Upsell Prediction
“Which subscribers will upgrade their plan?”
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A real-world example
Which subscribers will upgrade their plan?
Carriers send 50M+ upgrade offers monthly with 2-3% conversion rates. Each wasted offer costs $0.50-$2.00 in delivery and discounting, totaling $25M-$100M in wasted marketing spend annually. Worse, poorly timed offers train subscribers to wait for discounts. The upgrade signal is in the intersection of usage patterns, network experience, social influence from contacts on higher plans, and historical response behavior.
How KumoRFM solves this
Graph-learned network intelligence across your entire subscriber base
Kumo connects subscribers, plans, usage patterns, offer history, and response data into a graph where upgrade propensity propagates through communication networks. It learns that subscribers at 85%+ data utilization whose top contacts recently upgraded and who browsed the carrier app's plan comparison page convert at 12x the base rate. The model also learns offer fatigue: subscribers shown 3+ declined offers in 90 days respond 60% less to the next one.
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 | plan | tenure_months | monthly_arpu |
|---|---|---|---|
| SUB201 | Basic 5GB | 14 | $35 |
| SUB202 | Unlimited | 28 | $65 |
| SUB203 | Basic 5GB | 6 | $35 |
PLANS
| plan_id | name | monthly_cost | data_gb | tier |
|---|---|---|---|---|
| PLN01 | Basic 5GB | $35 | 5 | Entry |
| PLN02 | Unlimited | $65 | Unlimited | Mid |
| PLN03 | Unlimited Plus | $75 | Unlimited | Premium |
USAGE
| usage_id | subscriber_id | month | data_gb_used | overage_charges |
|---|---|---|---|---|
| U201 | SUB201 | 2025-02 | 4.7 | $0 |
| U202 | SUB201 | 2025-01 | 4.9 | $5.00 |
| U203 | SUB203 | 2025-02 | 2.1 | $0 |
OFFERS
| offer_id | subscriber_id | offer_type | sent_date | channel |
|---|---|---|---|---|
| OFF01 | SUB201 | Upgrade to Unlimited | 2025-02-01 | SMS |
| OFF02 | SUB201 | Upgrade to Unlimited | 2025-01-15 | |
| OFF03 | SUB203 | Add hotspot | 2025-02-10 | App push |
RESPONSES
| response_id | offer_id | action | timestamp |
|---|---|---|---|
| RSP01 | OFF01 | Opened | 2025-02-01 |
| RSP02 | OFF02 | Ignored | |
| RSP03 | OFF03 | Clicked | 2025-02-10 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(SUBSCRIBERS.PLAN_UPGRADE, 0, 30, days) FOR EACH SUBSCRIBERS.SUBSCRIBER_ID WHERE SUBSCRIBERS.PLAN != 'Unlimited Plus'
Prediction output
Every entity gets a score, updated continuously
| SUBSCRIBER_ID | CURRENT_PLAN | BEST_OFFER | UPGRADE_PROB_30D |
|---|---|---|---|
| SUB201 | Basic 5GB | Unlimited | 0.72 |
| SUB202 | Unlimited | Unlimited Plus | 0.15 |
| SUB203 | Basic 5GB | Unlimited | 0.08 |
Understand why
Every prediction includes feature attributions — no black boxes
Subscriber SUB201 -- Basic 5GB, 14-month tenure
Predicted: 72% upgrade probability within 30 days
Top contributing features
Data utilization (3-month avg)
94% of plan
30% attribution
Top contacts on higher plans
4 of 5 on Unlimited
22% attribution
Overage charges (last 90d)
$15.00 total
19% attribution
App plan-comparison page visits
3 in last 14d
17% attribution
Offer response history
1 opened, 1 ignored
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 30M-subscriber carrier that improves upsell targeting from 3% to 8% conversion generates $140M in incremental annual ARPU. Kumo identifies subscribers whose usage patterns, social network influence, and offer response history signal genuine upgrade intent, eliminating wasted offers that train subscribers to wait for discounts.
Related use cases
Explore more telecom 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.
