Cross-sell / Upsell
“For each customer, which product tier or add-on will they upgrade to in the next 30 days?”
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A real-world example
For each customer, which product tier or add-on will they upgrade to in the next 30 days?
Traditional cross-sell models rely on hand-crafted features like tenure or plan type — missing the rich behavioral signals buried in usage patterns, support interactions, and peer upgrade paths. The result: blanket upgrade offers that annoy 80% of customers and convert less than 2%.
How KumoRFM solves this
Relational intelligence for optimal actions
Kumo builds a heterogeneous graph connecting customers, products, upgrades, and usage events. Its graph neural network learns upgrade propensity from the full relational context — which peers upgraded, what usage thresholds preceded upgrades, and which product combinations co-occur. The ranked recommendation surfaces the top 5 most likely upgrades per customer, enabling hyper-targeted offers that convert at 3-5x higher rates.
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
CUSTOMERS
| customer_id | name | current_plan | tenure_months |
|---|---|---|---|
| C-1001 | Acme Corp | Pro | 18 |
| C-1002 | Bolt Inc | Starter | 6 |
| C-1003 | Cipher Ltd | Pro | 24 |
| C-1004 | Delta Co | Enterprise | 36 |
| C-1005 | Echo Labs | Starter | 3 |
UPGRADES
| upgrade_id | customer_id | product_id | amount | timestamp |
|---|---|---|---|---|
| U-501 | C-1001 | P-20 | $299 | 2025-11-15 |
| U-502 | C-1002 | P-10 | $99 | 2025-12-01 |
| U-503 | C-1003 | P-30 | $599 | 2025-10-20 |
| U-504 | C-1001 | P-25 | $449 | 2026-01-10 |
| U-505 | C-1005 | P-10 | $99 | 2026-02-14 |
PRODUCTS
| product_id | name | tier | price |
|---|---|---|---|
| P-10 | Analytics Add-on | Starter | $99/mo |
| P-20 | Advanced Reporting | Pro | $299/mo |
| P-25 | Team Collaboration | Pro | $449/mo |
| P-30 | Enterprise Suite | Enterprise | $599/mo |
| P-35 | AI Insights Pack | Enterprise | $799/mo |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT LIST_DISTINCT(UPGRADES.PRODUCT_ID, 0, 30, days) RANK TOP 5 FOR EACH CUSTOMERS.CUSTOMER_ID
Prediction output
Every entity gets a score, updated continuously
| CUSTOMER_ID | CLASS | SCORE | TIMESTAMP |
|---|---|---|---|
| C-1001 | P-35 (AI Insights) | 0.87 | 2026-03-12 |
| C-1001 | P-30 (Enterprise Suite) | 0.72 | 2026-03-12 |
| C-1002 | P-20 (Adv. Reporting) | 0.81 | 2026-03-12 |
| C-1002 | P-25 (Team Collab) | 0.64 | 2026-03-12 |
| C-1003 | P-35 (AI Insights) | 0.91 | 2026-03-12 |
| C-1005 | P-20 (Adv. Reporting) | 0.76 | 2026-03-12 |
Understand why
Every prediction includes feature attributions — no black boxes
Customer C-1003 (Cipher Ltd)
Predicted: P-35 — AI Insights Pack (0.91)
Top contributing features
Upgraded twice in 6 months (UPGRADES)
2 upgrades
34% attribution
Current plan = Enterprise (CUSTOMERS)
Enterprise
27% attribution
Peer accounts at same tier adopted P-35 (graph)
68% peer adoption
22% attribution
Tenure > 24 months (CUSTOMERS)
24 months
17% 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: Target the 15% of customers most likely to upgrade — increase cross-sell conversion 3-5x and drive $4-8M in incremental annual revenue per 100K customers.
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.
