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Tilting Point LAT UA Case Study

Tilting Point runs Contextually Driven LAT UA Campaigns with, FTDs Increase

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Tilting Point Intro 

Tilting Point is a leading free-to-play publisher that powers up existing live games using deep marketing and product expertise, cutting-edge technology, a user acquisition war chest. Founded in 2012, Tilting Point has grown to a staff of over 150 people with offices in New York, Barcelona, Boston, Kyiv, Seoul, San Diego & St. Petersburg. Tilting Point’s most successful games include Star Trek Timelines, Warhammer: Chaos & Conquest, and SpongeBob: Krusty Cook-Off.

In June 2019, Tilting Point entered into its largest UA Funding partnership to date, investing up to $30 million to fund user acquisition for developer Mino Games’ popular collection game, “Cat Game – The Cats Collector!. The funding from Tilting Point will help grow Cat Game, which was amongst the Top #100 grossing apps in Canada and New Zealand during its iOS soft launch. Intro is a tech product company driving mobile user acquisition for brands and app developers, operating world-wide and focusing on the US, KR, JP, RU, SEA, and IN markets. Using our proprietary bidder and machine-learning algorithms, we offer transparent, performance-based, highly-targeted UA, and retargeting solutions. strives to be more than just a vendor for its partners, but a partner that helps generate actual value, growth, and broad marketing insights that can be used across channels.

Campaign Goals

Starting in March, worked together with the Tilting Point team to expand their programmatic activity in order to reach and acquire engaged users for Cat Game on both iOS and Android, in the US, CA, GB, JP and KR. 

Considering the upcoming depreciation of the IDFA on iOS 14, during August, LAT (Limited Ad Tracking) campaigns, driven by our machine-learning contextual targeting were also started in order to maintain growth without user-level targeting. 


When we started running campaigns our initial approach was intent-based targeting. We targeted users that showed direct interest in a variety of relevant genres, mechanics, and themes – cats specifically and animal characters in general, collection mechanics, merge mechanics, and idle mechanics. This approach allowed us to quickly acquire users with significantly higher than usual D1 and D7 retention rates for the genre.

Then, our machine learning models gradually learned the patterns of the users that were most engaged or made purchases on the day of their install, which allowed us to target similar users and eventually resulted in meeting the ROAS KPI, and then scale.

In August, our approach towards setting up LAT campaigns was similar, but more heavily based on the contextual relationship between the placements apps and Cat Game.

Using ELMo, a natural language processing algorithm, we are able to discern each apps’ context, composed of its features, mechanics, and themes, by creating a vector representation of their app store description, which is significantly more representative than its store category.

This in turn allows us to calculate the contextual distance between the promoted app and each placement app, in real-time, and set up different bidding strategies based on their contextual distance, as well as dozens of the other of our models’ features, allowing for granular optimization, and effective exploration.


FTD CVR (install to purchase) - 4x higher than organic traffic.
D3 Retention - 20% higher than non-LAT UA
eCPI - on par with the non-LAT UA campaigns
 Diogo Martins - Associate User Acquisition Manager @ Tilting Point

The team at is super responsive, which is helpful when there are sudden shifts in strategy. When it came to testing new optimization methods for LAT campaigns on Cat Game, their team was quick to adapt to our specific needs.

Diogo Martins – Associate User Acquisition Manager @ Tilting Point
 Joseph Iris - RTB Product Lead @

The team at Tilting Point are true professionals, setting us up for success from the ground up with transparent communication and openness to share all the data needed to run effective UA, even without user-level targeting

Joseph Iris – RTB Product Lead @
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