Revenue Intelligence
Focus on accounts that will actually buy
Dynamic scoring that combines firmographic fit, buying signals, engagement history, and first-party data into a single prioritization layer. Your team works the accounts that matter—automatically.
1
Static scores decay instantly
You built a lead score based on title + company size + email opens. It worked for a month. Then Marketing started gaming it, Sales stopped trusting it, and nobody updated the model. Now it's furniture.
2
Fit without intent is noise
A VP at a Fortune 500 company sounds great—until you realize they're not in-market, have no budget, and just downloaded a whitepaper by accident. High ICP fit doesn't mean high purchase probability.
3
You're scoring fragments, not accounts
Your lead score looks at individual contacts. But B2B buying involves committees. An SDR at your target account clicking an email isn't a buying signal. Three people from the same account hitting your pricing page in a week is.
Lantern scores accounts across four dimensions: How well do they match your ICP? Are they showing buying signals? How engaged are they with your brand? What's their actual behavior tell you? One number, complete picture.
Aggregate engagement across everyone at an account. Three people from Acme Corp visited your site this week? The account is heating up, even if each individual visit looks minor.
Product usage, support tickets, previous purchases, contract dates, NPS scores—your first-party signals are often the best predictors. Lantern unifies them with third-party signals for complete scoring context.
Lantern tracks which scored accounts actually convert. The model learns what "good" looks like for your business and adjusts weights continuously. Your scoring gets smarter every month.
How closely does this account match your ideal customer profile?
Firmographics (industry, size, geography)
Technographics (tech stack, integrations)
Growth indicators (funding, hiring, revenue)
Organizational structure
Is this account actively researching solutions in your category?
Third-party intent (Bombora, G2, etc.)
Website behavior (pages visited, frequency)
Content engagement (downloads, webinars)
Search behavior (category keywords)
How actively is this account engaging with your brand?
Email engagement (opens, clicks, replies)
Ad interactions (impressions, clicks)
Event participation (webinars, conferences)
Sales touchpoints (calls, meetings, demos)
What does their actual behavior tell you?
Product usage (for existing customers)
Support interactions (tickets, NPS)
Contract data (renewal dates, expansion)
Historical patterns (previous purchases)
- Build a lead score in Marketo
- Base it on email opens and form fills
- Manually update weights quarterly
- Watch Sales ignore it within months
- Score leads, not accounts
- No connection to actual revenue data
- Score accounts, not just leads
- Combine fit + intent + engagement + behavior
- Auto-learn from closed-won data
- First-party and third-party unified
- Continuous optimization
- Scores that Sales actually trusts
Use cases
2x
meetings per SDR, 40% faster speed-to-lead
VP of Sales at a mid-market SaaS company
3x
connect rate on outbound, 60% shorter sales cycle
VP of Sales at a mid-market company
+25%
increase in net revenue retention
Head of Customer Success at an enterprise company
Jennifer Walsh
VP of Revenue Operations
Scores sync bi-directionally. Update a field in Salesforce, Lantern recalculates. Account heats up in Lantern, your CRM reflects it instantly.
See how unified account scoring helps your team focus on the accounts that will actually buy.
