Mad Max performance marketing workflows for ecommerce brands

Re-fits channel and campaign response curves weekly and moves budget across channels toward the best next dollar in small steps, inside every guardrail.

Maps each common anomaly type to a pre-approved fix that runs automatically, and escalates only what the playbooks cannot handle.

Values first orders by predicted 12-month customer value, so bidding pays more for customers likely to come back.

Replaces fixed thresholds with forecast intervals per campaign and metric, so alerts fire only when something is truly unexpected given seasonality and trend.

Uses an LLM to cluster recent wins in the CRM into new ICP segments, and turns each one into targeting for every channel.

Keeps testing new seed definitions (by value, recency, product and segment) for lookalike and predictive audiences, and promotes the seeds that produce the best customers.

Forecasts month-end spend, goal volume and cost per goal for each channel with a likely range, and says what to change today to hit plan.

When signal-based workflows surge spend (competitor outages, trends, launches), funds the surge by cutting the least efficient spend elsewhere so the cap holds.

A test-and-learn loop for Google Display ads: draft new image variants in Figma, Canva or Runway from the winning angles, write a brief for any video, test each against the current best with a fixed split, and retire the losers on cost per goal once there is enough data. Needs an image MCP such as Figma, Canva or Runway connected in Roadway.

Writes new responsive search ad headlines and descriptions from your winning angles, tests each against the current best with a fixed split, and retires the losers once there is enough data.

A test-and-learn loop for Meta (Facebook & Instagram) ads: build new image variants in Figma, Canva or Runway and new video in Runway or Google Veo from the winning angles, test each against the current best with a fixed split, and retire the losers once there is enough data. Needs an image MCP such as Figma, Canva or Runway, or a video MCP such as Runway or Google Veo, connected in Roadway.

Writes new responsive search ad headlines and descriptions from your winning angles, tests each against the current best with a fixed split, and retires the losers once there is enough data.

A test-and-learn loop for Reddit ads: draft new image variants in Figma, Canva or Runway from the winning angles, write a brief for any video, test each against the current best with a fixed split, and retire the losers on cost per goal once there is enough data. Needs an image MCP such as Figma, Canva or Runway connected in Roadway.

A test-and-learn loop for Snapchat ads: build new image variants in Figma, Canva or Runway and new video in Runway or Google Veo from the winning angles, test each against the current best with a fixed split, and retire the losers once there is enough data. Needs a video MCP such as Runway or Google Veo connected in Roadway.

A test-and-learn loop for TikTok ads: build new image variants in Figma, Canva or Runway and new video in Runway or Google Veo from the winning angles, test each against the current best with a fixed split, and retire the losers once there is enough data. Needs a video MCP such as Runway or Google Veo connected in Roadway.

A test-and-learn loop for X ads: draft new image variants in Figma, Canva or Runway from the winning angles, write a brief for any video, test each against the current best with a fixed split, and retire the losers on cost per goal once there is enough data. Needs an image MCP such as Figma, Canva or Runway connected in Roadway.

A test-and-learn loop for YouTube ads: build new image variants in Figma, Canva or Runway and new video in Runway or Google Veo from the winning angles, test each against the current best with a fixed split, and retire the losers once there is enough data. Needs a video MCP such as Runway or Google Veo connected in Roadway.

Measures how many Meta (Facebook & Instagram) conversions your ads truly drive and recalibrates targets with the result.

Measures the true impact of big account changes (a restructure, a bid strategy switch, a new channel) against a comparison group rather than before and after.

Reads how AI answers describe your brand against competitors from the Peec AI MCP, and uses ChatGPT ads and search ads to cover the questions where you are missing or misrepresented. Needs the Peec AI MCP connected in Roadway.

Tracks registrations against capacity for every event and scales campaigns so each one is 100% full a week out, then stops spending.

When a competitor launches a product or changes prices, coordinates the response across channels in phases, starting where the overlap is highest.

Uses an LLM to grade every new search term's relevance to the ICP and offer, adding negatives and keywords automatically before wasted spend piles up.

In the first 14 days of a launch, watches early demand signals (search volume, CTR and conversion rate) and scales the launch campaigns that show real pull in measured steps.

Recomputes next month's cap from forecast pipeline coverage, revenue targets and how fast spend pays back, instead of a static number set once a quarter.

Drafts a dedicated landing page when a keyword or ad theme's volume and profitability clear your thresholds, then sets up a test against the current page. Needs a page builder MCP such as Webflow, Framer, Lovable or Ploy connected in Roadway.