. You give an learning system millions of collected data, and it outputs back unexpected insights, that can help you focus on what matters, make you drop things that don’t work, clears mystery from your customers behaviors and potentially reorient your company strategy.
It’s compelling, but that makes you imagine the machine is doing the hard job … Of course not, the machine remains stupid, as always. Data mining is a long iterative process that requires a good load of intuition and a deep understanding of machine learning algorithms. However, it remains more accessible and fun than statistics to experiment because of its intrinsic empirical approach – sorry for feeding what’s already being an unfair preconception that favor ML trend over statistics since decades…
I’ve been longing to put in practice my learning in that field with Google Analytics (GA) data. What more can ML offer in addition to the great analysis features Google Analytics provides ? Is it even possible? Suspecting that GA is not designed for that, I started a short experiment to explore this potentiality.
In this article – addressed to GA novices and ML enthusiasts – I will give a basic introduction about the requirements and benefits of ML, and list some constraints with GA.
In a future blog-post (edit : here), I will share my findings on how to quickly get machine learn-able traffic data, describe the technicalities of the exploration, and provide the minimum to let you do your own experiments.
Looking for a job?
Let’s imagine a web site, like the Liip blog on which you are presently. And a visitor like you, reading this blog-post. Since Liip almost always has some open positions, we want to make sure that if you’re a developer, you won’t switch on your next data mining article before you’ve visited our open positions. If not, then you might well be a future client and thus interested to know about our service offering and expertise.
ML could help us to create a decision model to predict how much of a developer you are according to your attributes (ex : region, browser, visiting hours).
Our Google Analytics account contains thousand of examples where a visitor ended up visiting our job page, or not. That’s food for an automatic learner!
Continue reading about Machine learning on Google Analytics
Tags: Machine learning