Overview

Analyzing ad data and using it to decide "what creative to make next"—how much time and how many steps do you spend on this process every time?

Reading the analysis report, thinking through an improvement plan, briefing the designer, going back and forth on revisions—it's not unusual for it to take several days for actual creative to emerge from the data. Not "analyze and done," nor "produce and done," but a flow where analysis results are converted directly into creative—that's what's being demanded now.

In fact, we often hear things like:

• "Even after analyzing our ad data, it takes far too long to translate it into a concrete plan for improving the creative…"
• "Even when we have AI generate a banner, it can't accurately reflect the insights we drew from the data, so manual fixes just pile up…"
• "Analysis and creative production are handled by different people, which leads to miscommunication of intent and wasted effort…"

By combining generative AI with MCP, you get an end-to-end flow where ad data analysis results can be used directly as the input for creative production. The intermediate step of "the analyst copies the data and pastes it by hand into the production tool" disappears, making it possible to compress production effort to one-tenth.

In this seminar, we'll walk through concretely the actual flow design that automates ad data analysis → creative generation end-to-end, along with the steps for building it.

What You'll Learn

① The overall design of an end-to-end flow that converts ad data analysis results directly into creative

Which data to hand generative AI and when, and how to structure the instruction system so the creative runs on its own. We'll share the design philosophy and building blocks of the entire flow.

② Concrete implementation steps for connecting ad data via MCP and auto-generating creative

We explain how to connect data tools to generative AI via MCP and build a mechanism that runs analysis → production with zero intermediate steps, in steps you can reproduce on the ground.

③ The priorities for cutting production effort to one-tenth, and a phased automation roadmap

We'll share the decision criteria and rollout steps for where to start automating and how to expand the scope, plus how to build a structure that doesn't fail.

Who Should Attend

• Marketers who analyze ad data but feel it takes too long to translate it into the next piece of creative
• Anyone who uses AI to create creative but can't get a data-driven improvement cycle to run smoothly
• Teams where analysis and creative are handled by different people, making coordination cost and errors a challenge
• Anyone interested in workflow automation with generative AI × MCP who wants to grasp concretely how to apply it in marketing work

This seminar explains the end-to-end mechanism of "turning analysis results directly into usable creative," together with a concrete generative AI × MCP flow. We look forward to having you join us.