Replenishment Intelligence
“For each subscriber, will their largest single order exceed $200 in the next 30 days?”
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
By submitting, you accept the Terms and Privacy Policy.
Loved by data scientists, ML engineers & CXOs at
A real-world example
For each subscriber, will their largest single order exceed $200 in the next 30 days?
MAX focuses on the peak value, not the total or average. A subscriber likely to make a single large purchase should receive premium upgrade offers before they buy at full price. Traditional models predict average spend or total volume, missing the high-value spikes that signal upgrade readiness. A subscriber whose average order is $45 but who is about to place a $280 order represents a premium conversion opportunity — but only if you detect the spike before it happens. Missing these signals means losing upsell revenue and letting customers pay full price when a targeted offer would have driven loyalty.
How KumoRFM solves this
Relational intelligence for revenue growth
Kumo's PQL uses MAX aggregation to predict the peak single-order value, not the sum or average. The graph transformer learns from subscriber metadata, order history, and product category patterns to identify subscribers approaching a large purchase. The model discovers that Subscriber SUB-2201 (plan: Premium, recent orders trending from $120 to $180) has an 84% probability of exceeding $200 in their next peak order, signaling the perfect moment for a premium bundle offer.
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
SUBSCRIBERS
| subscriber_id | name | plan | signup_date |
|---|---|---|---|
| SUB-2201 | A. Chen | Premium | 2023-09-01 |
| SUB-2202 | K. Patel | Standard | 2024-04-15 |
| SUB-2203 | M. Torres | Basic | 2024-11-20 |
ORDERS
| order_id | subscriber_id | amount | category | timestamp |
|---|---|---|---|---|
| ORD-7701 | SUB-2201 | $180 | Premium Box | 2025-01-02 |
| ORD-7702 | SUB-2201 | $120 | Add-on | 2025-01-08 |
| ORD-7703 | SUB-2202 | $45 | Standard Box | 2025-01-05 |
| ORD-7704 | SUB-2203 | $22 | Basic Box | 2025-01-10 |
| ORD-7705 | SUB-2201 | $165 | Premium Box | 2025-01-15 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT MAX(ORDERS.AMOUNT, 0, 30, days) > 200 FOR EACH SUBSCRIBERS.SUBSCRIBER_ID
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | TIMESTAMP | TARGET_PRED | True_PROB |
|---|---|---|---|
| SUB-2201 | 2025-02-01 | True | 0.84 |
| SUB-2202 | 2025-02-01 | False | 0.18 |
| SUB-2203 | 2025-02-01 | False | 0.06 |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber SUB-2201
Predicted: True (84% probability of $200+ peak order)
Top contributing features
Peak order value trend (60d)
$120 → $180
38% attribution
Premium category purchase ratio
67% premium
25% attribution
Subscription plan
Premium
17% attribution
Order frequency (30d)
3 orders
13% attribution
Account tenure
16 months
7% 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's MAX aggregation predicts peak single-order values — not averages or totals — identifying subscribers ready for premium upgrades before they buy at full price. Targeted offers at the right moment drive loyalty and incremental revenue.
Related use cases
Explore more growth 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.
