Lead Scoring
“Which leads will convert to paid?”
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A real-world example
Which leads will convert to paid?
SDR teams spend 65% of their time on leads that will never convert. A SaaS company generating 10,000 MQLs per month with a 3% conversion rate wastes $2.4M annually in SDR labor on dead leads. Traditional lead scoring based on form fills and page views misses the buying signals hidden in multi-contact engagement patterns, firmographic fit, and the temporal sequence of interactions that distinguish real buyers from researchers.
How KumoRFM solves this
Graph-learned product intelligence across your entire account base
Kumo connects leads, contacts, activities, content views, and firmographic data into a graph where buying intent propagates through the company network. It learns that when 3+ contacts from the same account view pricing pages, download the security whitepaper, and attend a webinar within 14 days, that account converts at 15x the base rate. The model captures multi-threaded buying committee behavior, firmographic similarity to recent closers, and engagement velocity that single-contact scoring cannot detect.
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 | source | created_date | status |
|---|---|---|---|---|
| LD001 | Acme Corp | Webinar | 2025-02-15 | MQL |
| LD002 | DataTech | Google Ads | 2025-02-20 | MQL |
| LD003 | MegaRetail | Organic | 2025-03-01 | MQL |
CONTACTS
| contact_id | lead_id | title | department | seniority |
|---|---|---|---|---|
| CT01 | LD001 | VP Engineering | Engineering | VP |
| CT02 | LD001 | CTO | Engineering | C-Level |
| CT03 | LD002 | Data Analyst | Analytics | IC |
ACTIVITIES
| activity_id | contact_id | type | timestamp |
|---|---|---|---|
| ACT01 | CT01 | Demo request | 2025-02-18 |
| ACT02 | CT02 | Pricing page view | 2025-02-19 |
| ACT03 | CT03 | Blog view | 2025-02-22 |
CONTENT_VIEWS
| view_id | contact_id | content_type | title | timestamp |
|---|---|---|---|---|
| CV01 | CT01 | Whitepaper | Security & Compliance | 2025-02-16 |
| CV02 | CT02 | Case study | Enterprise deployment | 2025-02-17 |
| CV03 | CT03 | Blog | Getting started guide | 2025-02-22 |
FIRMOGRAPHICS
| firm_id | lead_id | industry | employees | revenue | tech_stack |
|---|---|---|---|---|---|
| FG01 | LD001 | Technology | 2500 | $500M | Snowflake, AWS |
| FG02 | LD002 | Analytics | 45 | $5M | PostgreSQL |
| FG03 | LD003 | Retail | 12000 | $2B | Azure, Databricks |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(LEADS.STATUS = 'Closed Won', 0, 90, days) FOR EACH LEADS.LEAD_ID WHERE LEADS.STATUS = 'MQL'
Prediction output
Every entity gets a score, updated continuously
| LEAD_ID | COMPANY | CONTACTS | CONVERSION_PROB_90D |
|---|---|---|---|
| LD001 | Acme Corp | 2 (VP + CTO) | 0.76 |
| LD002 | DataTech | 1 (IC) | 0.09 |
| LD003 | MegaRetail | 1 (Dir) | 0.34 |
Understand why
Every prediction includes feature attributions — no black boxes
Lead LD001 -- Acme Corp, Technology, 2,500 employees
Predicted: 76% conversion probability within 90 days
Top contributing features
Multi-contact engagement
2 contacts, VP + C-Level
31% attribution
High-intent content viewed
Security + Case study
24% attribution
Firmographic fit score
92% match to ICP
19% attribution
Demo request within 3 days
Yes
14% attribution
Tech stack compatibility
Snowflake (key integration)
12% 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: A SaaS company generating 10,000 MQLs per month that routes SDRs to the top-20% leads doubles conversion rates from 3% to 6%, adding $4.8M in new ARR annually. Kumo detects multi-threaded buying committee engagement and firmographic fit signals that single-contact lead scores miss entirely.
Related use cases
Explore more B2B SaaS 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.
