Account scoring is the foundation of ABM. It answers a simple question: which accounts should we focus on?
But “which accounts” depends on what you’re optimizing for. Are you looking for accounts that match your ideal customer profile (fit)? Accounts that are actively looking for solutions like yours (intent)? Accounts that are engaging with your content (engagement)?
The answer is usually: all three.
This guide walks you through building a comprehensive account scoring model that combines fit, intent, and engagement.
Without scoring, your team wastes time chasing bad accounts.
With scoring, you can answer: - Which 50 accounts should sales focus on this month? - Which accounts need nurturing before sales reaches out? - Which accounts are actively in-market and ready to buy? - Which accounts are long-term investments?
Account scoring creates a shared language between marketing and sales. When you say “this account is a 75 (out of 100),” everyone knows what that means.
Fit-based scoring answers: how well does this account match our ideal customer profile?
Components:
How to score:
Create a weighted formula. Example:
Fit Score = (Industry Match * 40) + (Company Size Match * 30) + (Geography Match * 15) + (Tech Stack Match * 15)
Scoring details:
- Industry Match: SaaS companies = 25, fintech = 25, other tech = 15, non-tech = 5. Max 25.
- Company Size Match: 100M-1B revenue = 30, 10-100M = 20, 1-10M = 10. Max 30.
- Geography Match: US West = 10, US East = 8, EMEA = 5, Other = 0. Max 15.
- Tech Stack Match: Uses Salesforce = 8, uses HubSpot = 5, other CRM = 3, no CRM = 0. Max 15.
Total: 100 points max
When to use fit scoring:
Pros: - Easy to calculate and understand - Based on publicly available data (company size, industry, etc.) - Works well for early-stage ABM programs - Good for list cleaning (filter out obviously bad fits)
Cons: - Doesn’t measure buying intent (high-fit accounts might not be in-market) - Static (doesn’t change as accounts progress) - Doesn’t account for engagement or behavior
Intent-based scoring answers: how much buying intent is this account showing?
Components:
How to score:
Create a weighted formula. Example:
Intent Score = (Keyword Intent * 30) + (Content Consumption * 25) + (Website Behavior * 25) + (News Signals * 15) + (Account Activity * 5)
Scoring details:
- Keyword Intent: Searches for "visitor identification" = 30, "ABM" = 25, "B2B marketing" = 15, no searches = 0. Max 30.
- Content Consumption: Read 3+ comparison guides = 25, read 2 guides = 15, read 1 = 10. Max 25.
- Website Behavior: Visited site 5+ times = 25, 3-4 times = 15, 1-2 times = 5. Max 25.
- News Signals: Raised funding recently = 10, hired key role = 8, product launch = 7, no signals = 0. Max 15.
- Account Activity: 3+ touchpoints = 5, 1-2 touchpoints = 3, none = 0. Max 5.
Total: 100 points max
When to use intent scoring:
Pros: - Identifies accounts actively in-market (most likely to buy soon) - Dynamic (changes as accounts show more intent) - Good for both inbound and outbound
Cons: - Requires intent data subscription or internal tracking - Intent is temporary (accounts cool off) - Can miss long-term opportunities (companies with low intent but high fit)
Engagement-based scoring answers: how much engagement is this account showing with us?
Components:
How to score:
Create a weighted formula. Example:
Engagement Score = (Website Behavior * 30) + (Email Engagement * 20) + (Form Fills * 20) + (Sales Response * 20) + (Social Engagement * 10)
Scoring details:
- Website Behavior: 10+ visits = 30, 5-9 visits = 20, 1-4 visits = 10, no visits = 0. Max 30.
- Email Engagement: 50%+ open rate = 20, 25-50% = 15, <25% = 5. Max 20.
- Form Fills: 3+ forms = 20, 2 forms = 15, 1 form = 10, none = 0. Max 20.
- Sales Response: Replied to email = 20, didn't reply = 0. Max 20.
- Social Engagement: Engaged with 3+ posts = 10, 1-2 posts = 5, none = 0. Max 10.
Total: 100 points max
When to use engagement scoring:
Pros: - Based on actual behavior (most predictive of buying intent) - Easy to calculate from your own data (no third-party data needed) - Real-time and dynamic
Cons: - Skews toward companies with active interest (misses early-stage opportunities) - Requires good tracking infrastructure - Doesn’t account for market fit or fundamental ICP alignment
The best approach combines all three: fit, intent, and engagement.
Hybrid Scoring Formula:
Account Score = (Fit Score * 40) + (Intent Score * 35) + (Engagement Score * 25)
This gives:
- Fit the most weight (40%): We want good fundamental fits
- Intent secondary (35%): We want accounts in-market
- Engagement last (25%): Nice to have but less important than fit and intent
Example calculation:
- Account A: Fit 80, Intent 90, Engagement 40
Account Score = (80 * 0.4) + (90 * 0.35) + (40 * 0.25) = 32 + 31.5 + 10 = 73.5
- Account B: Fit 60, Intent 50, Engagement 95
Account Score = (60 * 0.4) + (50 * 0.35) + (95 * 0.25) = 24 + 17.5 + 23.75 = 65.25
Account A scores higher (73.5 vs 65.25) because it has better fit and intent, even though B has higher engagement.
