Transcript of a BriefingsDirect podcast on how a gaming enyterprise uses big data to gather insights into sales trends and customer wants and needs.

1. GameStop Deeply Examines its Data to Gain Both a Retail
Advantage as Well as Vast Insights into the Gaming Business
at Large
Transcript of a BriefingsDirect podcast on how a gaming enyterprise uses big data to gather
insights into sales trends and customer wants and needs.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I'm
Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this
ongoing sponsored discussion on IT innovation and how it’s making an impact
on people’s lives.
Once again, we're focusing on how companies are adapting to the new style of
IT to improve IT performance and deliver better user experiences, as well as
better business results.
This time, we're coming to you directly from the HP Big Data 2014 Conference
Gardner
in Boston. We're here the week of August 11 to learn directly from IT and business leaders alike
how big data, cloud, and converged infrastructure implementations are supporting their goals.
Our next innovation case study interview highlights how GameStop, based in Grapevine, Texas
is using big data to improve how it conducts its business and serve its customers. To learn more
about how they deploy big data and use the analytics, we are joined by John Crossen, the Vertical
Lead at GameStop. Welcome, John.
John Crossen: Thank you for having me.
Gardner: My pleasure. Tell us a little bit about GameStop. Most people are
probably familiar with the retail outlets that they see, where you can buy, rent,
trade games, and learn more about games. Why is big data important to your
organization?
Crossen: We wanted to get a better idea of who our customers are, how we can
better serve our customers and what types of needs they may have. With prior
reporting, we would get good overall views of here’s how the company is doing
or here’s how a particular game series is selling, but we weren’t able to tie that
Crossen
to activities of individual customers and possible future activity of future customers, using more
of a traditional SQL-based platform that would just deliver flat reports.

2. So, our goal was to get s more 360-degree view of our customer and we realized pretty quickly
that, using our existing toolsets and methodologies, that wasn’t going to be possible. That’s
where Vertica ended up coming into play to drive us in that direction.
Gardner: Just so we have a sense of this scale here, how many retail outlets does GameStop
support and where are you located?
Crossen: We're international. There are approximately 4,200 stores in the US and another 2,200
international.
Gardner: And in terms of the type of data that you are acquiring, is this all internal data or do
you go to external data sources and how do you to bring that together?
Internal data
Crossen: It's primarily internal data. We get data from our website. We have the PowerUp
Rewards program that customers can choose to join, and we have data from individual cash
registers and all those stores.
Gardner: I know from experience in my own family that gaming is a very fast-moving industry.
We’ve gone from different platforms to different game types and different
technologies when we're interacting with the games.
It's a very dynamic changeable landscape for the users, as well as, of course, the
providers of games. You are sort of in the middle. You're right between the users
and the vendors. You must be very important to the whole ecosystem?
Crossen: Most definitely, and there aren’t really many game retailers left anymore. GameStop is
certainly the preeminent one. So a lot of customers come not just to purchase a game, but get
information from store associates. We have Game Informer Magazine that people like to read
and we have content on the website as well.
Gardner: Now that you know where to get the data and you have the data, how big is it? How
difficult is it to manage? Are you looking for real-time or batch? How do you then move forward
from that data to some business outcome?
Crossen: It’s primarily batch at this point. The registers close at night, and we get data from
registers and loads that into Vertica. When we started approximately two years ago, we didn't
have a single byte in Vertica. Now, we have pretty close to 24 terabytes of data. It's primarily
customer data on individual customers, as well Weblogs or mobile application data.
Gardner: I should think that when you analyze which games are being bought, which ones are
being traded, which ones are price-sensitive and move at a certain price or not, you're really at

