Friday 29 July 2016
Reading Time: 3 minutes
Unless you have been living under a rock for the last few weeks, you may have heard of Pokémon GO – the augmented reality mobile game that has swept the globe.
While you will have certainly heard about it, odds are also in-favour of you having at least downloaded and played it as it has already overtaken leading mobile apps Twitter and Tinder for active users.
It would be fair to argue that the Pokémon brand has seen a huge resurgence and much of this is down to the innovative way that they have used big data as the main ingredient of a free-to-play massively multiplayer online mobile game.
Niantic’s innovative approach in utilising big data in Pokémon GO has created an environment where the user base is perfectly happy to contribute to the data capture and perhaps just as important, it’s all in real-time.
How it works
The game itself uses the player’s physical location granted through means of GPS to generate a fantasy world full of Pokémon that the user can then interact with, ultimately capturing them with Pokéballs.
One aspect that must be credited to the game’s success is the immersion it has brought. By utilising a well thought-out data set it has turned a typically boring, non-eventful stroll through the town centre into a hot-spot for catching Pokémon, evidenced by the ‘Pokémon GO meetups’ happening across the world.
At the centre of all this is the fundamental use of big data powering the game. However, it’s not the first of its kind. Before Pokémon GO came another – Ingress, also developed by Niantic. Very similar to Pokémon GO, it encouraged users to visit real-life locations in a fantasy world, with the objective to capture ‘portals’ for the user’s team. The locations of these ‘portals’ now serve as Pokéstops and Pokémon gyms in the Pokémon GO game.
Obviously these games are massively reliant on Google Maps so it is even more interesting that Niantic is a spin-off from Google, and the founder John Hanke is the inventor of what is now known as Google Earth and the former Vice President of Product Management for Google’s ‘Geo’ division.
With access to this geographical data, Niantic had the means to create Ingress, adding real-world landmarks across the world as interactive portals within the game. Subsequently they opened this up to the user community who contributed no less than 15 million other real-life locations to add to the game as portals. As of this date Niantic has added 5 million of these.
Turning Big Data into a Game
The clever aspect of how Niantic has utilised big data in Pokémon GO can be attributed to the algorithm Niantic has designed in order to spawn Pokémon. It appears as though to ensure immersion is maximised they have created an algorithm to spawn Pokémon specific to a number of conditions such as user’s area, local climate, time and other unknown factors.
By using this combination of geo-tagging (for example water, grassy, urban areas, etc.), climate and time data the whole augmented reality experience becomes more immersive. It makes sense to see water Pokémon spawn next to sources of water, grass Pokémon spawn in large parks and even psychic and ghost Pokémon near to graveyards at night.
To work the mobile game requires the user’s GPS to be turned on and the app to be running at all times. The potential for data capture here is enormous. Niantic is able to track exactly where we go with the app open and has the potential to uncover behavioural insights such as the areas we like to frequent during the working week and weekends, the sort of data that would be the Holy Grail for marketers. There’s no doubt that this sort of behavioural insight would be majorly beneficial to companies in other industries.
The personal benefits users are receiving perhaps serve as an extra motivation for continued participation. As well as being a fun game, delivering nostalgia in the bucket-load, a US study undertaken on Pokémon GO players reveal that 43% of the users who participated in the study have lost weight whilst playing and on average players are spending two hours more per day outside than before.
What Does All of this Mean for the Future of Big Data?
The issue that many have with big data is its massive scale. Extracting the useful data for analysis still proves to be a challenge and any insight that has been acquired often is rendered obsolete because the expiration date of useful data is forever decreasing.
With Pokémon GO, the big data landscape has certainly changed. The problem with big data is that it’s mostly historical and thus has lost that relevance and ultimately is not all that useful. The emergence of Pokémon GO as an example of real-time big data capture could prove to be an essential use case for the future.
It remains to be seen how this data will be used to augment marketing and sales strategies used to target its customer base and whether or not the data will be shared with partner organisations but it may well open the door to projects of a similar kind.