You have 500 accounts in your pipeline. Which ones does your sales team call first?
Without account scoring, you're guessing. Sales calls random accounts. Some are tire-kickers. Some are perfect fits but dormant. Some are high-fit and actively buying, but sales doesn't know to prioritize them.
Account scoring fixes this. It combines fit (does this account match your ICP?) with engagement (are they actively buying?). The result: a ranked list of accounts worth your team's time right now.
This guide walks you through building a scoring model that works.
What Account Scoring Is (And Isn't)
Account scoring answers: "Of all the accounts in our pipeline, which ones should we work on right now?"
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Account scoring is NOT lead scoring (which ranks individual contacts). It's higher-level: "Account ABC, across all the buying committee members, is this a good fit and are they actively buying?"
Account scoring usually combines two dimensions:
Fit score (40%): Does this account match your ICP? - Company size - Industry - Growth characteristics - Technology stack fit - Revenue fit
Engagement score (60%): Are they showing buying signals? - Recent website visits - Email opens and clicks - Content downloads - Demo requests - Sales conversations - Recent funding or hiring announcements
Building Your Fit Score
Fit score is about whether this account is the right target.
Start with your ICP criteria. Assign points.
Example fit scoring (out of 100):
- Company size matches ICP range: 20 points (headcount or revenue)
- Industry matches your vertical: 20 points
- Growth rate 20%+ YoY: 15 points
- Funding or revenue level: 15 points
- Technology stack compatible (e.g., Salesforce user): 15 points
- Decision-maker reachable: 15 points
Total: 100 points
This scoring can be mostly automated. Pull data from ZoomInfo, Apollo, or your CRM.
Example account: - Company size: 250 employees (matches target of 200-500): 20 points - Industry: HR Tech (your sweet spot): 20 points - Growth: 35% YoY (exceeds 20% target): 15 points - Recent Series B funding: 15 points - Customer of Salesforce (compatible): 15 points - CEO and VP People reachable via LinkedIn: 15 points
Fit score: 100/100
Another account: - Company size: 50 employees (below target): 5 points - Industry: MarTech (adjacent, not core): 10 points - Growth: 15% YoY (below target): 8 points - Pre-Series A (too early stage): 5 points - Custom stack, not Salesforce: 5 points - Contacts not findable: 0 points
Fit score: 33/100
The first account is a better fit.
---Building Your Engagement Score
Engagement score is about whether they're actively buying right now.
Define engagement events and assign points.
Example engagement scoring (out of 100):
- Website visits in last 30 days: 0-15 points (more visits = higher score)
- High-intent pages visited (pricing, demo, comparison): 0-20 points
- Email open in last 14 days: 10 points
- Email click in last 14 days: 15 points
- Content download in last 30 days: 10 points
- Demo requested: 30 points
- Sales call scheduled: 40 points
- Recent funding announcement: 15 points
- Job posting for VP Sales or CRO: 15 points
- Active opportunity in CRM: 50 points
Total: 100+ points (normalize to 100)
Example account: - 5 website visits in last 30 days: 10 points - Visited pricing and comparison pages: 20 points - Opened marketing emails: 10 points - Clicked an email: 15 points - Downloaded a case study: 10 points - No demo scheduled: 0 points
Engagement score: 65/100
Another account: - No website visits: 0 points - No email engagement: 0 points - Active opportunity in CRM (created 2 weeks ago): 50 points
Engagement score: 50/100
Combining Fit + Engagement
Create a composite score: (Fit score * 0.4) + (Engagement score * 0.6)
Account A: - Fit: 100, Engagement: 65 - Composite: (100 * 0.4) + (65 * 0.6) = 40 + 39 = 79
Account B: - Fit: 100, Engagement: 50 - Composite: (100 * 0.4) + (50 * 0.6) = 40 + 30 = 70
Account C: - Fit: 33, Engagement: 80 - Composite: (33 * 0.4) + (80 * 0.6) = 13 + 48 = 61
Account A is your top priority. Account B is close. Account C is lower priority (poor fit despite high engagement).
Tiering Based on Score
Create tiers to guide sales prioritization:
- 80-100: Tier 1 (Call immediately). Hot accounts: great fit and actively buying.
- 60-79: Tier 2 (Call this week). Good fit, some engagement. Worth pursuing.
- 40-59: Tier 3 (Call if time). Medium fit or minimal engagement. Lower priority.
- Below 40: Don't call yet. Poor fit or no engagement signal.
In your CRM, flag accounts by tier. Sales reps see their queue organized by priority.
---Skip the manual work
Abmatic AI runs targets, sequences, ads, meetings, and attribution autonomously. One platform replaces 9 tools.
See the demo →Automating Your Scoring
Manual scoring doesn't scale. Automate with tools:
Native CRM scoring: Salesforce, HubSpot, and Pipedrive have built-in account scoring. Set up rules and thresholds.
Dedicated tools: 6sense, Demandbase, and Apollo score accounts automatically based on your criteria.
Basic automation: If you use a CRM and email platform, you can export data monthly and score in a spreadsheet. Not perfect, but workable for small teams.
Refreshing Your Scores
Engagement changes weekly. Update scores at least weekly, ideally daily.
An account scoring 40 today (low priority) might score 85 after their CEO visits your pricing page and opens 3 emails.
Set up automation so scores update in real time. Sales sees the change and knows to call.
Common Scoring Mistakes
Mistake 1: Fit-only scoring. You score accounts on fit (ICP match) and ignore engagement. A perfect-fit account with zero interest gets treated the same as one actively buying. Engagement matters.
Mistake 2: Engagement-only scoring. An account with high engagement but poor fit. They'll never close. Wasting sales time. Balance fit and engagement.
Mistake 3: Static thresholds. You score an account 75 and it stays 75 for months. Engagement changes fast. Update scores frequently.
Mistake 4: No sales buy-in. You score accounts. Sales doesn't trust the scoring. They ignore it and call their own list. Involve sales in defining criteria. They'll use what they helped build.
Mistake 5: No feedback loop. Sales calls a high-scoring account. It's a tire-kicker. No one updates the scoring model. Next week, you recommend the same bad account again. Add feedback: when a scored account closes (or churns), update your criteria.
---Example: Implementing a Simple Score
Month 1: Define fit criteria and engagement events (as above).
Month 2: Score your entire account database manually or with a tool.
Month 3: Organize sales queue by score tier. Sales calls Tier 1 accounts first.
Month 4: Track which scored accounts close. Refine your criteria based on closers.
Month 5+: Automate scoring updates weekly. Continuously improve.
Next: Track What Scores Close
After 3-6 months of using scoring, analyze which scores correlate with closed deals.
If most of your closes come from 70-85 score range, adjust your tiers. Maybe Tier 1 should be 70-100.
Scoring improves over time as you gather data on what actually converts in your market.
