What is lead scoring?
Lead scoring is the practice of attaching a number to every lead that summarizes how worth pursuing they are. Two main inputs: fit (do they match ICP — industry, size, role, geography?) and intent (have they shown buying signals — visited pricing, downloaded ToFu, spiked in third-party intent data?). Output is usually a 0–100 score or letter grade (A/B/C/D), with thresholds that gate automation.
In 2026 most modern scoring is AI-assisted — an LLM reads unstructured signals (a LinkedIn post, a job description, a website) and contributes to the score with explainable rationale.
Why it matters
- Reps spend time on the right leads, not whoever's on top of the queue
- Scoring is the contract between marketing and sales (above X = SQL handoff)
- Surfaces the patterns that predict conversion — feeds back into ICP refinement
Score components
- Demographic / firmographic: title, seniority, company size, industry
- Behavioral: site visits, email engagement, content downloads, demo requests
- Intent: third-party signals, hiring activity, tech-stack changes
- Negative scoring: competitor employees, free-mail addresses, role accounts
How TexAu helps
AI Column lets you define a scoring rubric (fit + intent dimensions, weights, thresholds) and apply it to every lead in a table — with a one-line rationale per score so reps trust it and ops can iterate the model.
See it on TexAu
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