Oops! Something went wrong while submitting the form.
Every week, Sai applies your weighted ICP rubric to every unscored lead, writes a score and a tier into your sheet, and notes which criterion earned or lost the points.
Most lead scoring dies the same way. Someone builds a rubric in a spreadsheet, scores fifty leads by hand over two afternoons, and never scores the fifty-first. Two months later the sales team is back to working the list top to bottom, and the rubric is a tab nobody opens. The rubric was never the hard part — applying it, every week, to whatever came in, is.
Sai takes a weighted ICP rubric you define once and applies it to every lead in your sheet that does not yet have a score. It researches each company from its website, LinkedIn, and public news, awards points criterion by criterion, and writes back a score out of 100, a tier, the single criterion that mattered most, and anything it could not verify. It runs on a schedule, so a lead that arrived on Thursday is scored by Monday morning.
Weights are the whole mechanism: they force you to say out loud which attribute actually predicts a closed deal. Start from the set below and move the numbers to match your own won-deal pattern.
Five columns per lead — Score, Tier, Top Reason, Missing Info, Scored On — written next to the row that already exists. Nothing is moved, nothing is deleted, and no lead is scored on a criterion Sai could not evidence: an unverifiable attribute scores zero and is named in Missing Info, so a low score is always explainable in a pipeline review.
Only unscored leads are picked up, so the run stays cheap and your manual overrides survive. Set the Rescore flag on a row and it is re-evaluated against the current rubric — which matters when you change a weight, because the whole list can then be brought onto the new rubric in one run. Scored On records which version of your thinking produced each number.
After a quarter of closed deals, compare tiers against outcomes: if C-tier leads are closing at the same rate as B-tier, a weight is wrong, not the lead. Pair this with a scheduled analysis of your sheet to see which criterion actually separates won from lost, and keep the inputs flowing with a weekly prospect list that appends to the same sheet. If early-stage companies score well for you, add newly launched startups every week, and check the targeting itself with recurring market research. Scoring frameworks such as BANT and MEDDIC are a reasonable starting point if you have no won-deal history yet.