How Big Data Can Boost the Gaming Industry
Big data is an industry worth $130 billion in 2016 and expected to soar to $203 billion by 2020. And its usage in modern day business and marketing is almost limitless. The ability to improve the consumer experience for both the end user and the corporation through the proper collection and utilisation of big data can have overarching ramifications. Gartner estimates that it is set to be the biggest trend in analytical marketing by 2018. Big data can have many uses: from driving customer engagement through optimising the consumer’s journey to capitalising on the best course of advertising to create the strongest conversion rate to sales. But how does this translate to the $101.1 billion gaming industry? And how can game developers look forward to truly taking advantage of the big data boom?
How Do You Collect Big Data for Gaming?
One of the most effectual uses of big data is through monitoring online consumer behavior, with 85% of respondents to a survey by Forbes stating they are collecting big data. By tracking movements online and reporting back on how best to target individual consumers, big data has found one of its fundamental uses. In gaming, before social media or demographic data is even collected, a great deal of consumer experience data is collected too. The game can log how users interact with the game, including: play time, the time of day they play, who they play with, and who they interact with while playing. Add to this social media engagement to identify how influential a player may be, and any information on gender, age, and location (which in turn can estimate financial status and other socio-cultural information), and there is a user profile created that can be used to monitor trends and evaluate targeting success.
Console Gaming and Big Data
Console gaming and big data can marry well to create an immersive experience for players when it comes to when they play and how long for. Creating massive campaign games like Skyrim or addictive quick-fire games like Activision’s Call of Duty allows developers to latch on to trends in gameplay. Call of Duty: Black Ops 3, for example, utilised a team of data scientists to make the gameplay more “fun”. Skyrim’s data will have discovered how long a player spends on the game – and can therefore increase the interactive elements to keep them playing longer. The various incarnations of FIFA give an idea of the interest in sporting games in a household, and can be used by the console stores to suggest other, similar sports games that are more likely to appeal to these players. The targeting campaign can find who plays the most and therefore would be more susceptible to the ad prompts. The job is even easier if Facebook and Twitter accounts are connected to the games’ servers – players’ likes are amalgamated and their gaming history evaluated in order to advertise products the consumer might actually want. If you’re interested in something on Facebook and connect it to your console, every time you activate Facebook you can be targeted for similar games and interests in the ad section.
Online Gaming and Big Data
Online gaming, especially iGaming – which yields a 32% market share of all UK gambling alone – is another example of how the consumer experience can be improved through the use of big data to target their offerings to what gamers might want to play. There are hundreds of titles – some capitalise on the skills needed for poker, like standard poker, blackjack, and baccarat, while others lean heavily on the fun and luck of bingo. Classic slots provide the standard gaming experience to mimic the casino, with features such as live scoreboards and live dealers allowing gamers to opt for the classics they want to play. By monitoring which players (signed in through a profile to play) opt for which games, casino game providers can create a profile on who plays what, which will inform decisions made by developers such as Microgaming and NetEnt. Thematic video slots take content, such as Game of Thrones, Jurassic Park, Batman, Deal or No Deal, in order to tailor to interests gamers have. Content-heavy games employ characters and iconic objects and logos from such franchises as wilds and scatters – symbols that can unlock bonuses and boost payouts in slot games, while you’ll often also hear music scores from the films as well as sound effects. For example, The Joker in Batman or one of the velociraptors in Jurassic Park. The variety of themes and modes of gameplay show the starting point that data collectors have to begin in order to find out which games work well for which particular players. Those interested in games heavy in content will need a different strategy to those interested solely in traditional play. And then these players can be targeted by the games they are more likely to want to play.
Pitfalls for Big Data
However, the GDPR (General Data Protection Regulations) that will change how data can be collected from May 2018 may affect how big data can work. Consumers will have to opt in to receive information regarding new products and services. At the moment, consumers are usually requested to opt out, so they’re part of data collection by default. This directly affects the quantity and quality of big data that can be mined. While general data will still be able to be collected seamlessly, allowing generalised gaming statistics to be developed, data collectors will have to approach the new rules and regulations laterally in order to continue using the data they are collecting in the same way. However, experts from Silicon Valley state that while GDPR may affect big data in some ways, on the whole, the data collection process will still allow for the customer experience to be bettered through big data.
The Future of Big Data and Gaming
Big data is largely quantitative in that it can identify how long is spent on a game, but can’t ascertain exactly what makes the game so fun to play. Relying solely on big data can be somewhat detrimental. To combat this, they need to be savvy and use the data as an exploratory research tool instead of a hypothesis-proving one. Plus, while quantitative statistical data is important in order to operationalise the findings, qualitative data is just as important. Players may all face the same issues that can only be deciphered if the numbers are taken into context of human interaction. So big data is most effective when used holistically to uncover both quantitative and qualitative solutions. The positive improvements that gamers will see will justify the need to collect big data and attempt to signpost future marketers – 39% of whom still don’t collect enough data – and businesses in the direction they need to go in order to use data to not only further themselves, but to improve their offering for consumers.
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