Automate the optimization loop

Cotinuous learning: analyze, compute, update

3 Layers of Optimization

Touching the entire monetization of a Free-to-Play game

Target Optimization

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.

Content 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 gold and an optimized amount of coins based on an optimization range set by the product manager.

Video Ad Optimization

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.

Clustering & Customization

Individual players are assigned to offer clusters 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.

Player Split

Gondola Group Control Group
Users 745,136 745,136
Revenue
ARPU $1.0102 $0.8872

15% Uplift!

Measurable Uplift

Optimizations with Gondola always run as a concurrent A/B test. This means that the player base is randomly split into two groups. The performance of the game with Gondola optimization (Gondola Group) and without Gondola optimization (Control Group) are continuously compared. Out customers are charged based on the financial uplift that is generated in the Gondola Group.

  • The group split can change over time (try it on the left).
  • No more performance baselines that are outdated, no more “before and after” hoaxes.
  • Just plain A/B performance measurements, renewed every single month.

Gondola and 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, 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.

Minimum Game Requirements

In order to reach statistical significance, we require at least $150,000 USD recurring monthly cross-platform net revenue (after deduction of all platform fees) to provide offer optimization, and $50,000 USD in recurring monthly video ad revenue to provide rewarded video ad optimization.