Observed Signal · Apr 10, 2026 · How-to Guide · Source: https://martech.org/feed/ · Impact: 2/5 · Sentiment: Positive
6-step AI workflow for seasonal campaigns
MarTech published a six-step how‑to for using large language models to plan and improve seasonal marketing campaigns. The guide outlines an iterative 'agent' workflow (intake, synthesis, build, refinement) and recommends building a Claude project with curated reference materials—past campaign dashboards, CRM data, brand guides, reviews and industry research (e.g., NAR, Bank of America, Zillow, Redfin). It explains prompt structure (role, context, task, output, considerations), connecting live research sources, and iterating on results while respecting compliance and data‑sharing limits. The article positions the workflow as applicable to complex, high‑consideration categories (mortgages) and transferable to other industries with more linear purchase paths. MarTech notes contributors’ opinions are independent and that MarTech is owned by Semrush.
Practical guidance for marketers on applying LLMs and agent workflows to campaign planning and data practices; useful operationally but not a major platform policy or product launch.
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Key Takeaways & Evidence Grounding
- MarTech published a step-by-step guide for a six-step AI workflow to plan seasonal campaigns.
- The workflow uses an iterative LLM agent approach and specifically demonstrates the process using Claude.
- Core steps include: define project purpose; create a Claude project and add reference materials; write structured prompts (role, context, task, output, considerations); connect research sources; write prompts; and iterate on results.
- Recommended reference inputs include past campaign dashboards, CRM funnel data, brand guidelines, customer reviews, and industry research sources such as the National Association of Realtors, Bank of America, Zillow, Redfin, LendingTree, and CFPB.
- MarTech is owned by Semrush and the article includes a legal/compliance reminder about sharing data with LLMs.
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