Review the data usage information, support, limits, and considerations for generative AI features, and the model details and usage for predictive AI features in Marketing Cloud Next.
- AI Data Usage in Marketing Cloud Next
AI is built on data. When using Marketing Cloud Next, review this table to understand the type of data used by AI agents and features. - Considerations for Agentic Campaigns
To use agentic campaigns, consider supported functionality, usage, limitations and allowances, limits, and other issues. - Considerations for Agentic Content
To use agentic content, consider supported functionality, usage, and limitations and allowances. - Considerations for the Campaign Creation Agent in Marketing Cloud Next
To use the Campaign Creation agent, consider supported functionality, usage, limitations and allowances, limits, and other issues. - Considerations for the Account Discovery Agent
Before you create an agent with the Account Discovery agent template, consider supported functionality, usage, limitations and allowances, limits, and other issues. - Considerations for the Journey Decisioning Agent in Marketing Cloud Next
To use the Journey Decisioning agent, consider supported functionality, usage, limitations and allowances, limits, and other issues. - Considerations for the Distributed Marketing Agent
Before you create an agent with the Distributed Marketing agent template, consider supported functionality, usage, limitations and allowances, limits, and other issues. - Considerations for the Content Creation Agent in Marketing Cloud Next
When using the Agentforce Content Creation agent to generate content, keep in mind these usage limits and considerations. - Considerations for the Marketing Planning and Operations Agent in Marketing Cloud Next
Before you set up an agent with the Marketing Planning and Operations agent template, review the supported languages, models, regions, usage, and other considerations. - Model Card: Einstein Engagement Frequency
In Marketing Cloud Next, Einstein Engagement Frequency analyzes each contact’s engagement record to determine the optimal frequency for sending messages to that contact. - Model Card: Einstein Engagement Scoring
In Marketing Cloud Next, Einstein Engagement Scoring predicts email opens and clicks, subscriber retention, web conversion, and overall engagement for each individual consumer contact. - Model Card: Einstein Metrics Guard
In Marketing Cloud Next, Einstein Metrics Guard analyzes email open and click events and calculates the likelihood that each event is real. The model primarily focuses on filtering emails through large-scale email providers that open messages before the customer opens them. The model is also effective for opens and clicks from email scanners on private, corporate email servers. - Model Card: Einstein Send Time Optimization
In Marketing Cloud Next, Einstein Send Time Optimization analyzes the optimal time to send a message to an individual to maximize the probability of the individual engaging with the message.