Marketing Budget Allocation
“How much revenue will each marketing campaign generate over the next 30 days?”
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A real-world example
How much revenue will each marketing campaign generate over the next 30 days?
Marketing teams allocate budgets based on last-touch attribution and historical ROAS, missing the relational signals that drive true incrementality. 30–40% of ad spend is typically wasted on campaigns that would have converted anyway. Accurate revenue-per-campaign predictions let you reallocate millions to the channels that actually drive incremental revenue — but only if you understand the audience overlap, channel saturation, and customer lifetime value context behind each campaign.
How KumoRFM solves this
Relational intelligence for every forecast
Kumo connects campaigns to conversions, customers, segments, and channel histories in a single relational graph. Instead of scoring each campaign on its own last-touch ROAS, Kumo learns that Campaign C-301's audience overlaps 60% with high-LTV customers who would convert organically, while Campaign C-450 reaches an untapped segment with genuine incremental lift. The graph captures channel saturation, creative fatigue, and customer journey context that flat attribution models cannot see.
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
CAMPAIGNS
| campaign_id | campaign_name | channel | budget | start_date |
|---|---|---|---|---|
| C-301 | Fall Retargeting | Display | $50,000 | 2025-09-01 |
| C-302 | Brand Search Q4 | Paid Search | $120,000 | 2025-10-01 |
| C-450 | New Segment Push | Social | $35,000 | 2025-09-15 |
CONVERSIONS
| conversion_id | campaign_id | customer_id | revenue | timestamp |
|---|---|---|---|---|
| CVR-9001 | C-301 | CUST-882 | $124.50 | 2025-09-18 |
| CVR-9002 | C-302 | CUST-1105 | $89.00 | 2025-10-03 |
| CVR-9003 | C-450 | CUST-2040 | $215.00 | 2025-09-20 |
CUSTOMERS
| customer_id | segment | ltv_tier | signup_date |
|---|---|---|---|
| CUST-882 | Returning | Gold | 2023-03-12 |
| CUST-1105 | Returning | Silver | 2024-01-08 |
| CUST-2040 | New | Bronze | 2025-09-14 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(CONVERSIONS.REVENUE, 0, 30, days) FOR EACH CAMPAIGNS.CAMPAIGN_ID
Prediction output
Every entity gets a score, updated continuously
| CAMPAIGN_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| C-301 | 2025-10-01 | $142K |
| C-302 | 2025-10-01 | $38K |
| C-450 | 2025-10-01 | $215K |
Understand why
Every prediction includes feature attributions — no black boxes
Campaign C-450 (New Segment Push)
Predicted: $215K revenue in next 30 days
Top contributing features
Audience overlap with high-LTV segment
12%
30% attribution
Channel saturation (Social)
Low
25% attribution
Campaign recency (fresh audience)
5 days
20% attribution
Creative engagement rate
4.8%
15% attribution
Customer segment growth
+22%
10% 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: Redirect 30–40% of wasted ad spend to truly incremental campaigns — turning the same budget into millions more in revenue.
Related use cases
Explore more forecasting 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.
