5 tools
Beanoc does not connect to your ad accounts and does not have your data. That is deliberate: you paste the figures you already have, and these tools do the interpretation — the arithmetic, the ranking of what matters, and the honest note about what your sample cannot prove.
Paste in the marketing data you already have — spend, traffic, conversions, whatever you're tracking — and this tool reads it back in plain English: what stands out, what's likely connected, and what's worth investigating further.
After a campaign ends, paste in the results — by channel, ad set or creative, whatever breakdown you have — and this tool structures a plain read of what performed, what didn't, and where the biggest opportunity for the next round likely is.
Paste in the numbers at each step of your funnel — visits, signups, activations, purchases, whatever stages you track — and this tool identifies where the largest drop-offs happen and reasons through likely causes based only on the figures and context you give it.
Paste in your variant results — visitors, conversions, whatever you tracked per version — and this tool explains what the numbers show in plain language, flags whether the sample looks large enough to trust, and suggests a next test.
Paste in your KPIs — CAC, conversion rate, retention, whatever you report on — and this tool explains what those numbers mean when read together: where there's tension between them, what a change in one likely means for another, and what's worth watching.
The fastest way for an AI to sound authoritative about marketing data is to invent an industry average. Every tool in this hub is prompted to refuse that. Comparisons are made against your stated target and your own previous figures, and where a number is missing, the tool asks you for it by name.
Conversion rates, cost per outcome, lift, payback — every derived figure is shown with the calculation, so you can verify the read instead of trusting a summary. The funnel diagnostic ranks leaks by recoverable volume rather than percentage, because a small drop on a big stage is usually worth more than a big drop on a small one.
The A/B test interpreter assesses whether your sample and runtime can support a decision at all, names the validity threats it can see, and will tell you to keep the test running. A tool that always finds a winner is not analysis.
No. There are no analytics integrations, and the tools never claim access to data you have not pasted in. You copy the figures from your own reporting and the AI interprets them.
Whatever you have — channel, spend, sessions, conversions, revenue, stage-by-stage counts. The more complete it is, the sharper the read. Each tool ends by naming the specific metrics that would improve the next analysis.
It can rank plausible explanations against your own numbers and give you the test that would confirm each one. Those are labelled as hypotheses, not causes — nothing in the output is presented as a finding your data cannot support.
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