Your cart is currently empty!
Today, I want to take you on a journey back to the inception of this project. When I first entered the video game industry, it felt like a natural choice to dive into the world of Twitch data and more.
In this industry, it’s all about getting games in front of the right audience. Brands can do it in a couple of different ways, such as having press media outlets like IGN, Game Rant, and PC Gamer cover their games (that can be proactive outreach or organic earned media), or engaging with content creators to play and promote them.
While the role that each of them plays varies from country to country, the impact that creators have on video game promotion has been growing worldwide. So much so that some creators garner more watch time on a single stream than showcases such as gamescom or PC Gaming Show, not to mention that some of them charge brands a significant amount of money to feature games in those showcases.
In this platform, I started exploring the content creator aspect of game promotion, particularly on Twitch, analyzing Twitch streams to understand who’s playing which games and how well those creators are performing.
[Don’t worry though, many other metrics are going to be added to the platform very soon.]
And here is what makes GGs Analytics different from tools such as TwitchTracker or SullyGnome.
I’ll start by explaining what they offer and what they lack.
First, they both offer several years of historical data and that data loads faster on your screen. While I’d love to have the data load faster, my focus is on providing a different type of perspective, and because of that, I end up sacrificing the loading speed.
TwitchTracker, to me, is the best platform for you to get aggregated numbers for monthly periods or to check individual channel stats.
Sullygnome, on the other hand, even though it can provide aggregated stats just as useful, wins for me in terms of granularity and the ability to export data.
GGs Analytics is not looking to replace any of them, we are here to fill in a gap that we understand exists. I’ll explain using a use case.
Use Case
Let’s say you want to understand how a game performed two weeks after its release, so you want the number of streamers and total number of hours watched, broken down by the size of the channel. Let’s take, for example, Helldivers 2, released on February 8, 2024.
For this use case, from the two platforms, you would use SullyGnome. You don’t have the option to select February 8 as the start date, so you would have to select the whole month. You can’t really select the last X number of days because that window is either gone or will include way more than you need.
Because you only need the period between February 8 to February 21, you would have to download all February data so you can later remove the ones that are outside of that period. As of the time I’m writing this, you can only see a max of 100 results per page, and according to the website itself, Helldivers 2 had 149,753 unique streamers in February. That means that there are almost 1,500 pages, e.g., 1,500 files for you to download. Kind of impracticable.
That’s the gap I mentioned.
The way I see it, before GGs Analytics you either have your own database to query from or you are limited in the way you analyze the data.
With GGs, you can click on the Game Performance dashboard, set your custom period and you would get all that information in a couple of seconds. And more.
Okay, but if you all have the same data, why are there differences in how websites make it available?
The easy answer is choice. Every week, Twitch hosts more than 7 MILLION livestreams, and GGs Analytics tracks every single one of them. However, because it isn’t very useful to make every single one of them available as the insights come from aggregations and trends, each one of us decides how to showcase the data in our own way. Also, costs. Costs are one of the most important decision factors here, as the more data we store and make available, the higher the costs.
For GGs Analytics, the magic happens when I start categorizing games, like for example by genre or associating them with brands. By doing that alone, brands can explore their competition, find similar games in the market, and see how their game is performing. They can also compare creators and identify the perfect fit for their campaigns.
Here are some other use cases
Imagine this: you are a brand that is about to launch a new game. You can use this website to pinpoint the creators who align perfectly with your game by analyzing games within the same genre, similar games, or even the competition – all of that on the same web page! You can then offer them early access keys or even set up paid partnerships, all based on data-driven decisions. But that’s not all. You can also get a ballpark estimate of the costs beforehand based on the concurrent viewer numbers we provide, all based on the most up-to-date data available. Here, there’s no intuition – I’ll leave the intuition part to you.
There are more use cases for brands, such as checking how other games performed to set your own goals, checking how a developer or publisher is performing and making comparisons among them on the fly, checking how other brands’ games are performing as a whole, and much, much more.
Now, let’s flip the perspective. Imagine you are a content creator. You want to grow your channel so brands like the above can find you. Here, you’ll have access to your channel’s overall performance and how it’s performing against other streamers. You can choose to look at the big picture, stacking yourself up against the streaming community as a whole. Or, you can narrow it down by comparing performance with streamers who’ve tackled the same game, a similar one, or a title from the same genre.
This project is about bridging the gap between creators and brands, offering you a platform where decisions are based on solid insights, not just hunches. It’s a win-win, where creators get to understand their value, and brands get to find the perfect match for their games.
In my next post, I’ll delve deeper into the technical aspects, giving you a sneak peek at the data pipeline that makes it all happen. If you are a data nerd like me, you are going to love it.
Catch you in the next one!
Gus
