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User Guide
Reference Models
In version 25.1.0, MaxAI Workbench provides reference models for use with products like Unica Campaign, Unica Journey, HCL CDP, etc.
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.

HCL MaxAI Workbench Documentation
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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.
- Reference Models
  In version 25.1.0, 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.

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.

Key Features

Time-based Personalization
Predicts the optimal send time for each user based on historical engagement.
Performance-driven
Improves open rates, click-throughs, and conversions.
Automated Scheduling
Outbound messages are sent at the time users are most likely to engage.

This model will be used as is with HCL CDP. For more details related to integration and using the models, see the chapter MaxAI Integration and its sub-topics, within the HCL CDP Integrations Guide.