Changing the way you manage a Free-to-Play game
Who sees what - Gondola chooses the best offer for a player.
Example: Determine if a new user should see the “Starter Pack” for $0.99 or the “Intro Offer” for $2.99 as the first offer in the game. Note: Gondola does not change $$ or virtual currency amounts for offers, but rather decides which offer from a group of offers to show to a player.
For in-game stores, Gondola chooses the most relevant items to show to a player.
Example: Your game offers 9 different “Gem Packs” at price points between $2.99 and $199.99. Gondola will compose the right line up of 5 offers in that range for every player cohort.
Gondola determines virtual currency quantities for offers and promotions.
Example: For a “Christmas Pack” that costs $9.99, Gondola determines an optimized amount of gold and an optimized amount of coins based on an optimization range set by the product manager.
Gondola determines the quantity of virtual currency that a player receives in exchange for watching a rewarded video ad.
Example: Watching a 30-second video ad rewards the player with an amount of gems based on an optimization range set by the product manager.
Individual players are assigned to cluster based on a multitude of dimensions. Players move between clusters as they mature in the game. The boundaries of each cluster are constantly reevaluated by Gondola’s machine learning algorithm. The result is that every player is presented with the offers that are most relevant to him.
Game developers with very active player communities and message boards ask us what the impact of Gondola’s optimization on their community will be. Generally, community backlash is not an issue when using Gondola. However, some games require a more conservative approach, and in these situations we recommend to focus exclusively on Target Optimizations (“who sees what”) and clearly communicate to your players that they are receiving exclusive offers specifically chosen just for them.