Editor’s note: Hassan Baig is an entrepreneur who runs White Rabbit Studios, a South Asian gaming startup he founded four years ago in Pakistan. Follow him on Twitter @baigi.
Mobile gaming is a huge worldwide opportunity at the moment, having clocked in at $9 billion in 2012, and it is poised to grow further in the coming years. With the world’s 1 billion smartphones scheduled to almost double in number by 2015 and games responsible for a whopping 66 percent of all app revenue, it’s easy for anyone to do the math and see where this is going.
Game development continues to have a bright future, but only for those who can develop profitable titles. Pursuing such profitability is an exact science now, with monitoring analytics and continuous A/B testing having become the staple of game development. In fact, Zynga – the gaming company to have popularized (if not introduced) the use of analytics – has been often categorized as a big data company.
One can imagine metrics to be ‘levers’ that a game developer can push or pull to create a desired outcome. Some levers have a generous range of motion, while others are more limited. In the end a game developer’s task is essentially to figure out the perfect combination of lever positions that will produce the best financial outcome at the least cost. Notions of creativity, novelty and fun are all confined within the prism of this analytics-centric approach: They have wiggle room as long as they improve analytics. That’s the fundamental philosophy behind modern-day game development.
For those looking for a more visceral understanding of game analytics, I’ve set up a simple mathematical simulation that compares game performances across hypothetical retention and viral profiles. It’s in simple spreadsheet format and can be downloaded here. I’ll quickly list out the assumptions governing this simulation, after which I’ll explain the noteworthy conclusions one can draw from the numbers.
Note that Game No. 1 is treated as a benchmark and the remaining games differ from it by no more than one metric. For example, Game No. 2 differs from game No. 1 in terms of average player lifetime (and is similar on all other metrics). I have used an average CPA of $1.3 throughout to calculate the games’ respective advertising spend. Lastly, in case more clarity is needed on the definitions of the terms I’ve used in the bullet points above, explanatory descriptions can be found in one of the tabs on the spreadsheet. Now on to the simulation’s broad conclusions.
1) The greater a game’s average player lifetime, the higher its DAU count. And since the DAU is an approximate measure of player engagement which, in turn, is directly correlated to revenue generation, average player lifetime turns out to have an obvious effect on a game’s money-making potential.
In the tables of game No. 1 and game No. 2 in the tab titled “Comparative Revenue” in the spreadsheet notice how game No. 2’s higher average player lifetime gives it superior DAU and revenue numbers in comparison to game No. 1.
2) The greater a game’s d2 retention, the higher its DAU count. And as explained earlier, DAU is directly correlated to revenue generation. Hence it can be surmised that d2 retention has a very obvious effect on a game’s money-making potential. It’s for this reason that most gaming companies utilize A/B testing to optimize their games’ retention rates early in the launch cycle. Also, given d2 retention usually doesn’t optimize beyond single-digit percentages, games with low retention rates are culled very quickly. Look at the comparison between games No. 1 and 3 in the spreadsheet: The latter’s higher d2 retention gives it a better DAU profile, which in turn translates to more revenue overall.
3) Big advertising budgets do not improve a game’s profitability. That is, if a game is a poor financial performer over a certain demographic of players, buying more users for it from the same demographic will not help the bottom line. It’s the reason gaming companies optimize a game’s metrics before buying expensive eyeballs for it, and it’s also the reason certain games get killed way before they’ve seen a full-fledged launch.
Those interested can check out the illustrative comparison between game No. 1 and No. 4 in the “Comparative Revenue” tab in my spread sheet.
4) The greater the virality of a game, the greater its profitability. That is, greater virality ensures more freely acquired users, hence minimizing a key cost consideration: cost per user acquisition. A somewhat similar effect can be garnered via having a captive player network which can be cross-promoted at negligible cost to another game – just that in the former case, virality causes the overall player network to itself expand as well.
Overall, the ability to get free users is extremely important for any gaming company’s financial health, so it’s no wonder that Mark Pincus stressed investing and leveraging Zynga’s player network as a cornerstone of the company’s future strategy in his recent earnings call.
As previously noted, avid number crunchers can have a quick look at the comparison between game # 1 and game # 5 in the “Comparative Revenue” tab in my spreadsheet and appreciate the marked difference between the two games’ eCPA as a result of differing K factors.
5) Higher monetization per user leads to greater profitability. This is quite a straightforward result, but its implications are far-reaching. It’s the reason gaming companies contend for long/multiple sessions and flock around the 43 year old housewife or the 28 year old male gamer, it’s the reason carrier billing is beinghailed as a boon for emerging markets like South Asia, it’s why real-money online gambling is heating up and even why Candy Crush Saga went cross-platform.
Analyze the comparison between game No. 1 and the relatively higher ARPDAU game No. 6. The difference in total revenue between these games illustrates my point.
This concludes the results of my spreadsheet simulation. Many of these results are confessedly intuitive and though looking at my simulated numbers may give a more visceral understanding of fundamental game analytics, it’s only reinforcing what many already know. After all, it’s quite obvious that a game developer should strive for producing a title with lengthy average player lifetimes, high retention rates, great virality and high ARPDAUs.
Rapid prototyping and play testing: This is critical for quickly gauging the potential retention of a proposed game design before full-fledged work is to start on it. Many game designs are just not worth the effort of taking to fruition.
Extensive A/B testing: Robust, extensive A/B testing throughout the life cycle of a game is very important because even minor bumps in analytics have a directly measurable effect on profitability.
Pipeline for frequent updates: A reliable pipeline to deliver frequent content updates is a must-have in the bid to prolong average player lifetimes. Once a gaming company commits to a game, it needs to consistently perceive the game as a work-in-progress.
Big-name gaming companies are already following the aforementioned fundamental tenets in their production pipeline – it’s more often the smaller studios which persist with informal methodologies. That’s bad practice because instead of facilitating the smaller studios to catch up, it exacerbates the gap between the big and small fish over time.
As the mobile gaming market continues to spew riches for the foreseeable future, it is imperative that modern day game developers structure their entire operations around the fundamentals of data analytics instead of trying to fit a metrics-based veneer over introverted, blind game development. Their jobs are basically to create digital entertainment products that activate the maximum possible number of highly viral users on a daily basis for the longest sessions.