Welcome
User Guide
Preconfigured Reference Models
MaxAI Workbench provides reference models for use with products like Unica Campaign, Unica Journey, HCL CDP, etc.
Churn Prediction Model
A churn prediction model identifies the likelihood that a customer will stop using a company's products or services within a given period. The objective is to proactively detect customers at high risk of churn so that timely retention actions can be taken.

HCL MaxAI Workbench Documentation
Release Notes
Installation Guide
Developer Guide
MLAC User Guide
User Guide
- Introduction
  MaxAI Workbench is an AI life cycle management platform for applying machine learning to real-world problems. MaxAI Workbench encompasses the complete pipeline from raw data ingestion to a deployable machine learning model with a less-code/no-code approach.
- Model Management
- Model Training
- Model Prediction
  Model prediction predicts the input data based on the pattern of training data.
- Trusted AI
  This feature is available for classification and regression problem types.
- Model Monitoring
  This feature is available for classification and regression problem types.
- Utility
  For Data Generator and BYOM (Bring Your Own Model), select the Utility tab.
- Preconfigured Reference Models
  MaxAI Workbench provides reference models for use with products like Unica Campaign, Unica Journey, HCL CDP, etc.
  - Next Best Channel (NBC) Model
    The Next Best Channel (NBC) model identifies the most effective communication channel for each customer based on demographics, banking behavior, device preferences, and previous marketing interactions. It helps optimize customer engagement and improve conversion rates across channels like WhatsApp, Email, SMS, IVR, Push Notifications, and more.
  - Send Time Optimization (STO) Model
    The Send Time Optimization (STO) Model enhances customer engagement and boost conversion rates by predicting the optimal time and channel to send marketing communications for each customer. By analyzing customer demographics, behaviors, device preferences, and historical engagement patterns, the model personalizes marketing strategies for improving open rates, click-through rates, conversions, and marketing ROI.
  - Churn Prediction Model
    A churn prediction model identifies the likelihood that a customer will stop using a company's products or services within a given period. The objective is to proactively detect customers at high risk of churn so that timely retention actions can be taken.
  - Buying Propensity Model
    A product propensity model predicts the likelihood that a customer, existing or new, will adopt or purchase a specific product in the near future. Predicting which product, a customer is likely to take next.

Churn Prediction Model

A churn prediction model identifies the likelihood that a customer will stop using a company's products or services within a given period. The objective is to proactively detect customers at high risk of churn so that timely retention actions can be taken.

This model leverages customer demographics, product holdings, transaction patterns, engagement behaviour, service interactions, and historical activity data. The target variable (target) is binary:

1 indicates the customer has churned (or is likely to churn), and

0 indicates the customer remains active.

The Churn Prediction model aims to forecast the likelihood of an existing customer discontinuing or reducing their relationship with the bank or financial institution. By accurately identifying customers at risk of churn, the model enables proactive retention efforts, strengthens customer loyalty, and minimizes revenue loss.

For example, assume a bank wanting to retain every valuable customer before the customer decides to leave. Using a churn prediction model, it can pinpoint who is most likely to churn and why, allowing the bank to take timely, personalized actions such as targeted offers, service improvements, or engagement campaigns. This not only reduces attrition but also enhances customer satisfaction and long-term profitability.