Claude Code for Marketers

Pull campaign and creative performance data from your advertising platforms. Give Claude access to the structured performance data alongside information about each creative.

Claude can then analyse patterns across campaigns, compare creative variables against outcomes and produce hypotheses for the next round of testing.

Feed performance learnings from existing creative into Claude. Ask it to identify why a concept may have worked and generate new hypotheses rather than simply making superficial copies.

Turn those hypotheses into structured creative briefs and use Figma or other creative tools to produce variations.

Provide Claude with the campaign objective, audience, brand context, historical campaign performance and relevant research.

Use this context to develop positioning, messaging architecture, campaign territories and production briefs. Store the resulting campaign system somewhere the wider team can work with it.

Connect behavioural and CRM data so Claude can identify meaningful customer patterns. Use those patterns to propose segments, lifecycle journeys, triggers and messaging.

Feed campaign results back into the system to improve future journeys.

Start with a source idea, transcript, essay or piece of research. Use Claude to research and develop the idea before adapting it for different formats.

Store source material and previous content so the system can maintain context and avoid simply generating disconnected posts.

Make brand guidelines, tone of voice, terminology, positioning, design principles and approved examples available as context. Claude can then review new work against those rules, flag potential inconsistencies and explain why something does or does not fit the brand.

The system should assist human creative judgement rather than attempt to replace it.

Combine search performance, keyword research and existing website content. Use Claude to identify gaps, clusters and pages that deserve improvement.

Generate briefs or structured improvements, monitor the resulting performance and feed those learnings back into future decisions. Avoid positioning this as mass-producing low-quality AI pages.

Bring data from multiple parts of the customer journey into an environment Claude can query. Rather than looking at dashboards independently, Claude can investigate changes across acquisition, onsite behaviour, product usage and CRM outcomes and develop explanations for what may have caused performance to move.

The system should distinguish between correlation and proven causation.

Create a structured record for every experiment containing the hypothesis, variables, audience, creative, dates, results and interpretation.

Claude can query this historical record before a new campaign or experiment is designed. Instead of asking “What should we test?”, the team can ask “What have we already learned about this?” Over time, this becomes an institutional memory for the marketing organisation.