How do I build a lead-scoring model with iDBQuery?
Ask iDBQuery 'Which leads look most like the ones that became customers?' It compares open leads against traits of past converters across your CRM data, returns a ranked, scored list, and cites each lead's score to the source attributes and history behind it.
Lead scoring ranks prospects by how likely they are to convert, so sales spends time on the best ones. Instead of a rigid points system buried in your CRM, iDBQuery lets you score leads directly against what actually converted in your history.
- You'd ask iDBQuery: 'Compare closed-won deals from the last year on industry, company size, source, and engagement, then rank current open leads by how closely they match those winning traits.'
- It analyses converters, weighs the distinguishing attributes, and returns a scored, ranked lead list with the reasons.
- Each score is cited to its source rows, so a rep can see exactly which attributes and activities pushed a lead up the list.
Because it is transparent, this beats a black-box score: the SQL and the contributing signals are visible, so RevOps can trust and refine it. Follow-ups work naturally: 'Only show leads scored high that no rep has touched in 14 days.' Since engagement data, firmographics, and deal outcomes often live in separate systems, iDBQuery's one live model joins them with no pipeline. Save the ranked list as a live report that refreshes daily, and let the Analyst agent surface which single trait most separates winners from losers, backed by the underlying records.
Updated 2026-06-22