Automate the optimization loop

Cotinuous learning: analyze, compute, update

4 Layers of Optimization

Changing the way you manage a free-to-play game

Target Optimization

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.

Store Optimization

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.

Currency Optimization

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.

Video Ad Optimization

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.

Clustering & Customization

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.

Improving Games with Multi-Armed Bandit Optimization

Gondola uses a Multi-Armed Bandit (MAB) framework for decision making. As opposed to common A/B testing, where a limited period of pure exploration with rigid traffic allocation to different variants is followed by a period of pure exploitation of the “winning” variants, MAB tests are adaptive. It combines exploration with exploitation (“learn and earn”) by gradually moving traffic towards winning variations while continuing to test other variants for different player segments.

Gondola’s MAB continuously switches between the learning phase (used to learn how different offers and rewards perform) and the earning phase (using the best offers to maximize performance). The approach works particularly well for free-to-play (F2P) games, since these games are subject to continuous change, requiring continuous optimization and testing. As a consequence, our MAB algorithm never declares a winning variant and never phases out other variants completely, but rather takes an adaptive approach to an ever-changing game environment. All 4 Gondola Optimization Modules use this technology.

Gondola & the Player Community

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.