How to build a better marketing tech stack for ad-monetized apps
Today we’re taking a look back at one of our favorite talks from Mobile Spree San Francisco 2017 with the CMO of Gazeus Games, Paula Neves.
Our Mobile Spree San Francisco conference in 2017 featured over thirty expert speakers from the mobile marketing industry, multiple panel discussions, and six different workshops. If you haven't heard of Gazeus Games, they're a developer of mobile games headquartered in Brazil. They specialize in classical card games - the ones you grew up playing - including canasta, spades and euchre.
In 2012 they ported their first game to mobile and have been mobile-first ever since. Today they have over 40 titles and are one of twenty Facebook Instant Game partners. To give you an idea of their size, Gazeus sees over 9 million monthly active users.
99% of Gazeus's revenues come from ads - the old school sort, so standard and interstitial banners rather than something like rewarded video ads. So how do they make it work?
You can watch Paula's talk in its entirety below right now, or read on for a summary of the key takeaways from the day:
With ad-monetized apps, you need big scale to make it happen.
Device-level data is a must. Having ROI for apps as a whole just doesn't cut it anymore- getting a campaign-level view and a device-level view allowed them to group users according to ROI and LTV, allowing Gazeus to group clusters together for personalized messaging and better acquisition overall.
Gazeus sends events for interstitial impressions, every standard impression, plus all of their product events; these are mapped out in Adjust and Gazeus uses the global callback and CSV features to get this information back into their own structure.
One major challenge for Gazeus involved some creative work around connecting each of the APIs they use to build all of their views in Tableau; their solution was to append tags their structure could understand. They now have a campaign ID generator inside their intranet.
Paula showed the crowd several graphs to demonstrate the efficacy of their marketing tech stack; in one case, with this setup they were able to look at average running revenue (a cumulative measure of LTV) by iPhone and iPad, revealing that one particular campaign performed far better on the iPhone, bucking a trend they had previously noticed. This fed straight into their UA strategy, allowing them to shift their spending strategy to only spend 1/3 of their original budget on the campaign and maintain the same results.