Below is an excerpt from a published interview with Scopely co-founder and CTO Ankur Bulsara.

It is very interesting to see how they predict and groom spenders, and ultimately encourage the cultivating of ‘whales’. If you’ve ever felt targeted, compelled, maybe even aggressively pursued to spend (pop-ups anyone?), this may be interesting to you.

His leadership bio can be found here: http://scopely.com/about/leaders/

The full interview can be found here: https://www.techemergence.com/techemergence-comscopely-ai-analytics-gaming/

(3:40) You had talked about just how business intelligence heavy…how instrumented the nuance of the mobile gaming environment area is—why is that?

Ankur Bulsara: The first place to start is realizing how big the mobile gaming marketplace is, so I encourage listeners to go to the Apple app store or Google play store and click on top growing, and you’ll see that maybe 90% of the apps are all games. What this tells us is that most of the things that make money on the mobile smartphone platforms are games, and you pair that with low conversion rate of payers and free to play gaming market, and it becomes critical to optimize the 2% to 3%; this is where you really need deep understanding of your users and analytics to be able to hone in on that 2% that will actually pay in the game, and then convert some of that 2% and even smaller percentage into your whales.

(5:20) On the average…you had mentioned 2% or 3 %—is this what a well-instrumented, well-orchestrated, free to play mobile game might expect? Is this baseline metrics for your company or the industry at large?

AB: This is pretty industry standard, you’re going to get about roughly 2% to 3% payer base…the thing to realize about these games is 98% are playing completely for free and having a great time, and we still have to pay for those installs, those marketing costs, those infrastructure costs, and that’s typical for the industry, that you have a very low payer conversion and an even lower whale ratio…

(6:40) This is to say people who would regularly and consistently pay for…the best features, the best abilities, people who would upon regular use consistently take you up on “the cool thing”…what constitutes whaledom?

AB: We want to keep them highly engaged, we don’t judge them by recurring payments as much as we do cumulative lifetime spend, so the whales are those people who have spent the most in your game, and it depends on the game…in some cases you may be buying acceleration to make something happen…we also run a lot of live events, and the live events—these are things like tournaments or alliance wars…and these social competitive events help to drive a lot of monetization, and it’s very reusable…instead of selling physical goods, we’re selling virtual goods, and just like any eCommerce platform, it’s up to us to keep that merchandise fresh, to create sales and opportunities to spend, to create want and desire, to create vanity and social competition…

(11:38) Talk to us a little about what were some of the initial applications of ML in this space for you guys, where you saw the ROI and it became a legitimate use of whatever (language) you’re leveraging; where did you first implement ML where it really made a difference?

AB: Taking a quick step back, the first thing we had to do—and I think this is important for any company—is we had to build a data platform…we had made the mistake before of trying to apply ML to a poorly formulated data set, and there’s an old adage in data of “garbage in garbage out”, and really until you get rid of the all the garbage coming in your data platform, it’s hard to build any kind of modeling…we’ve taken very significant strides, and I’d say now we have a quite robust and highly competitive analytics platform, compared to what’s in the commercial marketplace…and once we were able to do hindsight analysis in an accurate way, we started considering what is the predictive stuff we can do…

…one of the first use cases—that was to predict, or really estimate, lifetime value of people that we wanted to bid for. This is important because you need to calibrate your bids, and rather than make up a bid, you want to have some confidence that you are not overpaying and you want to bid as high as possible in a growth phase without bidding above your LTV…it’s really critical at first to get these games at scale, because if we don’t, you’re going to have a hard time creating enough social critical mass to make the game interesting, to create enough social competition, to create enough of a user base.

(15:26) From what I gather, you’re looking at immediate activities that these people take…have they taken the micro-actions, are they the kind of engaged users that do these things that tend to become the kinds of people with this likelihood that end up paying us X…I take it that this is what models are getting trained on.

AB: That is a more accurate description, and the other point I would make is that the other reason why we can’t wait four or five months…is the game will have evolved so much in that five months that essentially it’s a different game…this goes back to live content and frequent updates, and we try to have one release a month with a big new feature…one of the things we look for, from a data perspective, is are the new cohorts monetizing at a higher level than the old cohorts? Our product needs to improve over time, and this is why you need a little bit more machine learning to be more highly adaptable to these kinds of early signals that may be indicators of future spend.