Applied AI Marketing Operations: Synthetic Research, Forecasting and Calibration

Meet your instructor and review the course roadmap. Prepare for the rigorous AI operations work that yields reliable outcomes, from writing sharper briefs to running synthetic audience research before you spend a dollar.

Common habits can limit your AI results. Learn the mental moves that make AI work for marketing. We will explore setting goals instead of steps, the Golem problem, and the trap of overspecification.

Build a goal-rich brief using the five-element scaffold. Diagnose over-prescribed, under-specified, and vague prompts for an abandoned-cart email. Compare all four outputs to see what drives your goal.

"Human in the loop" is a common approach. Learn the alternative, human in the lead, and why being surprised by AI is a feature.

Assign an AI assistant a skeptical persona and explicit business goal to run an adversarial review on your campaign. Uncover second-order effects and document strategic revisions in a change memo.

Set up a Claude Project as a reusable base for recurring campaigns. You will learn to update documents when decisions change and open new chats in the same project for campaign consistency checks.

Marketers have four AI surfaces: chat, design, code-or-agent, and low-code. Choosing the right one for the task is essential. Learn the five criteria that decide it.

Use Claude and an image tool to take Vessl into boxing and MMA gyms. Check printed flyer concepts against the brand guide and campaign brief. Draft a short team note explaining your direction.

Hand off a marketing research task to a background subagent with clear scope and a definition of done, then audit what comes back, claim by claim and against the original request.

Learn how to assess and select AI marketing tools based on functional needs, stability, integration, and fit with your existing stack to make informed tool-selection decisions.

Score three AI copywriting tools and an in-house build option on six dimensions. Adjust the scores and weight the factors for a lean team. Write a build-versus-buy recommendation for your manager.

Synthetic personas are a widely discussed AI marketing tool. Learn what they do well and what they do not.

Seed three synthetic personas with real customer data to stress-test a product concept. Run structured interviews to separate real signal from confabulation. Assign a confidence rating to guide your live research.

Score three creative directions on four fixed dimensions using a synthetic persona panel of three personas. Identify panel splits. Deliver a ranked recommendation with a confidence level and stated limits.

Extend synthetic personas beyond 1:1 interviews and A/B tests into scaled audience research. Simulate a survey panel of 24-30 persona variants to produce a structured findings report.

Explore predictive performance analytics to understand what is mature, what is emerging in forecasting and bidding, and what is still hype.

To build a defensible forecast range, let AI detect past campaign patterns, add business context to decide what carries forward, and build low, mid, and high cases with labeled assumptions for your budget.

Check a 30-day forecast against holdout actuals to spot systematic misses and drift. Claude drafts a calibration report for a finance leader. Verify each verdict against evidence to tune or rebuild.

Claude slices customer LTV data to help you spot hidden confounds. You then use these insights to make one spend change for value-based bidding.

Take these AI skills into your next campaign. You can now turn vague asks into goal-rich briefs, pressure test creative with synthetic research, and build rigorous brand investment proposals.