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Lab guide for Marketing Campaign Generation

Overview

This lab simulates a marketing campaign generation scenario. The solution uses IBM's data and AI platform to ingest customer data, identify high-value segments, and automatically generate personalised campaign content — including copy, subject lines, and channel recommendations — ready for review and deployment.

Coming Soon

This lab guide is currently being developed. The full step-by-step instructions, reference architecture, and suggested scripts will be published here shortly. Check back later.

Pre-requisites

  • Make sure you've already set up the environment:
    • Lab Environment Setup (coming soon)
  • Access to the relevant IBM product environments (details to follow)

Reference Architecture

Reference architecture diagram coming soon.

Key Components

  • Customer Data Layer – Ingests and unifies customer profiles, purchase history, and behavioural signals from multiple sources.
  • Segmentation Engine – Clusters customers into meaningful segments based on recency, frequency, and monetary (RFM) analysis.
  • AI Campaign Generator – Uses a foundation model with domain-specific prompts and guardrails to produce on-brand campaign copy for each segment.
  • Governance & Review Layer – Tracks generated content lineage, applies content policies, and surfaces results for human review before deployment.
  • Analytics Dashboard – Monitors campaign performance metrics and feeds results back to improve future generation quality.

Steps

1. Set up the data environment

Steps coming soon.

2. Define customer segments

Steps coming soon.

3. Configure the campaign generation model

Steps coming soon.

4. Review and approve generated campaigns

Steps coming soon.

5. Test the solution end-to-end

Steps coming soon.

Suggested script

Suggested interaction scripts and expected outputs will be provided here.

Conclusion

👏 Congratulations on completing the lab! 🎉