Lead Scoring Model Template

Total score = Fit score + Behavior score. Fit is who they are. Behavior is what they did.

Fit caps at [50] points. Behavior caps at [50] points. Max possible score is [100].

Fit answers: does this person look like a customer who buys and stays?

Behavior answers: are they showing buying intent right now?

Negative points are allowed. A bad fit signal can pull the total down.

Review the model every [quarter] using closed-won and closed-lost data.

Owner of this model: [name]. Last updated: [date].

Company size [target range, e.g. 11-200 employees]: +[15]

Company size [too small, e.g. 1-3 employees]: -[10]

Industry is a target vertical [list yours]: +[10]

Job title is a decision maker [list titles]: +[15]

Job title is a non-buyer [student, intern, job seeker]: -[15]

Region you sell to [list countries or states]: +[10]

Free email domain instead of a company domain: -[5]

Replied to your AI agent on [website / WhatsApp / Telegram / Instagram]: +[10]

Asked about pricing or a plan in chat: +[15]

Booked a demo or call: +[20]

Visited the pricing page in the last [7] days: +[10]

Started a free trial or signup: +[20]

No activity for [30]+ days: -[10]. Unsubscribed or asked to stop messaging: -[20].

Cap rule: the positive points above add up to more than [50] when several fire at once, so cap the total behavior score at [50] no matter how many positive signals are present. Negative points still apply on top of the capped total.

Hot ([70]-100): strong fit and active intent. Route to a human now.

Warm ([40]-[69]): good fit or some intent. Keep nurturing, watch for a trigger.

Cold ([0]-[39]): weak fit or gone quiet. Stay in automated nurture only.

Disqualified: any -[20] signal present (unsubscribe or stop request) disqualifies the lead regardless of total score, OR the total falls below [0]. Check this rule first, before the hot, warm, or cold bands, so a high-fit lead who unsubscribes never lands in Warm. Then stop active outreach and suppress from sequences.

Tie-break rule: when two leads share a score, the one with the more recent behavior wins.

Set these numbers to your own pipeline, then recheck after [30] days of real data.

When a lead reaches Hot: assign to [rep or team], notify in [channel], first touch within [15 minutes].

When a lead reaches Warm: add to [nurture sequence], set a task to review in [3] days.

When a lead stays Cold for [30] days: move to [long-term newsletter], remove from active queue.

When a lead is Disqualified: tag [Disqualified], clear the owner field (set owner to unassigned), suppress from outreach.

When a Warm lead books a demo: auto-bump to Hot and route to a human immediately.

Round-robin hot leads across [rep names] so no one gets overloaded.

Log every status change with a timestamp so you can audit response times.

Create field: Fit Score (number).

Create field: Behavior Score (number).

Create field: Total Score (formula = Fit Score + Behavior Score).

Create field: Lead Status (dropdown: Hot, Warm, Cold, Disqualified).

Automation: when Total Score updates, recalculate Lead Status from the thresholds above.

Automation: when Lead Status becomes Hot, trigger the Hot routing rule.

Decay job: every [night], expire positive behavior points older than [30] days so stale visits or demos stop counting. This is separate from the 'No activity for [30]+ days: -[10]' penalty: decay removes old positive points, the penalty subtracts a flat [10]. Apply one or the other to a given signal, never both, so a quiet lead is not penalized twice.