Businesses struggle to accurately predict customer lifetime value (LTV) due to the complexities of user behavior and the dynamic nature of market conditions. Inaccurate LTV predictions lead to inefficient allocation of ad spend, suboptimal customer retention strategies, and ultimately, reduced return on ad spend (ROAS).

Churney leverages causal machine learning to provide precise LTV predictions, enabling businesses to optimize customer acquisition and retention efforts. By analyzing data warehouse information, Churney's deep causal ML models deliver predictions that are robust to drift and environmental changes. These predictions allow companies to directly optimize user acquisition campaigns by feeding LTV data to platforms like Meta and Google, focusing ad spend on campaigns that drive the most predicted LTV and quickly switching off underperforming efforts. Churney also predicts churn and identifies effective incentives to maximize customer LTV, ensuring the right treatment is applied to the right customer at the right time.

Causal machine learning models for accurate and reliable LTV predictions

Integration with Meta and Google Ads for direct campaign optimization

Churn prediction and identification of effective retention strategies

Analysis of data warehouse information to identify high-value customers

Treatment effect predictions to determine optimal incentives for each customer

Dashboards for management, marketing, and other teams to make data-driven decisions

GDPR and AICPA SOC 2 compliance for data protection

Churney targets marketing teams, management, and data-driven decision-makers focused on optimizing user acquisition and retention strategies for maximum lifetime value.