Sales Prioritization
“Which open opportunities will close in the next 30 days?”
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A real-world example
Which open opportunities will close in the next 30 days?
Sales reps spend 60% of their time on opportunities that never close. CRM stage-based forecasting is subjective and lags reality — a deal marked 'Negotiation' may have gone cold weeks ago. Without an objective signal, quota attainment depends on gut feel and manager intuition.
How KumoRFM solves this
Relational intelligence for optimal actions
Kumo connects opportunities, activities, accounts, and historical close data into a relational graph. The model learns close probability from activity velocity, engagement recency, account-level signals, and peer deal trajectories — producing a daily-updated probability score that tells reps exactly which deals deserve their next call.
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
OPPORTUNITIES
| opp_id | account_id | amount | stage | created_date |
|---|---|---|---|---|
| OPP-101 | ACC-50 | $125K | Negotiation | 2025-12-01 |
| OPP-102 | ACC-51 | $85K | Proposal | 2026-01-15 |
| OPP-103 | ACC-52 | $320K | Discovery | 2026-02-01 |
| OPP-104 | ACC-53 | $45K | Negotiation | 2025-11-10 |
| OPP-105 | ACC-54 | $200K | Proposal | 2026-01-28 |
ACTIVITIES
| activity_id | opp_id | type | outcome | timestamp |
|---|---|---|---|---|
| ACT-801 | OPP-101 | call | positive | 2026-03-05 |
| ACT-802 | OPP-101 | replied | 2026-03-08 | |
| ACT-803 | OPP-102 | demo | attended | 2026-02-25 |
| ACT-804 | OPP-103 | call | no answer | 2026-03-01 |
| ACT-805 | OPP-104 | no reply | 2026-02-10 |
CLOSED_WON
| close_id | opp_id | amount | timestamp |
|---|---|---|---|
| CW-901 | OPP-088 | $95K | 2026-01-20 |
| CW-902 | OPP-091 | $210K | 2026-02-05 |
| CW-903 | OPP-095 | $150K | 2026-02-18 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(CLOSED_WON.*, 0, 30, days) > 0 FOR EACH OPPORTUNITIES.OPP_ID
Prediction output
Every entity gets a score, updated continuously
| OPP_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| OPP-101 | 2026-03-12 | True | 0.88 |
| OPP-102 | 2026-03-12 | True | 0.72 |
| OPP-103 | 2026-03-12 | False | 0.19 |
| OPP-104 | 2026-03-12 | False | 0.11 |
| OPP-105 | 2026-03-12 | True | 0.65 |
Understand why
Every prediction includes feature attributions — no black boxes
Opportunity OPP-101 (Acme, $125K)
Predicted: True — will close (0.88)
Top contributing features
2 positive activities in last 7 days (ACTIVITIES)
call + email reply
38% attribution
Stage = Negotiation, 100 days in pipeline (OPPORTUNITIES)
Negotiation
25% attribution
Account ACC-50 closed 2 prior deals (graph)
2 prior wins
22% attribution
Deal size in typical close range (OPPORTUNITIES)
$125K
15% 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: Focus reps on the 30% of pipeline most likely to close. Increase win rates by 15-20% and accelerate deal velocity by 25%, driving $5-10M in incremental closed-won revenue per quarter.
Related use cases
Explore more next-best-action 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.
