Transform 2021

Elevate your enterprise data technology and strategy.

July 12-16

Amazon today announced the general availability of Redshift ML, which lets customers use SQL to query and combine structured and semi-structured data across data warehouses, operational databases, and data lakes. The company says that Redshift ML can be used to create, train, and deploy machine learning models directly from an Amazon Redshift instance.

In the past, Amazon Web Services (AWS) customers who wanted to process data from Amazon Redshift to train an AI model would have to export the data to an Amazon Simple Storage Service (Amazon S3) bucket and configure and start training. This required many different skills and usually more than one person to complete, raising the barrier to entry for enterprises looking to forecast revenue, predict customer churn, detect anomalies, and more.

With Redshift ML, customers can create a model using an SQL query to specify training data and the output value they want to predict. For example, to create a model that predicts the success rate of marketing activities, a customer might define their inputs by selecting database columns that include customer profiles and results from previous marketing campaigns. After running an SQL command, Redshift ML exports the data from Amazon Redshift to an S3 bucket and calls Amazon SageMaker Autopilot to prepare the data, select an algorithm, and apply the algorithm for model training. Customers can optionally select the algorithm to use if they opt not to defer to SageMaker Autopilot.

Redshift ML handles all of the interactions between Amazon Redshift, S3, and SageMaker, including the steps involved in training. When the model has been trained, Redshift ML uses Amazon SageMaker Neo to optimize the model for deployment and makes it available as a SQL function. Customers can use the SQL function to apply the model to their data in queries, reports, and dashboards.

Redshift ML is available today in the following AWS regions:

• U.S. East (Ohio)
• U.S. East (North Virginia)
• U.S. West (Oregon)
• U.S. West (San Francisco)
• Canada (Central)
• Europe (Frankfurt)
• Europe (Ireland)
• Europe (Paris)
• Europe (Stockholm)
• Asia Pacific (Hong Kong)
• Asia Pacific (Tokyo)
• Asia Pacific (Singapore)
• Asia Pacific (Sydney)
• South America (São Paulo)

With Redshift ML, customers only pay only for what they use. When training a new model, they pay for the Amazon SageMaker Autopilot and S3 resources used by Redshift ML, and when making predictions, there’s no additional cost for models imported into their Amazon Redshift cluster. Redshift ML also allows customers to use existing Amazon SageMaker endpoints for inference. In that case, the usual SageMaker pricing for real-time inference applies.

Amazon Redshift, which launched in preview in 2012 and in general availability a year later, is based on an older version of the open source relational database management system PostgreSQL 8.0.2. According to Cloud Data Warehouse report published by Forrester in Q4 2018, Amazon Redshift has the largest Cloud data warehouse deployments, with more than 6,500 deployments to date.

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How do you become one of the big winners in the booming games industry? The first day of the all-digital GamesBeat Summit, Driving Game Growth,  hosted in partnership between GamesBeat and Facebook, tackled that exact question.

During the session “Winning with Rewarded Video Across Genres,” two leaders in the space, Rose Agozzino from Ludia and Sarah Ketir from Product Madness joined Heath Schindler, strategic partner manager at Facebook Audience Network. They spoke about best practices in rewarded video, why rewarded video works, and why rewarded video is being adopted by mobile gaming developers across verticals.

Mobile in-game ad revenue is a booming business, Schindler said. “According to Omdia, mobile game developers generated $43 billion in revenue in 2020,” he commented. “And despite the upcoming challenges around IDFA, it’s expected to grow 118% percent to be a $94 billion business in 2025.”

That’s a huge opportunity, and it’s growing faster than IAP, he adds. One of the main reasons  developers are investing more in in-app advertising is because users are open to receiving in-game ads. According to a recent study by 2CV, 79% of players are happy with the ad-supported model.

Even though rewarded video is overtaking the IAP model of monetization, some developers weren’t initially on board.

“Because we’re primarily IAP-driven, we were among those skeptics — how is this going to impact IAP?” Agozzino said. “But slowly we’ve added in rewarded video ads and we’re not looking back.”

“If you think about just IAP, you’re missing a huge opportunity,” Ketir said. “More than 90% of users aren’t payers. This is basically where we come from with Product Madness. We’re doing well as a company, but how can we monetize those non-payers?”

When Ketir joined Product Madness, they had just gone live with rewarded video, without much money behind it, just to see if it would work. Her mission was to scale the strategy.

“There were lots of fears, as you can imagine, because for an IAP company, it can be stressful to feel that there could be a cannibalization effect,” Ketir says. “My role was to do lots of A/B testing and see how it goes. Obviously it became a good business.”

They chose rewarded video because as a user-initiated ad unit, it’s less intrusive than interstitials, for example, she said. Also, on top of that, obviously the user can opt in or opt out, and they get an incentive, which is something that helps in terms of engagement and retention within the game.

“When you work in ad monetization and you work closely with product, testing rewarded video is the best and easiest way to start your ad monetization journey,” said Ketir. “It’s less risky, because you can see your engagement rate within the ad.”

At Ludia, developers implemented a rewarded video strategy because they believe it provides a clear benefit for the player’s time.

“It’s important that when we create these placements, and the reward that goes with it, that the players not only understand that there’s an exchange happening, but that they see the value in the exchange,” Agozzino said. “If they don’t find that the reward is worth their time, they’re less likely to come back and repeat engagement with these ads.”

Across their titles, they’ve found that if they change a provider, or make some changes to the waterfall, the support team will start to get emails from players complaining because they don’t have any ads.

“They get mad, because the ads are such an intrinsic part of how they play, so if we take it away from them, they see it as a problem,” she said. “So don’t fear that your players are going to be mad, because a lot of times, once they understand the value, they’re going to build it into how they play.”

Another reason in-app ads continue to grow exponentially is because typically fewer than 5% of players make in-app purchases.

“Depending on that 5% is risky, and more and more publishers are realizing that,” Schindler said. “They’re tired of leaving money on the table, and I don’t blame them.”

Additionally, many publishers have found that after introducing rewarded video, their in-app purchases actually increased. That’s because rewarded video can demonstrate the value of an in-app purchase and give the user a taste of what can happen once they start making purchases.

Some developers still have concerns around IAP cannibalization, but it’s possible to balance IAP and ad monetization, says Agozzino.

“Since we started integrating rewarded video, we made sure that we had limits in place, so players couldn’t go ahead and run through thousands of ads a day and cannibalize the IAP,” she said. “What’s interesting is that once we really started to integrate RV a bit better, we’ve seen some great KPIs.”

For example, in one of their titles, they’ve seen a 4X better retention rate from players who engage in ads versus players who’ve never watched an ad. They’ve also noticed that players who watch ads are six times more likely to make a purchase within 24 hours of the ad watch.

“This lends to the theory that if players get a sample of what it’s like to be a paying user, they’re more likely to become a paying user,” she adds.

“What we need to think about is, even if there is a bit of cannibalization, if overall the revenue coming from ad monetization is above what that cannibalization loses, it’s worth it,” Ketir said.