Customer Lifetime Value Prediction

Customer purchase and browsing data predict lifetime value and churn to personalize marketing and retention efforts.

Business impact

• Customer Lifetime Value — More precise CLTV predictions enable better allocation of marketing resources and retention focus
• Churn Rate — Early identification of at-risk customers reduces churn through targeted interventions
• Marketing Campaign Effectiveness — Personalized campaigns based on CLTV improve conversion rates and ROI

Data requirements

• Transactional Purchase Data (Structured) — Used to calculate historical spend and returns for lifetime value estimation
• Web and App Browsing Data (Text) — Provides behavioral signals to enhance customer embeddings and feature sets
• Customer Profiles from CRM (Structured) — Aggregates demographic and engagement data to enrich prediction features

AI methods and techniques

• Predictive AI — Models forecast future customer value and churn risk based on historical and behavioral data
• Generative AI — Learns customer embeddings from browsing data to improve feature representation

AI models and model families

Random Forest, Deep Neural Network, TensorFlow, GPT-4o

Industries

Real-world evidence

1 documented case study on record.

Companies using this: ASOS.