You have 200 target accounts. Your team can realistically pursue 30-40 with depth. How do you know which ones are actually close to buying vs. just browsing?
Most teams guess. They look at open rates and click rates and assume engagement equals buying intent. The result is sales teams chasing accounts that will never close while ignoring the ones that are actually ready to buy.
An engagement scoring framework fixes this. It helps you separate noise from signal so you can focus your most expensive resource (sales) on accounts that are actually in market.
Here's how to build one that works.
Why Standard Lead Scoring Breaks in ABM
Traditional lead scoring looks at individual behaviors: opened email, clicked link, attended webinar. Good for inbound. Useless for ABM.
In ABM, you care about account-level momentum. One person opening your email doesn't mean the account is interested. But three different people from the same account visiting your pricing page in the same week? That's a signal.
Account engagement scoring looks at the buying committee, not the individual. It asks: Is the whole account moving toward a buying decision?
The Four Signals That Actually Matter
Before you build a complex model, start simple. These four signals predict buying intent in 90% of cases:
1. Buying Committee Engagement (40% weight) Are multiple people from the target account engaging with you? - Account executives, architects, business leaders engaging = 20 points - Procurement and legal starting conversations = 10 points - One person engaging repeatedly = 5 points - No engagement = 0 points
Why this matters: Real buying decisions involve committees. One person clicking your emails doesn't mean the deal is moving. Three people from different functions engaging means the account is discussing you internally.
2. Behavioral Velocity (30% weight) Is engagement increasing or decreasing week over week? - Engagement trending up (more visits, clicks, opens this week than last) = 20 points - Stable high engagement = 15 points - Stable low engagement = 5 points - Engagement trending down = 0 points
Why this matters: The direction matters as much as the absolute level. An account that went from one visit to five visits is more interesting than an account stuck at three visits per week for months.
3. Content Consumption Depth (20% weight) Are they consuming educational vs. bottom-of-funnel content? - Viewing pricing, case studies, technical docs = 15 points - Attending demos or sales calls = 10 points - Consuming educational content only = 5 points - No content engagement = 0 points
Why this matters: Early-stage browsers consume educational content. Serious buyers dig into pricing, case studies, and technical specifics. Track where they're spending time.
4. Intent Signal Timing (10% weight) Are they showing buying signals at the right moment? - Recent job posting for the roles your solution serves = 10 points - Recent funding or leadership announcement = 5 points - Third-party intent data confirming account interest = 5 points - No external signals = 0 points
Why this matters: Context matters. An account that just hired a Director of Marketing is more likely to buy marketing software than one that hasn't hired for that role in two years.
Building Your Scoring Model in Practice
Start by assigning points to each signal type. Then track four accounts through your model:
Account A: SaaS company, 250 employees, Series C - 3 people from buying committee engaging (20) - Engagement up 40% week-over-week (20) - Visiting pricing page, downloading case studies (15) - No recent hiring signals (0) - Total Score: 55/100 (High Priority)
Account B: Software company, 180 employees, late-stage - 1 person opening emails, no other engagement (5) - Engagement flat month-over-month (5) - Occasional educational content consumption (5) - No external signals (0) - Total Score: 15/100 (Lower Priority)
Account C: Mid-market SaaS, 400 employees, growth-stage - 2 people engaging, one is CFO (20) - Engagement up 25% (20) - Viewing case studies, scheduling demo (15) - Just hired VP of Finance (10) - Total Score: 65/100 (Highest Priority)
Account C and Account A should get your sales team's attention this month. Account B gets nurturing plays until engagement increases.
Step 1: Define Your Scoring Thresholds
Decide what scores trigger what actions:
- 70+: Sales outreach immediately. These are hot.
- 50-70: Sales cadence. Include in your 30-day engagement plan.
- 30-49: Marketing nurture. Send them educational content, retargeting. Check back in 30 days.
- Below 30: Lower priority. Inbound marketing only.
Your sales team should check daily for accounts that crossed into the 70+ threshold.
Skip the manual work
Abmatic AI runs targets, sequences, ads, meetings, and attribution autonomously. One platform replaces 9 tools.
See the demo →Step 2: Automate Data Collection
This only works if you're tracking activity automatically. Manual data entry kills adoption.
Set up your CRM/ABM platform to automatically log: - Website visits by named account - Email opens and clicks - Content downloads - Webinar attendance - Calls and meetings with sales - Job postings (via LinkedIn or scraping) - Funding and news alerts
The moment data entry becomes manual, it breaks.
Step 3: Review Accounts Monthly, Adjust Quarterly
Run a monthly review. Pull your top 50 accounts by score. Ask your sales team:
"Do these rankings make sense? Are we chasing hot accounts or missing obvious ones?"
Often you'll learn: - "That score of 15 company actually has a champion in procurement who told us they're evaluating us." - "That 60-score account is a competitor plant trying to steal our pricing."
Use that feedback to adjust weightings. Maybe buying committee engagement should be 50% of the score for you, not 40%. Maybe your sales team cares more about recent job postings than you do.
Adjust quarterly, not daily. You want stability in your model.
Step 4: Track Scoring Accuracy
Once you've been scoring for 90 days, measure it.
- Which accounts with 70+ scores closed deals?
- Which accounts with low scores turned into surprise wins?
- Which accounts with high scores never moved?
Calculate your "scoring accuracy" this way: - High-scoring accounts that closed: (wins / total high-scoring accounts) - Should be 40%+ if your model is working
If it's below 20%, your weights are wrong. Adjust.
Key Takeaways
- Account engagement matters more than individual engagement. Track buying committee movement, not just opens.
- Velocity is a leading indicator. Accounts accelerating toward you are more interesting than stagnant accounts.
- Consumption depth reveals intent. Prospects digging into pricing are further along than those just reading educational content.
- Simple models are better than complex ones. Four signals beat twenty. You'll actually use it.
- Adjust based on your data. Run the model, learn from misses, improve quarterly.
The goal isn't perfect prediction. It's separating warm accounts from cold ones so your sales team uses their time on the accounts most likely to close this quarter.
Abmatic AI helps teams build and maintain engagement scoring models that predict account-level buying intent. See how you can prioritize your pipeline with account scoring.