Weights vary by use case:
For outbound ABM: Fit 50%, Intent 30%, Engagement 20% (You’re choosing who to target, so fit matters most)
For inbound ABM: Fit 30%, Intent 35%, Engagement 35% (You’re responding to inbound, so engagement and intent matter more)
For account expansion (retention): Fit 20%, Intent 30%, Engagement 50% (You’re upselling to existing customers, so engagement with new products/services matters most)
Use your account scores to create tiers:
Tier 1: Priority Accounts (Score 75-100) - Characteristics: High fit, high intent, active engagement - Action: Sales calls this month, personalized outreach, demo scheduling - Cadence: Weekly check-ins
Tier 2: Growth Accounts (Score 50-74) - Characteristics: Good fit, moderate intent, some engagement - Action: Marketing nurture campaigns, webinar invitations, content - Cadence: Monthly check-ins
Tier 3: Monitor Accounts (Score 25-49) - Characteristics: Fair fit, low intent or limited engagement - Action: Add to nurture campaigns, re-target with ads, wait for intent signals - Cadence: Quarterly check-ins
Tier 4: Long-Term (Score 0-24) - Characteristics: Low fit or low engagement - Action: Don’t pursue yet, but monitor for future intent - Cadence: Annual review
Step 1: Choose your components
What data do you have access to? What matters for your business?
Build your formula with components you can actually measure and update.
Step 2: Set scoring thresholds
Define when an account moves from one tier to another.
Example: Tier 1 is 75+, Tier 2 is 50-74, etc.
Step 3: Build in HubSpot or your CRM
Create custom properties for each component (fit score, intent score, engagement score, total account score).
Set up workflows to auto-calculate total score from components.
Step 4: Test with historical data
Score your existing customers. Do high-scoring accounts correlate with customers? If not, adjust your formula.
Step 5: Iterate based on feedback
After 30 days, ask sales: do the scores match reality? Are Tier 1 accounts actually good opportunities?
Adjust weights and components based on feedback.
Over-weighting engagement:
Engagement is important, but an account that’s engaging but not a good fit will waste your time.
Under-weighting intent:
A high-fit account with no intent might close in 18 months. A lower-fit account with high intent might close in 2 months.
Balance fit and intent.
Not updating scores:
Scores should change as accounts progress. Set up automatic updates so scores are fresh.
Ignoring sales feedback:
If sales says your Tier 1 accounts are bad fits, listen. Adjust your fit scoring formula.
Too many components:
Keep it simple. 5-7 components is ideal. More than 10 and the model becomes unwieldy.
Month 1: Design your scoring model - Define components (fit, intent, engagement) - Assign weights and point values - Document the logic clearly
Month 2: Build in your CRM - Create custom fields - Set up automatic calculations - Test with historical data
Month 3: Calibrate against reality - Score your existing customers. Do they score high? - Score your lost deals. Do they score low or medium? - Adjust weights based on findings
Month 4: Launch - Score all accounts - Create tier segments - Brief sales and marketing - Monitor feedback
Month 5+: Iterate - Gather sales feedback monthly - Refine weights quarterly - Add or remove components based on what predicts conversion
Example 1: SaaS ABM Platform
ICP: $10M-100M ARR SaaS companies
Fit Score (100 points): - SaaS industry: +40 - Revenue $10-100M: +30 - US-based: +20 - Uses HubSpot or Salesforce: +10
Intent Score (100 points): - Searching “ABM platform” or “visitor identification”: +40 - Visited comparison page: +30 - Downloaded case study: +20 - Visited 5+ pages: +10
Engagement Score (100 points): - Visited site 10+ times: +40 - Opened 3+ emails: +30 - Filled out form: +20 - Responded to LinkedIn message: +10
Total account score: (Fit * 0.4) + (Intent * 0.35) + (Engagement * 0.25)
Account scoring in action: Company A scores 75 (Fit 80, Intent 85, Engagement 60). It’s ready for a sales conversation. Company B scores 45 (Fit 90, Intent 30, Engagement 20). It’s a good fit but not ready. Nurture it.
Example 2: Enterprise SaaS (Long Sales Cycle)
ICP: $100M+ revenue, 1,000+ employees, enterprise vertical
Fit Score (100 points): - Enterprise vertical (financial services, healthcare): +50 - Revenue $100M+: +30 - Global presence: +20
Intent Score (100 points): - 6sense signals high intent: +60 - Article engagement on your website: +20 - Attended webinar: +20
Engagement Score (100 points): - Responded to cold outreach: +60 - Scheduled meeting: +40
Total: (Fit * 0.5) + (Intent * 0.35) + (Engagement * 0.15)
Note: for enterprise, fit is most important (companies are carefully selected). Engagement is less important because it takes longer.
How often should I update account scores?
Fit scores: quarterly (when your ICP changes) Intent scores: weekly (as new intent data comes in) Engagement scores: daily (as new behavior comes in) Overall account scores: daily
Should I use 0-100 scale or 1-10 scale?
0-100 gives more granularity. But 1-10 is simpler. Pick whichever your team understands better.
What if I don’t have intent data?
Skip intent scoring. Use fit and engagement. Add intent scoring later when you have intent data.
How do I incorporate third-party intent data (Bombora)?
Bombora gives you a list of in-market accounts. Create an Intent Score component: if account is on Bombora list = 50 points, if not = 0.
What if multiple scoring models contradict?
Fit says one account is good. Intent says another is hotter. Both are true. Use the hybrid model that weights both. For that moment, prioritize the high-intent account (close sooner). Follow up on the high-fit account later (bigger potential).
How do I explain scoring to sales?
“A score of 75 means this account is likely to close in the next 90 days. A score of 40 means it’s a decent fit but not ready yet. Focus on 75+.”