Growth data infrastructure based on DataVault makes it simple for clients of data science agency Patya to compete
Patya analytics is a data science agency. The company provides business intelligence and data science services on top of mobile app data.
TENJIN USE CASES
Patya works with all kinds of app and game companies. The app store has become such a data game for everyone – but most of the time app developers do not have an internal data science team, or the ‘know how’ on what to do with their data and how to do it. This is where Patya comes in – Patya sets up LTV models and predictions, does behavioral analyses to identify the best performing user groups, & creates BI systems.
There’s three scenarios. For one, most of Patya’s indie clients have an attribution provider and an analytics provider. At best these companies run band-aid projects with small chunks of their data sets that are super time-consuming. Secondly, the big guys often often have Bigquery or Redshift databases where they integrate different data sets – but the marketing teams never get access to the raw data without nudging the gatekeepers in engineering daily. Asking an internal BI team for the FB Lookalike query the UA team needs and getting a response 5 days later does not work in this competitive landscape. That’s why also these companies, despite their resources, rarely systematically do the data tricks needed to succeed. And then there’s Tenjin. With DataVault the company get immediate access to all the user-level growth data they need for their queries and models – whenever they want and without being a hassle to the engineering team. Once the service company finishes the project it’s super easy for the UA team of the app developer to keep their value-creating data operations up.
“Without Tenjin, my biggest hurdle as a data scientist is to get direct access to clean user-level data from all the mobile campaigns. For example, in a typical user segmentation project without Tenjin I spend 80% of my time trying to complete and clean data sets. On a three weeks project this means more than two weeks of data cleaning and two, three days of actual modelling. With DataVault I get immediate access to all the user-level growth data I need for my queries and models – whenever I want and without being a hassle to the engineering team.”
Elisabeth Reitmayr, Co-founder and Data Analyst
The company has helped a couple of Tenjin clients to build up growth data use cases on top of DataVault that make a real difference to their business. Patya’s clients now create unique FB Lookalikes and run AdWords at scale. They have change pieces of their core game mechanics to address the need of their most valuable user segments. They run campaigns based on their own daily LTV prediction. They enrich their Tenjin data sets by combining it with their other data sources.
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