Experimentation workflows for revenue growth in performance marketing

A test-and-learn loop for ChatGPT ads: writes new titles and one-line copy from the winning angles, tests each against the current best with a fixed split, and retires the losers on cost per goal once there is enough data.

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 LinkedIn 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 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.

Keeps a ranked backlog of test ideas across channels, ordered by expected effect and the spend at risk, so the next test is the one with the best payoff. You can set your own scores.

After every concluded test on any channel, writes a short readout and logs the learning, so future workflows do not repeat it.

Uses native split tests to prove whether a different Google Display bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different Google Search bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different LinkedIn bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different Meta (Facebook & Instagram) bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different Microsoft (Bing) bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different Snapchat bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different TikTok bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different YouTube bid strategy or target actually beats the current one.