Lately, I’ve been receiving some questions from multiple people on LinkedIn and other platforms regarding being a game analyst, or a data analyst or product analyst working in the videogames industry.
It’s not farfetched to conclude that videogames gained increased importance in peoples’ lives during 2020. The entertainment industry has a history of performing well during an economic recession or turmoil, and 2020 revealed itself as a good game for games.
We can see that multiple players in the industry had a year of growth if we look at the stock prices. So it’s not strange to see the increased amount of professionals wanting to break into the industry as analysts.
I’ve been working in the games industry for some years now in roles as different as data analyst researcher for academic serious games, working in business development for a third-party Android market place, owning my own studio in VR, and performing duties of technology lead and data-driven product manager, to being data analyst working on Free to Play games at Rovio, and now taking up the challenge of being a product analyst for the Swedish company Star Stable Entertainment. Since I’m currently onboarding at my new role, I think I might have some insights that might help aspiring game analysts (even though most of this advice can be used for other industries).
What do I need to become a Game Analyst?
This is a complex question because different companies want different profiles at different times. A former supervisor once presented a model to map a team of data analysts given their interests and strengths. I think this model can be very useful for someone new trying to get themselves situated.
We can think of the analyst job as something that borrows from 3 main areas with strong overlaps:
- Tech – Querying (SQL), knowing how to program develop (data) products, knowledge of database technology, data visualization, etc…
- Mathematics – models, statistics, experiments, etc…
- Business – Product/Industry knowledge, KPIs, Technical analysis, communication, etc…
When reading a job description for a Data Analyst position it’s important to have the self-knowledge of where our strengths lie, and how and where and how we want to grow as professionals. There’s a high variance when it comes to responsibilities, and it might take some time to find a good match.
Someone trying to break into the industry from an academic position or straight out of university might need some time to develop business knowledge. At the end of the day, the best policy is to try to understand how you can best add value to the company.
That said, I often get asked “Do I need to know how to program to be a game analyst?” and my most earnest answer is yes. You should have at least some basic knowledge of programming (especially in a language framework widely used like Python or R). If you do have an analytical mindset this should be a skill well within reach.
Lastly, a common pattern that I observe in all the analysts that I’ve met is that they’re people that combine tech/math background with better than average soft skills. Communication and teamwork are crucial aspects of this job.
A Games Analyst is a Data Advocate, not a Data Zealot
A game analyst often works very closely with the game teams. Game teams are very diverse multi-disciplinary units, and in my opinion, interacting with them is the best part of the job. Your main responsibilities as a data analyst in the team are to help the team make data-driven decisions and promote data literacy. For this to happen you have to earn the team’s trust.
Part of being able to do this job is recognizing that not all decisions should come from data. It’s important to make sure that the creativity and product intuition of the team is encouraged.
Your role is to advocate for using data to make better decisions, help the team manage uncertainty and risk.
Understanding the Business
In order to break into the industry in a position that is not an internship or a junior position, it’s necessary to understand the business models, objectives, standard metrics. This often takes time and the best way of building this knowledge is hands-on.
It’s important to understand:
- How does the game monetize? Premium, Subscription, IAP/microtransactions, Advertisement, etc…
- How does the game acquire new players/customers?
- How do players behave while in the game?
In a very simplistic way, in order to understand how the game monetizes, you will want to understand what is the shape of the LTV (lifetime value curve). How does conversion happen?
The player acquisition has to do with the inflow of new players and customers. If we run ad campaigns we will want to know how much we can afford to spend in order to attract new customers, or how to optimize a given budget to get the highest ROI and volume possible.
When the player is in the game it’s important to understand how they play the game.
If we’re working on a premium game, it’s often important to deliver an experience that will last at least for more than 2 hours because according to Steam (https://store.steampowered.com/steam_refunds/), players can refund games if they have played them less than 2 hours.
Imagine that we’re working on a F2P game that monetizes primarily via in-app purchases (IAPs). Your job is to help the team keep players in the game for as long as possible (retention), where they’ll be exposed to opportunities to spend money (conversion).
At the end of the day, even large game companies, tend to work a little bit like startups. If you want to thrive in this environment it’s important that you understand how you can add value. Prioritize your tasks having an ROI (return on investment), mindset. Help the team focus on optimizing the game and measuring the effect of optimizations. And don’t forget to also set aside time for being creative and trying new ideas like exploring available data and trying different algorithms.
Hope 2021 brings new opportunities for you. And I wish everyone looking into breaking into the games industry as a game analyst good luck!
If you’d like to learn more about the free to play business I recommend the following reading: