Expansion Revenue Prediction
“Which accounts will upgrade?”
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A real-world example
Which accounts will upgrade?
Top SaaS companies achieve 130%+ net revenue retention through expansion. But CSMs waste 60% of upsell outreach on accounts that are not ready to expand, creating friction. A $200M ARR company where expansion outreach has 5% success rate leaves $30M in accessible expansion revenue on the table. The expansion signal sits in the intersection of seat utilization rates, feature adoption velocity, billing trends, and champion engagement patterns.
How KumoRFM solves this
Graph-learned product intelligence across your entire account base
Kumo connects accounts, users, usage metrics, billing data, and feature adoption sequences into a graph where expansion signals propagate through the account network. It learns that accounts at 85%+ seat utilization, where 3+ departments have adopted the API integration, and where the finance admin has viewed the billing portal 4+ times in 30 days expand at 10x the base rate. The model captures cross-account expansion patterns: when similar-sized accounts in the same vertical expand, peer accounts follow within a quarter.
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 | name | arr | seats_purchased | plan_tier |
|---|---|---|---|---|
| ACC101 | DataFlow Inc | $96,000 | 40 | Growth |
| ACC102 | RetailCo | $180,000 | 80 | Enterprise |
| ACC103 | Startup Labs | $18,000 | 10 | Starter |
USERS
| user_id | account_id | department | last_login | role |
|---|---|---|---|---|
| U101 | ACC101 | Engineering | 2025-03-02 | Admin |
| U102 | ACC101 | Marketing | 2025-03-01 | User |
| U103 | ACC101 | Sales | 2025-03-02 | User |
USAGE_METRICS
| metric_id | account_id | month | active_seats | api_calls |
|---|---|---|---|---|
| UM01 | ACC101 | 2025-02 | 38 | 45,000 |
| UM02 | ACC101 | 2025-01 | 35 | 38,000 |
| UM03 | ACC102 | 2025-02 | 62 | 12,000 |
BILLING
| billing_id | account_id | date | amount | overage |
|---|---|---|---|---|
| BL01 | ACC101 | 2025-02-01 | $8,000 | $450 |
| BL02 | ACC101 | 2025-01-01 | $8,000 | $200 |
| BL03 | ACC102 | 2025-02-01 | $15,000 | $0 |
FEATURE_ADOPTION
| adoption_id | account_id | feature | first_used | monthly_events |
|---|---|---|---|---|
| FA01 | ACC101 | API v2 | 2025-01-10 | 15,000 |
| FA02 | ACC101 | SSO | 2024-12-01 | 800 |
| FA03 | ACC101 | Custom reports | 2025-02-15 | 120 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(ACCOUNTS.EXPANSION_EVENT, 0, 90, days) FOR EACH ACCOUNTS.ACCOUNT_ID WHERE ACCOUNTS.PLAN_TIER != 'Enterprise'
Prediction output
Every entity gets a score, updated continuously
| ACCOUNT_ID | CURRENT_ARR | SEAT_UTIL | EXPANSION_PROB_90D |
|---|---|---|---|
| ACC101 | $96,000 | 95% | 0.82 |
| ACC102 | $180,000 | 78% | 0.28 |
| ACC103 | $18,000 | 60% | 0.05 |
Understand why
Every prediction includes feature attributions — no black boxes
Account ACC101 -- DataFlow Inc, $96K ARR
Predicted: 82% expansion probability within 90 days
Top contributing features
Seat utilization trend
95% and rising
29% attribution
Cross-department adoption
3 departments active
23% attribution
API usage growth (MoM)
+18% increase
20% attribution
Overage charges (last 60d)
$650 total
16% attribution
Peer account expansion rate
4 of 6 expanded
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 $200M ARR SaaS company that focuses expansion outreach on the 20% of accounts most likely to upgrade captures $30M in additional revenue with 10x better conversion rates. Kumo identifies expansion-ready accounts through seat utilization, cross-department adoption, and peer-account signals that manual health scoring misses.
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.
