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The Reactivate module uses AI for content generation, customer analysis, and email personalization.

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Multi-Phase Email Generation Workflow

The AI-powered email copy generation follows a structured multi-phase workflow:
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Phase 1: Research

The AI research agent gathers information about the brand, products, and target audience. Uses search integration for market research.
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Phase 2: Strategy

Based on research, the marketing analyst agent defines the email strategy: tone, messaging, key selling points, and call-to-action approach.
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Phase 3: Segmentation

AI-driven dynamic segmentation analyzes customer data to create targeted segments for personalized messaging.
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Phase 4: Copy Generation

AI generates email copy tailored to each segment, using brand variables and global prompt variables for consistency.
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Phase 5: Visual Content

Hero images and banners are generated using the configured AI image generation provider.
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Phase 6: Assembly & Review

Content blocks, images, and copy are assembled into complete email templates ready for review and scheduling.

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Prompt Variables

The system uses two levels of prompt variables for AI consistency:

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Global Variables (GlobalVariableEntity)

System-wide variables that apply to all brands:
  • Writing style guidelines
  • Compliance requirements
  • Default tone and voice

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Brand Variables (BrandVariableEntity)

Per-brand customization:
  • Brand voice and personality
  • Product terminology
  • Audience descriptors
  • Key selling propositions
See the PROMPT_VARIABLES_GUIDE.md in the repo for detailed configuration.

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Marketing Analyst Agent

The marketing analyst (POST /reactivate/marketing-analyst) is an AI agent that:
  • Analyzes brand positioning and competitive landscape
  • Generates email marketing strategies
  • Recommends sequence structures and timing
  • Provides copy direction for email templates

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Research Agent

The research agent (ResearchSessionEntity) conducts automated research sessions:
  • Search integration for market data
  • Competitor analysis
  • Product trend identification
  • Results stored for reuse across campaigns

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Product Recommendations

AI-driven product recommendations (ProductRecommendationResultEntity) power personalized product grids in emails based on:
  • Customer purchase history
  • Browsing behavior
  • Product affinity scores
  • Trending products in the catalog