How the Audyense priority score works, and what it does not claim

A list of a hundred prospects is only useful if the right twenty are at the top. That is the whole job of the Audyense priority score: to put the most obvious fits first so you spend your time on them. It is deliberately simple, and it is deliberately not a prediction.

Points you can check

The score is the sum of points for checks anyone could verify on the prospect's record, and every prospect shows the full breakdown under the score:

• Role: the title matches one of your target roles (up to 25).
• Seniority: their level is one you are targeting (15).
• Industry: their company is in your target market (15).
• Company size: within your range (10).
• Location: in your target geography (5).
• Buying signals: new in their role (10), and hiring or growth signals where the data source has them (15).
• Contact: a work email is available (5).

The number you see is exactly the sum of the lines under it. Nothing is hidden and nothing is weighted by a model you cannot inspect.

Unknown is not a zero

When the data cannot settle a check, it says "unknown" or "not checked" and scores nothing, rather than guessing. That keeps a thin record from looking like a bad fit, and it keeps a guess from looking like a fact.

Why not a prediction?

A tool that claims to know who will buy has to be trained on who bought, and a small company does not have enough history for that to mean anything. Points for visible checks are honest about what is known today. As your pipeline fills up with real replies and meetings, that history is what will let the weighting learn.

How to use it: work down the list from the top, read the "Why this prospect?" before you contact anyone, and mark people Not a fit when the score is wrong. That feedback is how your ICP gets sharper.