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UATOPIA Panel: How Do You Spot a Potential Winner?

Luis de la Camara and Guy Bauer discussing mobile game creative testing at UATOPIA in Barcelona.

A creative win should change what your team makes next. In this UATOPIA panel on creative testing for mobile games, Luis de la Camara, ex-Rovio and King, and Guy Bauer, Sr. Director of Marketing at Playtika, discuss how to turn campaign results into repeatable creative insights.

This is the first of the UATOPIA sessions we're releasing. More go live over the next month.

Luis starts with player motivation. Using examples from Tuscany Villa and Angry Birds 2, he explains how a creative theme becomes more useful when it works across several executions. The discussion looks at the signals that distinguish a repeatable insight from a campaign that has found a particular pocket of players, and how the audience and ad network shape the creative approach.

Guy describes the process behind that learning: digging into historical campaign data, running smaller creative tests and getting feedback back to the people making the ads. His examples from Slotomania bring the conversation to a practical question for established games: how do you challenge assumptions and find fresh directions when the team already has years of results?

The panel also gets into where the two speakers differ. Luis discusses ads that stay true to player motivations while departing from literal gameplay; Guy shares why misleading ads have cost him in retention and results. From there, they explore how UA, creative and product teams work together, what creative specialists need to understand about performance, and how to decide which AI tools to build internally or bring in through partners.

What you’ll take away

  • Identify the player motivation behind a winning creative and test whether the idea can travel across executions.
  • Build a feedback loop between campaign results, creative production and product decisions.
  • Give teams room to experiment while setting clear expectations for the test and its results.
  • Weigh the opportunity cost of building AI tools internally against using external partners.

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