Every ad is a hypothesis; most accounts never read the result. NextVector helps creative teams put structure and precision behind volume: one hypothesis per concept, five gates per test, and a learning library that makes every brief smarter than the last.
Meta's Andromeda retrieval system reads the visuals, audio, on-screen text and landing page of an ad to decide who sees it. Audiences, bidding and placement have been absorbed by automation. What remains in your hands is the input the machine learns from.
Creative diversity is a structural requirement, not a testing preference. A thin feed gives the system nothing to match against a fragmented audience.
Ten crops of the same idea don't expand the matching space. The unit of testing is the concept, with format and hook variants nested underneath.
Name every asset to its territory, concept, hook and format. Then a result at asset level rolls up into a learning at territory level, and you reinvest in territories rather than cloning single ads. Speed is motion; velocity is progress.
Every creative that enters an account travels the same loop. Each gate protects the one after it: weak assets never reach the auction, unpowered tests never reach a read, noisy reads never reach a verdict, and no verdict escapes the learning library.
Before a pound is spent, score every asset against platform best practice: text coverage, legibility, visual hierarchy, scroll-stop potential, brand consistency and format fit. Weak assets don't just underperform; they waste learning budget and pollute the read for everything tested alongside them.
Every test gets a documented hypothesis, a pre-registered primary metric, a sample-size and minimum-detectable-effect calculation, and a read date. Tests that can't reach significance at the available budget shouldn't run, or should be redesigned as directional reads and labelled honestly as such. Keep a backlog of six or more prioritised hypotheses, reviewed quarterly.
If any field is blank, the test isn't designed yet. It's a guess with spend attached.
Calling winners early is a classification problem, so validate it rather than feel it. Check 6–12 months of history: did early signals (hook rate, thumbstop, CTR) actually predict the final winners? If the early signal doesn't classify past winners, it doesn't earn the right to kill future ones. Direct-response accounts should bias toward killing fast; brand work toward protecting potential heroes.
Measurement is a ladder, and confidence climbs it. Climb as high as budget and addressability allow, and respect the full attribution window rather than the dashboard on day 14. Never let the bottom rung set targets on its own.
Log the delta first, the interpretation second and the action third. Then cite the library in every new brief, so each quarter's brief is smarter than the last.
On a quick-service restaurant conversion campaign, the softer sell drove cheaper orders. It didn't become a footnote; it became a deliberately designed follow-up test.
When one insurance product line outperformed its sibling, the diagnosis traced to incentive-led messaging, so the same logic was briefed into the other line: transferring the territory, not cloning the asset.
Learning that lives in people's heads leaves with them. Without the library, the same mistakes repeat each quarter.
Two reads from James's own accounts, anonymised; staged the way learnings should be logged — delta first, interpretation second, action third.
Pick an example ad. The rubric score reflects the tool's criteria; it is not a performance forecast.
Based on the seven marks of a mature creative-testing programme. Your answers stay on this page.
Based on your answers; nothing has been independently checked.
Start with an example and grow into a review.
James Dunn's complete approach to creative testing in the Andromeda era: the principle, the platform shift, the five gates and what a mature programme looks like.
Read the article →Every ad is a hypothesis. The job is making sure every hypothesis gets read.