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How big data powers GameStop to gain retail advantage and deep insights into its markets

Published on 20 July 15
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The next BriefingsDirect analytics innovation case study interview highlights how GameStop, based in Grapevine, Texas, uses big data to improve how it conducts its business and better serve its customers.

By accessing data sources that were unattainable before and pulling that data out into reports in just a few minutes across nationally distributed retail outlets, GameStop more deeply examines how its campaigns and products are performing.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy.


To learn more about how they deploy big data and use the resulting analytics, BriefingsDirect sat down with John Crossen, Data Warehouse Lead at GameStop. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: 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 hereâs how the company is doing or hereâs how a particular game series is selling, but we werenât able to tie that 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.

How big data powers GameStop to gain retail advantage and deep insights into its markets - Image 1

Crossen

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 wasnât going to be possible. Thatâs 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.

How big data powers GameStop to gain retail advantage and deep insights into its markets - Image 2Gardner: I know from experience in my own family that gaming is a very fast-moving industry. Weâve quickly 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 arenât 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: Itâs primarily batch at this point. The registers close at night, and we get data from registers and loads that into HP 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.

Become a member of myVertica today
Register now
Access the FREE HP Vertica Community Edition

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 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 are 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 HP 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.

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 itâs 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 havenât 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?

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.

Crossen: As far as a platform, we use Tableau as our visualization tool. Weâve 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 HP 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?

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 thatâs 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 thatâs 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 wasnât very efficient.

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 youâve 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.

Become a member of myVertica today
Register now
Access the FREE HP Vertica Community Edition

For example, in a row database you would tend to freak out if you had a 700-column table. In the column stores, that doesnât really matter. So just to get in the right mindset of hereâs how a column-store database works, and not try to duplicate row-based system in the column-store system.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy. Sponsor: HP.








The next BriefingsDirect analytics innovation case study interview highlights how GameStop, based in Grapevine, Texas, uses big data to improve how it conducts its business and better serve its customers.

By accessing data sources that were unattainable before and pulling that data out into reports in just a few minutes across nationally distributed retail outlets, GameStop more deeply examines how its campaigns and products are performing.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy.

To learn more about how they deploy big data and use the resulting analytics, BriefingsDirect sat down with John Crossen, Data Warehouse Lead at GameStop. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: 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 hereâs how the company is doing or hereâs how a particular game series is selling, but we werenât able to tie that 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.

How big data powers GameStop to gain retail advantage and deep insights into its markets - Image 1


Crossen

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 wasnât going to be possible. Thatâs 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.

How big data powers GameStop to gain retail advantage and deep insights into its markets - Image 2
Gardner: I know from experience in my own family that gaming is a very fast-moving industry. Weâve quickly 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 arenât 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: Itâs primarily batch at this point. The registers close at night, and we get data from registers and loads that into HP 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.

Become a member of myVertica today
Register now
Access the FREE HP Vertica Community Edition


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 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 are 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 HP 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.

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 itâs 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 havenât 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?

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.

Crossen: As far as a platform, we use Tableau as our visualization tool. Weâve 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 HP 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?

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 thatâs 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 thatâs 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 wasnât very efficient.

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 youâve 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.

Become a member of myVertica today
Register now
Access the FREE HP Vertica Community Edition


For example, in a row database you would tend to freak out if you had a 700-column table. In the column stores, that doesnât really matter. So just to get in the right mindset of hereâs how a column-store database works, and not try to duplicate row-based system in the column-store system.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy. Sponsor: HP.

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