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Big Data Is a Game Changer for GSN Games
GSN Games hits top prize using big data to uncover deep insights into gamer preferences
By: Dana Gardner
Sep. 11, 2014 12:00 PM
It's a shame when the data analysis providers inside a company get the cold shoulder from the business leaders because the data keeps proving the status quo wrong, or contradicts the conventional corporate wisdom.
Fortunately for GSN Games in San Francisco, there's no such culture clash there. "The real thing that's helped us get to the point we are is a culture where everybody is open to being wrong -- and open to being proven wrong by the data," says Portman Wills, Vice President of Data at GSN Games.
"One of the things we use data for is to challenge all of our assumptions about our own products and our own businesses, says Wills. "It's really gotten to a point where it's almost religious in our company. The moment two people start debating what should or shouldn't happen, they say, 'Well let's just let the data decide.' That's been a core change not just for us, but for the game industry as a whole."
How did GSN Games get to the point where the data usually wins? It took a blazing fast data warehouse of 1.3 trillion rows that consumes, stores and produces analysis from some 110 million registered game-players in near real time. The next BriefingsDirect podcast focuses on just how GSN Games exploits such big data to effectively uncover game-changing entertainment trends for their audience. Oh, and it changes corporate cultures, too.
The discussion, at the recent HP Discover conference in Barcelona, is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Wills: GSN started as a cable network in the U.S. We’re distributed in 80 million households as the Game Show Network, and then we also have a digital wing that produces casual and social games on Facebook, web, tablets, and mobile. That division has 110 million registered game-players. My team takes data from all over those worlds, throws them into a big data warehouse, and starts trying to find trends and insights for both our TV audience and our online game-players.
In terms of the games, which is really where the growth is, our core demographic is older females, believe it or not, who love playing casual games. We skew more in the 55-plus age range, and we have players from all over the world.
Gardner: The word “games” means a lot of different things to a lot of people. We’re talking about a heritage of network television games back in the ’60s and ’70s that have led us to what is now your organization. But what sort of newer games are we talking about, and what proportion of them are online games, versus more of the passive watching like that on a cable or other media outlets?
Wills: Originally, when our games division started as a branch of GSN, it was companion games to Wheel of Fortune, Minute to Win It, whatever the hot game show was. That's still a part of it, but the growth in the last few years has been in social games on Facebook, where a lot of our games are more casual titles and have nothing to do with the game shows -- tile-matching games or solitaire games, for example.
Then, in the last year or year-and-a-half for us, like everyone else, there’s been this explosion in mobile. So it’s iPad, Android, and iPhone games, and there we have the solitaires and the tile matching, too.
Increasingly, a lot of our success and growth has come from virtual casino games. People are playing Bingo, video poker, even slots, virtual slots. We have this title called GSN Casino. That’s an umbrella app with a lot of mini games that are casino-themed, and that one has really just exploded really in the last six months. It's a long way from the Point A of Family Feud reruns to the Point Z of virtual slot machines, but hopefully you can see how we got there.
Gardner: It seems like a long distance, but it’s been also a fairly short amount of time. It wasn't that long ago that the information you might have in your audience came through Nielsen for passive audiences, and you had basically a one- or two-dimension view of that individual, based on the estimate of what time was devoted to a show. But now, with the mobile devices in particular, you have a plethora of data.
Tell us about the types of data that you can get, and what volumes are we talking about.
Wills: Let’s take mobile, because I think it's easy to grok. Everything about the device is exposed to us. The fact that you’re playing on an iPad Mini Retina versus an iPad 1 tells us a lot about you, whether you know it or not.
Then, a lot of our users sign-in via Facebook, which is another vector for information. If you sign-in via Facebook, Facebook provides us your age range, gender, some granular location information. For every player, we get between 40 and 50 dimensions of data about that player or about that device.
That’s one bucket. But the actual gameplay is another whole bucket. What games do you choose to play in our catalog? How long do you play them? What time of day do you play them? Those start to classify users into various buckets -- from the casual commute player, who plays for 15 minutes every morning and afternoon, to the hard-core player who spends 8 to 10 hours a day, believe it or not, playing our games on their mobile devices.
