In this special guest feature, Mark Robinson, CEO of deltaDNA, looks at the challenges solved by big data in the games industry and the evolution of analytics which has enabled these changes. Fascinated by the potential of big data and games, Mark Robinson co-founded deltaDNA in 2010 and has made it his personal mission to evangelize how analytics can change the games industry. Mark has over 15 years’ experience in the data mining industry, across companies such as Heineken, Office Depot, Aviva and Unibet. deltaDNA’s mission is to unlock big data to drive player understanding, introducing the concept of Player Relationship Management to build better games.
In a relatively short number of years, the games industry has undergone a remarkable transformation, in terms of understanding and engaging its customers – the players.
Until recently, video games were purely physical products, purchased over the counter. No-one knew how much value was derived by the player because the games offered a closed environment, so any notion of player retention or behavioural analysis was strictly for the birds, angry or otherwise.
However, all of that changed with the emergence of smartphones and social networks as major new gaming platforms. These platforms were ubiquitous and the games available were fun and in most cases Free-to-Play (F2P). For the first time, gaming had become mainstream and not just something enjoyed by so-called ‘gamers’.
Because smartphones and social networks are network connected, the games industry found itself in the enviable position of being able to collect and analyze data about every single player, signalling the start of its journey towards deep-data.
Big-data for big challenges
The accessibility of casual games, like Candy Crush, opened games up to billions of new casual players who had never previously played games before. Publishers and developers, therefore, had to balance their games to accommodate the vast array of different abilities and experience levels. If the game difficulty is set too hard, then novice players will quickly become frustrated and quit. Make it too easy, and the experienced players won’t feel challenged.
However, balancing game difficulty is just one of the challenges big data solves in F2P games. Game makers also need to make money, yet incorporating ads and in-game monetization mechanics into the game can decimate the player experience and seriously impact player retention. In fact, it’s common for between 40-60% of players to quit a game for good after just one session, because they become bored or find the ads or In-App Purchases (IAP) too intrusive.
When it comes to making money, the harsh reality of F2P is that often less than 1% of players ever spend money in-game. Therefore, it’s vital for developers to have the ability to quickly identify those players who have a propensity to spend so that the in-game experience can be tailored to help increase engagement.
Data-analytics has therefore become a central pillar of the game design and ongoing management process, enabling developers to get to know their players better and find that delicate balance between fun and business.
The evolution of analytics in gaming
During the last few years there has been a significant shift in the games industry’s use of analytics, an evolution which can be tracked across three distinct phases:
Analytics 1.0 – This is focused solely on game performance, dashboard reporting of what had happened in the game but without providing the clarity that would enable developers to know where any issues may lie, or how to solve them.
Analytics 2.0 – This phase involves data mining and is about changing the game at the design level. Developers could see where the problem may lay, but could only implement broad-brush and one-size-fits-all changes to the game.
Analytics 3.0 – With the latest approach, publishers can give users the ability to change the game for each player. Big Data capabilities — capturing large numbers of data points powered by incredibly fast database technology — enables game designers to personalize the gaming experience to individual players within player segments, based on player engagement and playing style.
Data for data’s sake
It’s now possible to record a wealth of data, but with so much of it available, game developers run the risk of recording ‘data for data’s sake’. What we are starting to see now is a shift towards focusing on the right data to generate actionable insights, driven by a market in which it’s increasingly hard to retain players long enough to monetize.
Deep data taking over
It’s not enough anymore to simply measure KPIs and basic interactions. Data should be informing actions and creating high-definition archetypes. Deep data is a move towards efficiency and effectiveness, and it signifies the democratization of analytics.
With the emergence of deep data and the latest analytics tools, highly accurate game personalization is now a more effective option, and we’re seeing many more developers using it. By segmenting players based on their behaviour, games are being adapted and augmented to suit their style. Not only can this be used to change the gameplay, it can completely change the method of monetizing each (and every) player.
Using deep data analysis tools to personalize player experiences in games not only improves monetization, it improves the gameplay for the whole audience. It’s this win-win scenario that means developers will continue to adopt a deep data approach in the future.
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