Account Scoring
A methodology for ranking target accounts based on their likelihood to purchase, using firmographic fit, technographic data, intent signals, and engagement history to prioritize sales and marketing effort.
Account Scoring Prevents You From Chasing the Wrong Companies
Without account scoring, sales teams chase whoever responds first. With account scoring, they chase the accounts most likely to buy and most valuable if they do. The difference is focus — and in enterprise sales, focus is everything.
Building an Account Scoring Model
Start with your best 50 customers. What do they have in common? Company size, industry, technology stack, growth stage, funding history? Those commonalities become your fit criteria. Layer in engagement data (are they interacting with your content?) and intent data (are they researching your category?). Weight the criteria and score every account in your target list.
The Score Determines the Motion
High-score accounts get Tier 1 ABM treatment — personalized outreach, custom content, executive engagement. Medium-score accounts get Tier 2 — targeted campaigns by segment. Low-score accounts get programmatic nurture until their score increases. Your resources are finite. Account scoring allocates them to maximum effect.
Refreshing Scores
Account scores should update dynamically as new signals emerge. A low-score account that suddenly shows intent surge should jump in priority. A high-score account that goes quiet should drop. Set up automated score updates in your CRM or ABM platform — stale scores are worse than no scores.
Frequently Asked Questions
How is account scoring different from lead scoring?
Lead scoring ranks individual people. Account scoring ranks entire companies. In ABM, the account is the unit of analysis — multiple people at the same company might engage at different levels. Account scoring aggregates all signals from everyone at the company into a single account-level score.
What data points should feed account scoring?
ICP fit (company size, industry, revenue, technology stack), engagement (website visits from the account, ad interactions, content consumption), intent (third-party intent signals, G2 activity), and relationship data (existing contacts, past conversations). Weight each category based on which best predicts conversion in your historical data.