At that point, and this is a little bit of a pet peeve of mine, mobile doesn’t necessarily mean mobile, like out and about. A lot of our players are on their iPad, sitting on the couch in their home.
It’s not mobility. They’re not using 3G. They’re not using augmented reality. It’s just a device that happens to be a very convenient device for playing games. So it’s much more of a laptop replacement than any sort of mobile thing. That’s sort of a side track.
We collect all of this data, and it’s a fair amount. Right now, we’re generating about 900 million events per day across all of our players. That’s all streamed into our HP Vertica data warehouse, and there are a few tables, event time series tables, that we put the stuff into. A small table for us would be a few hundred billion records, and a large table, as I said, is 1.3 trillion records right now.
So the scale is big for us. I know that for other companies that seems like peanuts. It’s funny how big data is so broad. What’s big to one person is tiny to someone else, but this is the world that we’re dealing in right now.
We have 110 million players. Thankfully, not all of them are active at one time. That would be really big data. But we will have about 20 million at any given time in peak time playing concurrently. That’s a little bit about the numbers in our data warehouse.
Gardner: Understanding your audience through this data is something fairly new. Before, you couldn’t get this amount of data. Now that you have it, what is it able to do for you? Are you crafting new games based on your findings? Are you finding information that you can deliver back to a marketer or advertiser that links them to the audience better? There must be many things you can do.
Wills: First of all, we don’t do any advertising in our mobile games. So that’s one piece that we’re not doing, although I know others are. But there are two broad buckets in which we use data. The first is that we run a lot of the A/B tests, experiments. All of our games are constantly being multivariate tested with different versions of that same game in the field.
We run 20 to 40 tests per week. As an example, we have a Wheel of Fortune game that we recently released, and there was all this debate about the difficulty of the puzzles. How hard should the puzzles be? Should they be very obscure pieces of Eastern literature, mainstream pop culture, or even easier?
So, we tested different levels of difficulty. Some players got the easy, some players got the medium, and some players got the hard ones. We can measure the return rate, the session duration, and the monetization for people who buy power-ups, and we see which level of difficulty performs the best. In the first test of easy, medium, hard, easy overwhelmingly did the best.
So we generated a whole bunch of new puzzles that were even easier than were the previous easy ones and tested that against what was now the control level. The easier puzzles won again. So we generated a whole new set of puzzles that were absurdly easy. We were trying to prove the point that if we gave Wheel of Fortune puzzles that are four-letter words like “bird” and “cups,” nobody would enjoy playing something that simplistic.
Well it turns that they do -- surprise, surprise -- and so that’s how we evolved into a version of Wheel of Fortune that, compared to the game show, looks very different, but it’s actually what customers want. It’s what players want. They want to relax and solve simple puzzles like “door.”
Gardner: So Vertica analysis determined that everyone is a winner on GSN, but you’re able to do real-time focus-group types of activities. The data -- because it's so fast, because there is so much information available and you can deal with it so quickly -- means that you’re able to tune your games to the audience virtually overnight.
Wills: Hopefully faster than overnight. Overnight is a little too slow these days. We push twice a day both to our platform code and updates to all of our games in the morning around 11 a.m and in the afternoon around 3:30. Each one of those releases is based on the data that came from the prior release.
So we're constantly evolving these games. I want to go back to your previous question, because I only got to talk about one bucket, which is this experimentation. The other bucket is using the usage patterns that customers have to evolve our product in ways that aren’t necessarily structured around an A/B test.
We thought when we launched our iPhone app that there would be a lot of commuting usage. We had in our head this hypothetical bus player, who plays on the bus in the morning. And so we thought we would build all the stuff around daily patterns. We built this daily return bonus that you can do in the morning and then again in the evening.
