Agents

Other hook tools give you a score. This one refuses to. It measures which axis your own numbers support, and throws out the ones they do not. Paste your hooks with their real numbers. It builds candidate axes from your own copy, tests within-group homogeneity first, and only then compares groups. An axis whose groups are not internally consistent is rejected, not crowned. Ships with one measured prior: name the specific thing, not the category. On 14 ad creatives that ran 2.44x on CTR.

Your CTR looks fine but sales do not follow. Usually the problem is not the creative. It is what happens after the click. Give it spend, impressions, clicks and landing-page views per campaign. You get cost per arrival beside cost per click, and the campaign whose rank flips between them. That flip is the finding. From a real account: 307 clicks became 221 arrivals, while another turned 99 clicks into 2. A 166x gap — that one sent paid traffic to a social profile. Never touches tracking code.

You ran a test and one variant looks better. Is that a finding or a coin flip? Paste trials and successes per variant. You get Wilson 95% intervals, a two-proportion test, and the check almost nobody runs: an overdispersion gate asking whether your grouping label explains anything. If the spread inside a group beats the spread between groups, no winner is declared. A p-value near zero has died on this gate before. Post-hoc axes get labelled. Underpowered tests get a sample size.

Name a source country, destination market and sector — Ccorridor returns a ranked, regulation-gated, margin-checked shortlist of what will actually sell cross-border, plus live-commerce bundles. It scores every candidate on the TSF framework: global trend momentum × source-country validation × destination demand-and-supply gap × live-commerce fit × unit economics. Data, not hype. by AscendraAI.