Forecast the net profit revenue for each B2B customer by predicting their lifetime value.
CLV/LTV predictions and ordered B2B customer groupings based on forecasted value
Ordered B2B customer groupings based on predicted churn
Understand key predictors for B2B CLV/LTV value
Overview of proportional hazards modeling and discrete-time logistic hazard models
This describes how far ahead in the future this model can be trained to predict. Ideally, the historical data available for training would be 2-3 times longer than the prediction window.
Identify high-value B2B customers and provide them a red-carpet experience.
Maintain ongoing high-value B2B customer focus.
A typical modeling engagement with GlorifAI lasts 3-4 weeks once we get access to the data and proceeds in three distinct phases.
