Account Scoring Agent

Scores every account based on fit, intent, and engagement—so your team focuses on accounts that will actually convert.

The Problem

The Problem

1

Your team treats all accounts the same.

Without scores, reps cherry-pick based on gut feel or work accounts alphabetically. High-potential accounts sit untouched while reps chase dead ends.

2

Static scores decay instantly.

You built a score in Marketo based on company size and email opens. It worked for a month. Now nobody trusts it because it doesn't reflect who's actually in-market.

3

Fit without intent is noise.

A Fortune 500 account looks great on paper—until you realize they're not researching solutions, have no budget, and won't buy for 18 months.

What it does

What it does

This agent scores every account in your CRM based on ICP fit, intent signals, and engagement data. Scores update automatically as new signals come in. High-scoring accounts surface to the top of your team's queue.

This agent scores every account in your CRM based on ICP fit, intent signals, and engagement data. Scores update automatically as new signals come in. High-scoring accounts surface to the top of your team's queue.

How it helps

How it helps

Your reps stop guessing which accounts to work. Marketing stops passing over accounts that "look small" but are actually in-market. Pipeline becomes predictable because you're focusing on accounts with the highest probability to close—not the biggest logos.

Your reps stop guessing which accounts to work. Marketing stops passing over accounts that "look small" but are actually in-market. Pipeline becomes predictable because you're focusing on accounts with the highest probability to close—not the biggest logos.

How it works

How it works

How it works

Define your model

Set the criteria that matter: firmographics, technographics, intent signals, engagement thresholds, and weights for each.

Connect your data

Score calculated

Continuous updates

Scores delivered

Define your model

Set the criteria that matter: firmographics, technographics, intent signals, engagement thresholds, and weights for each.

Connect your data

Score calculated

Continuous updates

Scores delivered

Define your model

Set the criteria that matter: firmographics, technographics, intent signals, engagement thresholds, and weights for each.

Connect your data

Score calculated

Continuous updates

Scores delivered

Example

Example

Scenario

Two accounts in your CRM—one looks better on paper, one is actually in-market.

What happens

Agent evaluates both accounts across fit, intent, and engagement:

Output

CRM Updated:

2x

2x

increase in rep productivity (working right accounts)

increase in rep productivity (working right accounts)

85%

85%

accuracy in predicting closed-won

accuracy in predicting closed-won

40%

40%

shorter sales

cycles

shorter sales

cycles

Commonly used with

Commonly used with

Lead Routing Agent

Auto-assign champion companies to right rep

Intent Alert

Agent

Trigger score recalculation when intent spikes

Web Intent

Agent

Website activity feeds into engagement score

The future of revenue is here

The future of revenue is here

Start a free trial to see how Lantern can

unlock revenue from your existing customers.

USE CASES

Revenue Team

Marketing Team

Customer Success

PRICING

Pricing

RESOURCES

Blog

About Lantern

Status

Support

© LANTERN 2025

Terms

Privacy

Linkedin

USE CASES

Revenue Team

Marketing Team

Customer Success

PRICING

Pricing

RESOURCES

Blog

About Lantern

Status

Support

© LANTERN 2025

Terms

Privacy

Linkedin

USE CASES

Revenue Team

Marketing Team

Customer Success

PRICING

Pricing

RESOURCES

Blog

About Lantern

Status

Support

© LANTERN 2025

Terms

Privacy

Linkedin