Case Study: Predicting user LTV early and accurately
Random Logic Games increases user predicted lifetime value (pLTV) accuracy and saves time by leveraging Audience Reporting forecasting.
Random Logic Games is a sophisticated app developer. Their chart-topping puzzle games are renowned for being a fun, engaging, long-lasting source of entertainment for users around the world. But it’s not just a great product that makes Random Logic successful. Andrew Stone, their co-founder, takes a very sophisticated, data-driven approach to all parts of the business.
“We test and validate new features, communication, monetization, and acquisition strategies to make our product better We even rely heavily on testing potential titles when deciding the next app to build.”
Data-wrangling multiple immense, fragmented, complicated data-sources is expensive and time-consuming. Andrew is one of the few companies who’s invested in building out his own data pipeline, infrastructure and visualization.
“We needed visibility so we invested a lot of time in building a system that gathers, stores and reports across all of our data providers.
It worked great, but it was time-consuming to maintain, difficult to build new features and generally too difficult to use .
I had the data, I just didn’t have time to manipulate it to make it useful.”
A longtime AdLibertas customer, Random Logic Games was an early adopter of Audience Reporting. By just supplying credentials Andrew and his team was able to start viewing the pLTV of user groups at the highest level of granularity.
“We can now run pLTV of new users quickly and easily. No more wrangling data into a database, exporting, then harassing spreadsheets to derive projections. With just a few clicks I can fit a pLTV curve against a selected audience and get a 1-year LTV. I can do this for the entire app, against my Firebase AB tests, or against newly acquired users.
I can easily get a pLTV of a user who’s played 5 games then compare against one that plays 10. I’ve never been able to do this before. This is a must have for any app developer.”
Interested in how predictive forecasting works? See a demo above or read about how the product works by reading the product release at the link below.