Account Scoring
“Which target accounts will generate revenue in the next 90 days?”
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A real-world example
Which target accounts will generate revenue in the next 90 days?
Account-based marketing teams target hundreds of accounts but lack the ability to rank them by actual revenue potential. Static ICP definitions based on employee count and industry miss the dynamic engagement signals that predict conversion. Sales and marketing waste alignment cycles on accounts that were never going to close, while high-intent accounts slip through the cracks unnoticed.
How KumoRFM solves this
Relational intelligence for smarter acquisition
Kumo connects your ACCOUNTS, OPPORTUNITIES, and CONTACTS tables into a single relational graph. The model learns cross-account patterns — like 'accounts whose contacts have engagement scores above 80 and share industry peers that recently closed deals' — automatically. No manual ICP definition required. The result is a continuously updated account score that reflects real buying signals, not stale firmographics.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
ACCOUNTS
| account_id | company_name | industry | employee_count | region |
|---|---|---|---|---|
| A001 | Vertex Financial | Finance | 12,000 | North America |
| A002 | Orbit Retail | Retail | 3,500 | EMEA |
| A003 | Helix Health | Healthcare | 8,200 | North America |
| A004 | Nova Tech | Technology | 1,800 | APAC |
OPPORTUNITIES
| opp_id | account_id | amount | stage | timestamp |
|---|---|---|---|---|
| OP01 | A001 | $120,000 | Negotiation | 2025-10-15 |
| OP02 | A002 | $45,000 | Discovery | 2025-10-20 |
| OP03 | A003 | $89,000 | Proposal | 2025-11-01 |
CONTACTS
| contact_id | account_id | title | engagement_score |
|---|---|---|---|
| C01 | A001 | VP Data Science | 92 |
| C02 | A001 | CTO | 85 |
| C03 | A002 | Director Analytics | 41 |
| C04 | A003 | Chief Risk Officer | 78 |
| C05 | A004 | ML Engineer | 23 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(OPPORTUNITIES.AMOUNT, 0, 90, days) > 0 FOR EACH ACCOUNTS.ACCOUNT_ID
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| A001 | 2025-11-01 | True | 0.93 |
| A002 | 2025-11-01 | False | 0.18 |
| A003 | 2025-11-01 | True | 0.82 |
| A004 | 2025-11-01 | False | 0.09 |
Understand why
Every prediction includes feature attributions — no black boxes
Account A001 — Vertex Financial
Predicted: True (93% probability)
Top contributing features
Two contacts with engagement scores above 80
92, 85
31% attribution
Active opportunity in Negotiation stage
$120K
28% attribution
Industry peers closed 3 deals in last 90 days
3 deals
19% attribution
Employee count > 10,000 (enterprise tier)
12,000
14% attribution
Region — North America
North America
8% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Kumo account scores identify revenue-generating accounts with 2.8x higher precision than static ICP models, letting ABM teams concentrate budget on the accounts most likely to close.
Related use cases
Explore more acquisition use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