3. the vanguard of knowing the trends in the gaming industry even perhaps before anyone else.
How has that worked for you, and what are you finding?
Crossen: A lot of it is just based on determining who is likely to buy which series of games. So
you won't market the next Call of Duty 3 or something like that to somebody who's buying your
children's games. We're not going to ask people buy Call of Duty 3, rather than My Little Pony 6.
The interesting thing, at least with games and video game systems, is that when we sell them
new, there's no price movement. Every game is the same price in any store. So we have to rely on
other things like customer service and getting information to the customer to drive game sales.
Used games are a bit of a different story.
Gardner: Now back to Vertica. Given that you've been using this for a few years and you have
such a substantial data lake, what is it about Vertica that works for you? What are learning here at
the conference that intrigues you about the future?
Quick reports
Crossen: The initial push with Vertica was just to get reports fast. We had processes that
literally took a day to run to accumulate data. Now, in Vertica, we can pull that same data out in
five minutes. I think that if we spend a little bit more time, we could probably get it faster than
half of that.
So the first big push was just speed. The second wave after that was bringing in data sources that
were unattainable before, like web-click data, a tremendous amount of data, loading that into
SQL, and then being able to query it out of SQL. This wasn't doable before, and it’s made it do
that. At first, it was faster data, then acquiring new data and finding different ways to tie different
data elements together that we haven’t done before.
Gardner: How about visualization of these reports? How do you serve up those reports and do
you make your inference and analytics outputs available to all your employees? How do you
distribute it? Is there sort of an innovation curve that you're following in terms of what they do
with that data?
Crossen: As far as a platform, we use Tableau as our visualization tool. We’ve used a kind of an
ad-hoc environment to write direct SQL queries to pull data out, but Tableau serves the primary
tool.
Gardner: In that data input area, what integration technologies are you interested in? What
would you like to see Vertica do differently? Are you happy with the way SQL, Vertica, Hadoop,
and other technologies are coming together? Where would you like to see that go?

4. Crossen: A lot of our source systems are either SQL-server based or just flat files. For flat files,
we use the Copy Command to bring data, and that’s very fast. With Vertica 7, they released the
Microsoft SQL Connector.
So we're able to use our existing SQL Server Integration Services (SSIS) data flows and change
the output from another SQL table to direct me into Vertica. It uses the Copy Command under
the covers and that’s been a major improvement. Before that, we had to stage the data
somewhere else and then use the Copy Command to bring it in or try to use Open Database
Connectivity (ODBC) to bring it in, which wasn’t very efficient.
Gardner: Any interest in Hadoop? Is that something on your horizon?
Crossen: We're looking at that.
20/20 hindsight
Gardner: How about words of wisdom from your 20/20 hindsight? Others are also thinking
about moving from a standard relational database environment towards big data stores for
analytics and speed and velocity of their reports. Any advice you might offer organizations as
they're making that transition, now that you’ve done it?
Crossen: Just to better understand how a column-store database works, and how that's different
from a traditional row-based database. It's a different mindset, everything from how you are
going to lay out data modeling.
For example, in a row database you would tend to freak out if you had a 700-column table. In the
column stores, that doesn’t really matter. So just to get in the right mindset of here’s how a
column-store database works, and not try to duplicate row-based system in the column-store
system.
Gardner: Great. I am afraid we’ll have to leave it there. I’d like to thank our guest, John
Crossen, the Vertica Lead at GameStop in Grapevine, Texas. I appreciate your input.
Crossen: Thank you.
Gardner: And also thank to our audience for joining us for this special new style of IT
discussion, coming to you directly from the HP Big Data 2014 Conference in Boston.
I'm Dana Gardner; Principal Analyst at Interarbor Solutions, your host for this ongoing series of
HP sponsored discussions. Thanks again for listening, and come back next time.
Listen to the podcast. Find it on iTunes. Sponsor: HP

5. Transcript of a BriefingsDirect podcast on how a gaming enyterprise uses big data to gather
insights into sales trends and customer wants and needs. Copyright Interarbor Solutions, LLC,
2005-2014. All rights reserved.
You may also be interested in:
•
How Waste Management Builds a Powerful Services Contiunuum Across Operations,
Infrastructure, Development, and IT Processes
•
GSN Games hits top prize using big data to uncover deep insights into gamer preferences
•
Hybrid cloud models demand more infrastructure standardization, says global service
provider Steria
•
Service providers gain new levels of actionable customer intelligence from big data
analytics
•
How UK data solutions developer Systems Mechanics uses HP Vertica for BI, streaming
and data analysis
•
Advanced cloud service automation eases application delivery for global service provider
NNIT
•
HP network management heightens performance while reducing total costs for Nordic
telco TDC
•
How Capgemini's UK financial services unit helps clients manage risk using big data
analysis
•
Perfecto Mobile goes to cloud-based testing so developers can build the best apps faster
•
Software security pays off: How Heartland Payment Systems gains steep ROI via
software assurance tools and methods
•
HP ART documentation and readiness tools bring better user experiences to Nordic IT
solutions provider EVRY