GiGse Launchpad winners reflect on a landmark year and why ‘happy people spend money’

GiGse Launchpad winners reflect on a landmark year and why ‘happy people spend money’

Thursday, March 29, 2018 Posted by Joanna Mapes
Dr. Jason Fiege and Dr. Stasi Baran, co-founders of the artificial intelligence-based algorithms and software specialists, nQube

Ahead of their presentation at this year’s GiGse, (The Biltmore Hotel, Miami, May 31 – June 1), co-founders of the artificial intelligence-based algorithms and software specialists nQube, Dr. Jason Fiege and Dr. Stasi Baran, took the time to discuss their burgeoning business, why they’ve used their expertise to target the gambling sector, and the power of creating a positive experience.

Can you outline what has happened to the business in the 12-months since you won the Launchpad competition at GiGse 2017?

The GiGse Launchpad competition was pivotal for us as a company. Prior to the Launchpad, we were working entirely with mathematical models of slot floors based on artificial data. This approach is normal – with any kind of data modelling problem, one usually starts with theories and artificial data to refine ideas and test methods. But that can only take you so far, and eventually, real data is needed to make progress. Exposure from GiGse allowed us to obtain our first real data sets, which we used to challenge our models. Additionally, the Launchpad exposure really helped us meet the right people. It allowed us to connect with casino properties and collaborators and we even met the first participant of the trial program on the plane as we travelled home from GiGse!

How important is it for the industry to have events such as the GiGse Launchpad?

The casino industry is shifting towards technology driven solutions, and events like the GiGse Launchpad are a perfect way for the industry to interact with innovators, and to learn about new technologies that they might miss out on otherwise. Likewise, they give a voice to small, innovative companies, and provide a way for these companies to get much needed feedback from the gaming industry.

What topics/issues will you be covering at GiGse 2018 in Miami?

We will review how our business and technology has changed since the LaunchPad. We will also discuss some of the interesting and unexpected discoveries that working with AI has allowed us to make and share some valuable insights into how players interact with the casino floor. How can we best use data to determine the optimal mix of machines for the slot floor? What spatial attributes of a casino (e.g. table games, restaurants, bars, entrances, etc.) influence performance of nearby machines? How do players cluster on the floor? What is the benefit of having open sightlines on the floor?
 
Additionally we plan to talk quite a bit about segmentation and will use our AI-guided evolutionary computing platform to demonstrate an effective method to find the best number of segments and optimal segment boundaries, while also optimizing the performance of a marketing campaign. We believe that there are also potential applications of our technology to iGaming. We will discuss some ideas, and we look forward to feedback from the audience.

Why gambling – what made you target this sector?

There were three things that were particularly attractive about the casino industry. Firstly, the incredibly vast and clean data sets collected by this industry, which are any scientist’s dream. Secondly, the significant financial opportunities that are available in this industry; and thirdly the slot floor optimization problem that we identified is a perfect fit for the AI-guided optimization technology that nQube had already invented.
 
The opportunity to have fun at work was also very attractive. We get to do science in an industry that is centered around people having fun. What could be better than that?

What other verticals are you active in and is there a disconnect with gambling in any way?

nQube is also involved in AI-guided optimization of trading strategies, primarily for futures markets. We offer a B2B algorithmic trading service to financial companies engaged in trading. This service utilizes the same underlying AI technology as our work optimizing slot floors, although the mathematical models are quite different.

You use the phrase ‘Happy people spend a lot of money’ could that be a mantra or motto for the gambling industry?

Absolutely – It’s all about creating a positive experience. One way to think about our technology is that we are optimizing slot floors to match player preferences. A well-matched floor improves the gaming experience for players, and happy players spend money. Everyone wins.

It’s a big transition from the Science Lab to the Casino Floor - have you had to negotiate a cultural shift and how have you found it?

There are many cultural differences between academia and the other industries we work in, and the casino industry. The casino industry has been extremely positive and welcoming. When we started in this industry, we were completely open about the fact that we had powerful tools to offer, but lacked domain knowledge, and we’re still learning every day. Along the way, we met a number of key people who became very excited about our technology, and they helped us to make this transition. Notably, we met several of these key people as a direct result of the GiGse Launchpad.

Gambling has a difficult reputation with some observers, is this something you considered and how did you/have you responded?

Gambling can be very polarizing to people. We have occasionally experienced negativity when explaining what we do to people outside of the industry. We respond with the positive side – gambling fuels state economies, it funds tribal communities, and in Canada (where we’re from) it helps to fill government coffers. We are sensitive to the responsible gambling issue, and once we are more established, we would like to develop techniques to identify and mitigate early-stage problem gambling behaviors, using the same data that we use for slot floor optimization.

Based on your field trials, what percentage of uplift from drop can a typical casino expect by utilizing your product?

We’ve been modelling data sets provided to us by several casino operators, but our solution hasn’t been out in the field long enough to measure actual uplift results. However, our model fits the data to within 1.5% mean error without machine segmentation, and well under 1% error when machines are segmented into types, so we are confident that the model is working extremely well. The same model shows that it is relatively easy to achieve an uplift in win in excess of 10%, by swapping out or moving a small fraction of the machines. Even higher uplifts can be obtained by swapping out or moving a greater number of machines.

Putting North American gaming under the microscope where do you see the big challenges and the big opportunities occurring?

We have started in this industry just as it is experiencing a major shift towards innovation – driven by both forward thinking and necessity. Because this shift is current, we encounter operators who are sceptical (the challenges) when it comes to new technology almost as often as those who are excited about it (the opportunities). The industry in general wants and needs innovation, but it can be challenging to break into gaming. The difference in culture between the casino industry and innovators outside of the casino industry, can make it difficult for small companies to gain traction. In general, the big opportunities are in insights from the “big data” collected by casinos. Data science provides the opportunity to maximize operational efficiencies, maximize casino floor performance, and tune casinos to suit the entertainment preferences of a changing demographic. The answers are already there in the data. It's just a matter of creating the tools needed to find them.

For more information on the 2018 edition of GiGse, which has been co-located with Juegos Miami, visit: www.gigse.com.

The full interview with nQube, co-founders Dr. Jason Fiege and Dr. Stasi Baran can be accessed here.

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