Lead Scoring
“Which leads will convert to a paying customer in the next 30 days?”
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A real-world example
Which leads will convert to a paying customer in the next 30 days?
Sales teams waste 60% of their time on leads that never convert. Current lead scoring uses demographic rules — company size plus job title — missing behavioral and relational signals entirely. The result is bloated pipelines, burned-out SDRs, and missed quota. If you could score leads by actual conversion probability, reps focus on the 20% of leads that drive 80% of pipeline, shortening sales cycles and dramatically improving win rates.
How KumoRFM solves this
Relational intelligence for smarter acquisition
Kumo builds a heterogeneous graph across your CRM, product usage, support interactions, and marketing touchpoints. Instead of hand-crafted rules, the graph neural network automatically discovers signals like 'leads whose colleagues at the same company already purchased' or 'leads who viewed pricing pages after a webinar.' The model learns from every relationship in your data — not just flat lead attributes — delivering conversion probabilities that are 3x more accurate than rule-based scoring, with zero feature engineering.
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
LEADS
| lead_id | company | industry | source | signup_date |
|---|---|---|---|---|
| L001 | Acme Corp | Finance | webinar | 2025-11-01 |
| L002 | Beta Ltd | Retail | organic | 2025-11-03 |
| L003 | Gamma Inc | Healthcare | paid_search | 2025-11-05 |
| L004 | Delta Co | Finance | referral | 2025-11-07 |
ACTIVITIES
| activity_id | lead_id | activity_type | page | timestamp |
|---|---|---|---|---|
| A101 | L001 | page_view | /pricing | 2025-11-02 |
| A102 | L001 | demo_request | /demo | 2025-11-04 |
| A103 | L002 | page_view | /blog | 2025-11-04 |
| A104 | L003 | page_view | /pricing | 2025-11-06 |
| A105 | L004 | email_click | /case-study | 2025-11-08 |
ORDERS
| order_id | lead_id | amount | timestamp |
|---|---|---|---|
| O501 | L001 | $24,000 | 2025-11-15 |
| O502 | L004 | $18,500 | 2025-11-20 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(ORDERS.*, 0, 30, days) > 0 FOR EACH LEADS.LEAD_ID
Prediction output
Every entity gets a score, updated continuously
| LEAD_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| L001 | 2025-11-01 | True | 0.89 |
| L002 | 2025-11-03 | False | 0.12 |
| L003 | 2025-11-05 | True | 0.74 |
| L004 | 2025-11-07 | True | 0.81 |
Understand why
Every prediction includes feature attributions — no black boxes
Lead L001 — Acme Corp
Predicted: True (89% probability)
Top contributing features
Viewed pricing page within 3 days of signup
True
34% attribution
Requested demo after webinar attendance
True
27% attribution
Company industry — Finance
Finance
18% attribution
Lead source — webinar
webinar
13% attribution
Connected to 2 existing customers in same industry
2 connections
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-scored leads convert 3.2x more often than rule-based scoring. Sales reps reclaim 60% of prospecting time by focusing on leads the model identifies as high-probability converters.
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.
