Four months ago, emboldened by a complete lack of knowledge, I set a goal to become one of the world’s best game analysts. Laughing yet? How about if I told you I thought ‘game mechanic’ was an ironic title that game developers had given themselves? Or that DAU sounded more like a new Chinese philosophy than a baseline metric obsession? It suffices to say, I had a long way to go. So I did what most people do — I Googled. A lot. And when I ran out of stuff to Google, I bought books. And after I read those books, I asked industry experts questions. And just as I was starting to get cocky, I got baptized by the fire of closed betas and soft launches, during which I figured out that 40 percent of my learning time could have been spent better. So I decided to write this post. Consider the following titles:
- From Zero to (Analytics) Hero in Nine Easy Steps!
- Nine things you can do today to start Dominating Games Analysis!
- How to be a killer Games Analyst in One Month!
If any of those sounds good to you, prepare to be disappointed! This post is not a tutorial. You will not read the next nine recommendations and know everything about games analysis. This post is the post I wish I had first read when jumping into games analysis. It’s a starting bundle that outlines the process of becoming a viable games analyst.
Pick a game type
Game type definitions vary from distribution platforms (e.g. PC, Console, Mobile) to mechanics (e.g. Match-3, MMOs, Slow Sims) to genres (e.g. Action, Adventure, Racing) to some combination of the three. When selecting a game type, make sure it has enough market share to justify the selection. At a minimum, the game type should grace a well-known top 100 grossing list (or two). Picking an initial type doesn’t mean you’ll have to spend your career analyzing that game type. Picking a type means you’ll be setting a clear starting point. Be aware of the law of steeply diminishing returns when studying outside one area focus. There is value in studying multiple game types – just less so at the onset. You can spend your initial time better than trying to stitch together insights gleaned from console first person shooters and casual freemium mobile games.
Read Game Analytics by El-Nasr, Drachen, and Canossa
Every game analyst has to start somewhere, and this book is as good a place as any to begin. The book is informative, broad in perspectives, and accessible on introductory and complex topics alike. While critics of the book would probably comment on the book’s length (800+ pages), tonal inconsistency (it contains over 50 different point of views), and outdated vendor and technology references, the book contains really useful and practical information. When you are starting from zero, this amount of information isn’t a detraction but an opportunity to learn.
Identify and follow the experts
If you have completed the two steps above, it’s time to hit the search engines. A combination of metrics and industry buzzwords should return a host of articles and sites. If you’re feeling lucky, try typing a specific hit game title and “analysis” into the search bar. The information will consist mainly of re-treads of the same industry studies or findings but once you sort through the noise, you’ll discover valuable information from individual contributors and gaming service vendors.
Like book recommendations, expert analysis or opinion is usually tied (by necessity) to commercial interests. Always be mindful of the context in which the information is given and search for dissenting opinions whenever possible. Still, knowledge is gold in the game analyst economy so express virtual thanks and throw any expert blogs and sites into a RSS reader such as Feedly. Once you have subscribed to the experts’ web content, take your cyber-stalking to the next level.Follow the experts on Twitter. Social media has changed the way information is delivered so be sure to use all the tools you can in your research.
Know your Metrics
By this point, you’ll have read so much about game metrics that they’ll color your daily interactions. I actually spent five minutes of my life debating whether or not Shamu the whale would have been perceived as cuter if SeaWorld had named him ARPU instead. I’ll never get those five minutes back.
Metrics serve as the conversational starting and ending point for most analysis meetings. For all the talk of fun factor and innovative design, games are ultimately measured and judged by their bottom-line numbers. Did you develop an amazing game? How do you know for sure? Your mom’s approval doesn’t count. Often, a metric is a top line indicator of more subtle or nuanced problems. The metric points you in a direction and then it is up to the analysts to dive into the detail and figure stuff out. It’s why we get to have jobs. Knowing the metrics is not a differentiator but it’s a baseline requirement.
But be warned, not all is Candy Crush dust in metrics-land. Some people believe the use of metrics spawns derivative, unoriginal games geared more towards manipulating users into spending money than making the game fun. The extreme use of data can certainly produce terrible results but I and many others would argue that the non-use of data is equally if not more hazardous. Ultimately, most industry people believe data-informed design (as opposed to data-driven design) is something with the capacity to make games both more enjoyable and more profitable.
Acquire the base skills
Start with SQL and Excel. SQL is the predominant database query language and although there are variations across platforms, the fundamentals are fairly consistent. SQL allows you to retrieve, format, organize, and manipulate data from most traditional data stores. Excel, of course, is the most widely used data analysis tool in the world and provides a spreadsheet-centric way of viewing, presenting, and acting on data. Remember that these are just the starting base skills. The technology and tools used to derive data insights are amazing and always changing so if after a year or two you find yourself only manipulating spreadsheets or just writing SQL queries, you will be working at a significant competitive disadvantage.
The premise of checklists is simple: The less you rely on your memory the better. When diagnosing game performance, many analysts rely on their learned knowledge to start exploring the data. How did it become learned knowledge? Through repeated memorization of the metrics, dimensions, and techniques required to analyze the data. But no matter how good your memory is, trying to remember everything is hard and people make mistakes.
Checklists will save you the stress of trying to remember everything. They lock in analytical thought in a concise manner, make process more repeatable, and prevent mistakes — allowing you to focus brainpower on new challenges and new questions. If you take away nothing else from this post, please use checklists. They are really wonderful things.
Slow down on learning new hard skills
If you want to do your job well as an analyst, there will be an overwhelming amount of hard skills you want to learn (e.g. Python, Memcache, R, Qlikview/Tableau, Informatica). The problem with learning new hard skills is that it is mentally taxing and often requires mental energy you cannot afford to spend while in a high information consumption period. Don’t be afraid to give yourself ample time and space to grow true hard skill competency and lengthen your learning curve.
Be the enemy of simplicity
Between our sound bite driven media and Apple’s minimalist design ethos, the cultural desire to simplify or streamline has never been stronger. In games analysis, the growing desire for simplicity manifests itself in articles that purport a singular number to be the principal component of success. Ultimately, each number is dependent on a whole host of other factors and hitting a goal benchmark or two does not constitute game success. Even seemingly obvious connections such as 2nd and 30thday retention do not always correlate. Instead, measure as many aspects as you possibly can (without sacrificing game performance or blowing out the analytics budget). Design data structures to provide the best performance and most flexibility – not just to be the simplest to read. Approach game performance from multiple angles – not just the top-down metrics approach that everyone else uses. Simplicity is great for user interfaces and visualizations but some areas such as data warehouses and statistical algorithms are necessarily abstruse. Embrace the complexity.
Have a vision
Your job as an analyst is not just to monitor numbers and provide daily, weekly, and monthly reports. We have computers for that. Your job is to figure out the reasons behind the numbers and deduce potential avenues for improvement. Good analysts are competitive differentiators to their respective game studios. Have a vision on what you think data and analytics can do and then go after it.
Last but not least, try to have a little fun as you embrace the process. After all, we get to be in an industry where millions of users can validate a hypothesis in near real-time. At some point, stop and ask yourself, “How could it get any cooler than that?”