The Internet started as a very fragmented network where users spent their time on a variety of static web pages serving specific niches. All that changed around 10 years ago with the explosion of the giant online platforms that acted as aggregators and mediators of content. Google, Facebook, Amazon, and a few others, became the focal point for most online users. As of today, nearly 60 percent of all time spent online by US users is spent on the top 10 websites. Facebook alone is responsible for over 20 percent of time spent online while YouTube for a smaller but still significant 6 percent. Similar figures apply for Europe.
The success of most of these platforms is based on user generated content (UGC) – the user here can be your average Joe or an actual organization. Whether it’s a video uploaded to YouTube, a second-hand bicycle listed on eBay, or a shiny new TV listed on Amazon, much of what we browse today is generated by a third party that is utilizing these platforms to reach us.
What did we create? A system we admin, but barely control. A system full of content, and information. Self-growing. A new creature!
The boom of content was noticed by several companies. In the last few years, several companies in the field of analytics & market intelligence focused solely on either collecting as much data as they can, and matching that with the demographics they have, or, analyzing the piece of content itself, what is mentioned in a tweet, or what sentiments are there in a YouTube video comment. The companies doing this are either giants like Nielsen, ComScore, and Ipsos, or a growing number of startups. These are the guys who tell you that “In Finland, Male between the age of 20-30 listen to songs over YouTube most”, or that the word “easy” is the most trending now over Twitter regarding your product.
On the other hand, with growing competition, the demand is growing for deeper and deeper analysis. It’s obvious the time to make millions from “male in their 20s, watching YouTube” has passed.
There is a growing need for deeper granular analytics. If the content is a song – is it Pop, or Metal? If it’s Pop, which singer? What quality the song is on YouTube? The list can go on for long. It even gets more demanding, when you discover that the same song is on YouTube, Facebook, and Spotify. Try doing that with a massive amount of data – it is nearly impossible without something to help you navigate.
That’s what we are building at Floralytics – the DNA the Internet has been missing, something desperately needed for a deeper analysis.
Dish is a co-founder and CEO of Floralytics, a Finland-based Startup.