Market Expansion
“Which geographic regions will generate the most new customer signups in the next 90 days?”
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A real-world example
Which geographic regions will generate the most new customer signups in the next 90 days?
Market expansion decisions are typically driven by manual research, gut instinct, and lagging indicators like last quarter's signup numbers. By the time a region shows strong trailing metrics, competitors have already moved in. Teams need forward-looking signals that combine marketing spend efficiency, organic signup momentum, and demographic fit — but these signals live in separate tables and systems that traditional models cannot connect. Entering the wrong market wastes $2-5M in go-to-market costs; delaying entry into the right market means losing first-mover advantage worth 3-5x that amount.
How KumoRFM solves this
Relational intelligence for revenue growth
Kumo's graph transformers learn from the relational structure connecting regions, signups, marketing spend, and channel effectiveness to produce region-level signup forecasts. The model discovers that Region R-204 (Southeast Asia) has accelerating organic signups despite modest marketing spend, while Region R-201 (Western Europe) shows diminishing returns from increased paid spend — insights invisible in any single table but clear in the relational graph.
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
REGIONS
| region_id | region_name | country | population |
|---|---|---|---|
| R-201 | Western Europe | Germany | 83M |
| R-202 | Northeast US | United States | 56M |
| R-203 | Latin America | Brazil | 214M |
| R-204 | Southeast Asia | Indonesia | 275M |
SIGNUPS
| signup_id | region_id | customer_id | channel | timestamp |
|---|---|---|---|---|
| SU-601 | R-201 | C-7701 | Organic | 2025-01-02 |
| SU-602 | R-204 | C-7702 | Organic | 2025-01-04 |
| SU-603 | R-202 | C-7703 | Paid | 2025-01-06 |
| SU-604 | R-204 | C-7704 | Referral | 2025-01-08 |
MARKETING_SPEND
| spend_id | region_id | channel | amount | timestamp |
|---|---|---|---|---|
| MS-101 | R-201 | Paid Search | $45,000 | 2025-01-01 |
| MS-102 | R-202 | Social Ads | $32,000 | 2025-01-01 |
| MS-103 | R-204 | Content | $8,500 | 2025-01-01 |
| MS-104 | R-203 | Paid Search | $22,000 | 2025-01-01 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(SIGNUPS.*, 0, 90, days) FOR EACH REGIONS.REGION_ID
Prediction output
Every entity gets a score, updated continuously
| REGION_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| R-201 | 2025-02-01 | 1,240 |
| R-202 | 2025-02-01 | 2,180 |
| R-203 | 2025-02-01 | 3,450 |
| R-204 | 2025-02-01 | 5,820 |
Understand why
Every prediction includes feature attributions — no black boxes
Region R-204 (Southeast Asia)
Predicted: 5,820 new signups in 90 days
Top contributing features
Organic signup acceleration (30d)
+142% MoM
36% attribution
Referral-to-signup conversion rate
34%
24% attribution
Marketing cost per signup
$1.46
19% attribution
Population in target demographic
48M
13% attribution
Similar-region growth trajectory
Matches India 2023
8% 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 predicts regional signup volume from relational marketing, demographic, and behavioral data — helping growth teams prioritize expansion into markets with the highest forward-looking potential, not just the best trailing metrics.
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.
