THE BLOG

Big Data and Gaming: Match Made In...?

02/05/2013 13:57 BST | Updated 01/07/2013 10:12 BST
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Many of our activities today create data. Data that is collected, analysed and acted upon by an increasing array of businesses. While we may pride ourselves on our independence, in truth more and more of our activities are heavily influenced by Big Data-driven marketing.

Perhaps nowhere is this more true than in the virtual worlds created by the gaming industry.

Whether you're playing World of Warcraft or FarmVille, every click is being registered and acted upon. As Ben Goswami, Team Lead for Analytics Engineering at KIXEYE, an online game developer explains here, each click carries a wealth of information, telling game developers the time of the event, what happened before or after the click and how different clicks are correlated.

Such data gets fed into an optimisation tool that figures out how to improve the game-playing experience.

The question is, for whom is it optimised?

Social game developers such as Zynga use analytics to optimise the gaming experience such that a player will want to spend another £2.00 to generate a new weapon, add a stable to their farm and so on. Or maybe the developer will spot a trend that suggests that gamers tend to abandon the game if they die within the first two levels, so the developer tweaks the game to ensure the gamer will stick around.

Such optimisations are made to keep the gamer online, playing the game as much as possible.

Good for the gamer? Yes, in the sense that it enhances their enjoyment of the game.

Good for the developer? Definitely. The more game play the more monetisation.

When Optimisation Goes Too Far

While Goswami reports that less than 1% of data has actionable intelligence, small percentage can have a big impact on a company's bottom line...and on the lives of its customers.

Take my son, for example.

Online gaming companies depend heavily upon so-called "whales" for revenue. As one former Zynga employee revealed, 1% of Zynga's players account for 25% to 50% of its revenue. Get one of these whales on the hook and you can literally sell them a never-ending supply of virtual goods.

But what happens when the "whale" is a 13-year-old boy?

In the case of my son, he told me that one of the reasons he couldn't stop playing Gra'al, an online game, is because of the rush he felt when he'd log on and discover he had a new weapon, or that some other advancement had happened in the game. Scientists have a word for this "rush": it's called dopamine, and it's perhaps the primary motivation driving social media. There's plenty of research on the phenomenon, including this study from Harvard.

In the case of my son, it turned into a very expensive addiction, given Facebook's willingness to let him charge our family's mobile data plan to the tune of hundreds of dollars. I'd talk with him and he'd promise to stop, but he struggled. The data was conspiring against him. Finally we pulled the plug on his phone and laptop. It seemed to be the only way to block his data-driven obsession with the game.

Not that I'm placing the blame on the data-optimising shoulders of the game developer. I don't expect anyone but me to parent my kids.

Walking Big Data's Fine Line

Still, I can't help but feel that Big Data is fraught with not only a Big Promise but also a Big Peril.

The difference between trapping unsuspecting users into parting with more of their cash and optimising their experience for their good is a fine line, and one not always easy to walk. For example, Karmasphere, a Big Data analytics company, markets its Big Data offering for the social gaming market in this way:

"Segment your users by gender, age, use patterns, spending patterns and stickiness to offer them features they'll enjoy."

Sounds great, right?

Well, yes, until you consider my son. Or until you imagine any number of other industries that are almost certainly using Big Data to segment users and optimise their experience: from the easy villains like Big Tobacco or payday loan centres, to the less obviously evil fast food chains or retailers that mine data to micro-target coupons or advertisements. The businesses are simply trying to market their products more efficiently, but it's possible that data gives them an unfair advantage.

Again, this is not to suggest that enterprises should eschew Big Data strategies. Given the power of data to transform one's business, this simply isn't going to happen.

But it does suggest that as an industry we need to be careful not to overlook Big Data's pitfalls even as we chase its promise.