The global sports market is huge with its total revenue projected to be around 90 billion dollars in 2017. In sports big data and web scraping is one of the sectors in which data analytics…
Summary
Google Analytics is rife with Fake or Ghost referrers, this post takes a look at how Google itself classifies these users by Age, Sex, Interests etc
Anyone with a website that uses Google Analytics must have noticed quite a bit of traffic coming from “Referrals”.
Some of the sites, whose names keep changing every month, also seem to generate quite a bit of traffic for your websites.
Sites such as site-auditor.online or fix-website-errors.com or free-social-buttons-aaa.xyz or any of such similar websites.
The funny thing is that these sites don’t really refer any traffic to you (sorry for bursting your popularity bubble). They actually don’t even visit your site, they just tell Google that they did – simple.
The sad part is that after all these years, Google with all its smarts and money hasn’t been able to curb this menace – who doesn’t like (fake) inflated numbers?
Did we say Google had smarts? (oh yes we did) and part of those smarts they also have a “profile” of users that live on the Internet and visit sites all day. They know how old you are, what you like, your gender etc.
We decided to analyze what Google knew about these fake users, so lets take a look from a data perspective.
Gender
Predominantly Male
Age
Fairly young, assuming old people aren’t fake 😉
Affinity Categories (reach)
Whatever this title means, the actual data is better self-explained
In-market segment
Not sure what that means
Other Category
Again – no idea
Top 10 Countries for our beloved fake users
45.3% were from Russia (with love)
