Market Data

Game Changers Podcast: Episode 8

data.ai

data.ai's Melania Calinescu on what Next-Gen AI Means for App Publishers

Slowly but surely, machine learning (ML) is seeping into everyday life. Everyone is familiar with voice recognition, instant translation and "if you liked that, you'll like this" recommendations.

The success of data.ai is, of course, built on these technological breakthroughs. Advances in ML, data science and artificial intelligence have enabled us to build tools that help app and game publishers understand the specific actions of their users as well as broader shifts in the market.

But where can ML take data.ai customers next?

There is no one better placed to answer this question than Melania Calinescu, Head of Data Science at data.ai. Melania has been with data.ai for eight years, which makes her one of the company's longest serving employees.

In the latest edition of Game Changers, data.ai's podcast series with mobile’s top influencers and leaders, Melania talks in detail about her own data.ai journey – and looks ahead to the key innovations to come.

"In eight years, there has not been one single boring day," she says. "We never solve the same problem – or apply the same algorithm – twice. And the more the mobile device becomes central to our lives, the more interesting puzzles there are for us to solve."

This is certainly true now. In the podcast Melania describes how data.ai is evolving beyond its key offerings of estimating downloads and revenue, and describing user patterns. She explains how the extraordinary recent advances in ML are moving data.ai in four new directions namely:

  • Broadening the training data to include social media activity around apps and games
  • Going deeper into advertising – helping publishers to better optimize their ad strategies  
  • Using ML to make specific recommended actions to customers 
  • Awarding customers one 'mobile performance' score across four pillars: acquisition, engagement, sentiment and monetization. Then giving customers the ability to see which metrics they should study in order to improve.

Needless to say, Melania is confident that the above activities will improve over time – as her team gets the chance to assess the results of real-world actions and feed them back into the training data.

"We like to talk to our customers and find out what specific problems they want to solve," she says. "Being able to have full customer feedback will help us. So let's say we recommend three actions and a customer implements one. What's the success? With this information, we can calibrate our models. We can tune our models to make more successful actions for the next implementation. That's truly AI."

To tune into Episode 8 of the Game Changers Podcast please click here:

July 1, 2022

Market Data

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