The data showed us that that really was only a tiny fraction of our players. There were, in fact, very few players who had this bimodal, morning and evening usage pattern. Most people didn't play at all until after dinner and then they would play a lot, sometimes even binge from 7 p.m. until 2 a.m. on games.
That was an area where we didn't even set up an experiment. We just had false assumptions about our player base. And that happens a surprising amount of the time. We all -- especially the game-design team and people who spent their careers designing video games -- have assumptions about their audience that half the time are just wrong. One of the things we use data for is to challenge all of our assumptions about our own products and our own businesses.
It's really gotten to a point where it's almost religious in our company. The moment two people start debating what should or shouldn't happen, they say, “Well let's just let the data decide.” That's been a core change not just for us, but for the game industry as a whole.
Because we’re here in Spain, a quick tidbit that we uncovered recently is that our main time-frame in every country on Earth, when people play games, is 7 p.m. to 11 p.m., except in Spain where it’s 1 p.m. to 3 p.m. -- siesta time. That’s just one of the examples of how we use big data to use discover insights about our players and our audiences worldwide.
Understanding the audience
Gardner: I have to imagine that the data that led you to that inference in Spain was something other than what we might consider typical structured data. How did the different data brought together allow you to understand your audience better?
Wills: We use this product from HP called Vertica, which is just a tremendous data warehouse, that lets us throw every single click, touch, or swipe in all of our games into a big table. By big, I mean right now it’s I think 1.3 trillion rows. We keep saying that we should really archive this thing. Then, we say we’ll archive it when it slows down, and then it just never slows down, so we have yet to archive it.
We put all of the click stream data in there. The traditional joins, schemas, and all of that don’t really have to happen because we have one table with all of the interactions. You have the device, the country, the player, all these attributes. It’s a very wide table. So if you want to do things like ask what is the usage in five-minute slices by country, it’s a simple SQL query, and you get your results.
Gardner: What you’re describing is very much desired by a lot of types of businesses through understanding a massive amount of data from their audience, to be able to react quickly to that, and then to stop guessing about products and pricing and distribution and logistics and supply chain and be driven purely by the data. You’re a really interesting harbinger of things to come.
Portman, tell me little bit about the process by which you were able to do this. Did you have an older data warehouse? What did you use before, and how did you make a transition to HP Vertica?
Wills: When we started the social mobile business three years ago, we were on MySQL, which we are still on for our transactional load. We have three data centers around the world. When people are playing our games, it’s recording, reading, and writing 125,000 transactions per second, and that MySQL, sharded out, works great for that.
When you want to look at your entire player base and do a cross-shard query, we found that MySQL really fell down. Our original Vertica proof of concept (POC) was just to replace these A/B test queries, which have to look across the entire population.
So in comes Vertica. We set up a single node, a Vertica data warehouse. We pull in a year's worth of data, and the same query to synthesize these sessions ran in 800 milliseconds.
So the thing that took 24 hours, which is 86,400 seconds, ran in less than one second. By the way, that 24-hour query was running across dozens of machines, and this Vertica query was running on a single server of commodity hardware.
That's when we really became believers in the power of the column store and column-oriented data warehouses. From the small beginning of just one simple query, it’s now expanded -- and pretty much our whole business runs on top of HP Vertica on the data warehouse side.
Gardner: As I said, I think GSN Games is a really harbinger of what a lot of other companies in many different vertical industries will be seeking. Looking back, if you had to do it again, what might you have done differently or what suggestions might you have for others who would like to be able to do what you are doing?
Wills: I definitely wish that we had switched to a column store sooner. I think the reason that we've been so successful at this is because of our game design team, which was so open to using data.
I’ve heard hard stories from other companies where they want to use a data-driven approach, and there's just a lot of cultural inertia and push back against doing that. It's hard to be consistently proven wrong in your job, which is always what happens when you rely on data.
The real thing that's helped us get to the point we are in is a culture and a company where everybody is open to being wrong -- and open to being proven wrong by the data, which I am very thankful for.
Gardner: Well, it's good to be data-driven, and I think you should feel good being responsible for making 110 million people feel good about themselves every day.
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