MochiBits is a studio based in Los Angeles with over 20 million downloads across their portfolio of games. Their most popular title is Left vs Right, a Brain Training game with 5+ million downloads and countless features on both the App & Play Stores.
Left vs Right has recently pivoted to focus on building a subscription tier for it’s most engaged and loyal community members.
Success in this area required complete focus for a long period, and consequently the pre-existing ad implementation was not optimised to it’s potential.
With the success of subscriptions, users increasingly fell into two very different categories; those that opted for a subscription (and were monetised effectively), and non-paying users interacting with ads (that weren’t being monetised effectively).
The challenge facing Kyle and his team was around how they could optimise Ad ARPDAU within the game to improve monetisation rates in non-subscribing users.
Protecting the path to a subscription was key, and as a result it was not possible to introduce additional ad placements that may cause extra friction in the user experience.
“Alastair has helped us see the full revenue potential of our existing ad inventory. He had a significant impact on our revenue quickly and continues to search for ways to incrementally improve our setup.” Kyle Yamamoto, MochiBits
The first step towards improving Ad ARPDAU within the game was to increase competition for the inventory, by adding three new demand partners to the five that were already live.
We then reviewed each partner in the stack and assessed whether they would add the most value through bidding or the waterfall.
Once the right partners are operating in the right environment, a tiered global waterfall was created. As user experience was to be protected at all costs, no additional ad placements were introduced to the game and so waterfall optimisation and management would be our sole angle for success.
When creating a waterfall with no insights from prior optimisation, outside of past experience with each network we know little about how each network will perform relative to one another, and how valuable advertisers will perceive the userbase to be.
An initial discovery phase allowed us to start developing an understanding of these answers. We start with a balanced “skeleton” structure, and then move through several initial rounds of making changes and assessing their impact. We move towards maximising the contribution of each network while ensuring that fill rates are not sacrificed.
“Alastair has helped us see the full revenue potential of our existing ad inventory. He had a significant impact on our revenue quickly and continues to search for ways to incrementally improve our setup.” Kyle Yamamoto, MochiBits
