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When discussing data requirements with our clients there are three focus areas:

This article provides a general overview of the data Churney requires to deliver its value. For detailed technical data requirements, please refer to the comprehensive guide shared with you during the onboarding process.

Our causal prediction models thrive on log-level data - the more granular, the better. They are actually designed to process vast amounts of detailed user interactions, transactions, and behaviors. However, not all data is created equal; certain types of information are more crucial than others in predicting customer lifetime value for optimization. But before we dive into what data to share, let’s first align on some basics: 

Data types - the good, the bad & the ugly

Event level user interactions & telemetry

Providing the right data in the right format is critical to unlocking the full potential of Churney's predictive LTV models. For any questions or support during the onboarding process, our team is here to assist and ensure a smooth and compliant data integration experience.

Predictive LTV modeling requires specific data inputs to optimize user acquisition strategies. We frequently address questions about our data requirements. This guide explains which data is essential, important, and nice-to-have for our machine learning models, while also addressing common concerns about data sharing and privacy.

Essential Data Guide for Predictive LTV Modeling

What data do you need

From onboarding to running an experiment

Providing access to campaigns, historical user and revenue data

Share Ad network access with Churney

Connect your data to Churney

Tag your data for Churney’s model

Sharing

Analyzing data and building predictive lifetime value models

Sit tight while we build your model

Assess the size of your business opportunity

Modeling

Sending predictions to ad networks for optimization

Optimize the signal volume

Launch our experiment

Learning Phase - Frequent Checks & Updates

Optimize the experiment

Summarize the experiment

Activating

Churney’s onboarding takes 10 days, with a standard experiment length of 55 days. This guide details when data science, data engineering, and UA staff from both your team and ours are needed to ensure a successful experiment. Experiment duration is flexible and can extend up to 4 months, depending on your business needs and cohort maturity requirements.

Onboarding V2

The onboarding process to Churney is mostly painless, as we only require consistent user IDs, understood payment events, and reliable timestamps across events. However, we identified some issues that may arise with time. We outline these issues in this document, both from a data engineering perspective and from potential vulnerabilities in the ad platform setup.

Guides