sales_ops · saas · workflow
How Granicus Doubled Win Rates with AI-Powered Account Scoring
Granicus's account scoring model, in place since 2017, had grown stale — it was static, used only 5-10 variables, and did not leverage modern AI or machine learning, resulting in decreased sales team engagement and a narrowing gap between high- and low-target accounts.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Multi-source data ingestion
The model incorporates over 50 different variables including CRM data, website information, and federal agency data.
Tools used
Pursuit
Outcome
The new AI-powered scoring model produced a 200% increase in win rates for high-target accounts, revealed that 25-30% of pipeline efforts had been directed at low win-rate accounts enabling better resource allocation, and drove adoption across sales, marketing, and executive teams.
What failed first
The existing static model with only 5-10 variables was no longer effective, causing decreased sales team engagement and eroding scoring differentiation between high- and low-target accounts.
Results
Volume200%
Cost replaced25-30%
Grounding & classification
Source type: vendor customer story
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