Changing the way you manage a free-to-play game
Who sees what? Gondola chooses the best offer for each player.
Example: Determine if a new user should see the $0.99 “Starter Pack” or the $2.99 “Intro Offer” as the first offer in the game. Gondola selects the optimal offer for each player from an existing inventory. Gondola does not price identical offers differently for each player.
For in-game stores, Gondola chooses the most relevant items to show to a player.
Example: Your game offers 12 different “Gem Packs” at price points between $2.99 and $199.99. Within that range, Gondola composes the optimal line up of 6 offers 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 diamonds and an optimized amount of coins based on a range set by the product manager.
Gondola determines the quantity of virtual currency a player receives in exchange for watching a rewarded video ad.
Example: Watching a 30-second video ad rewards the player with a number of gems based on a range set by the product manager.
Individual players are assigned to clusters based on a multitude of dimensions. Players move between clusters as they mature in the game. Gondola’s machine learning algorithm continuously reevaluates the boundaries of these clusters. The result is that every player is presented with their most relevant offers -- increasing purchase conversion.
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. In these situations, we recommend focusing on Target Optimizations (“who sees what”) and clear communication to your players that they are receiving exclusive offers chosen specifically for them.