Marketing Attribution
“How much incremental revenue does each marketing touchpoint generate?”
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A real-world example
How much incremental revenue does each marketing touchpoint generate?
Marketing teams spend millions across channels but cannot measure true incremental impact. Last-touch attribution over-credits the final click; multi-touch models spread credit arbitrarily. Without causal uplift measurement, budget allocation is guesswork — high-performing channels get under-funded while vanity channels consume budget with no real conversion lift. CMOs need to know: 'If we turned off paid search, how many conversions would we actually lose?'
How KumoRFM solves this
Relational intelligence for smarter acquisition
Kumo uses counterfactual PQL queries to answer causal questions. The ASSUMING clause simulates a world where a specific touchpoint exists, and the model compares this to the baseline (without that touchpoint). The difference in predicted conversion probability is the true incremental uplift for that channel. This is not correlation-based attribution — it is learned counterfactual reasoning over the full relational graph of users, touchpoints, and conversions.
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
USERS
| user_id | segment | signup_date |
|---|---|---|
| U001 | high_value | 2025-09-01 |
| U002 | mid_value | 2025-09-15 |
| U003 | new_user | 2025-10-01 |
| U004 | high_value | 2025-10-10 |
TOUCHPOINTS
| touch_id | user_id | channel | campaign | timestamp |
|---|---|---|---|---|
| T01 | U001 | paid_search | brand_q4 | 2025-10-20 |
| T02 | U001 | nurture_series | 2025-10-22 | |
| T03 | U002 | organic | — | 2025-10-18 |
| T04 | U003 | paid_search | prospecting | 2025-10-25 |
| T05 | U004 | webinar | demo_day | 2025-10-28 |
CONVERSIONS
| conversion_id | user_id | revenue | timestamp |
|---|---|---|---|
| CV01 | U001 | $8,400 | 2025-11-01 |
| CV02 | U004 | $12,200 | 2025-11-05 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT COUNT(CONVERSIONS.*, 0, 14, days) > 0 FOR EACH USERS.USER_ID ASSUMING COUNT(TOUCHPOINTS.* WHERE TOUCHPOINTS.CHANNEL = 'paid_search', 0, 1, days) > 0
Prediction output
Every entity gets a score, updated continuously
| USER_ID | True_PROB (with) | True_PROB (without) | UPLIFT |
|---|---|---|---|
| U001 | 0.87 | 0.62 | +0.25 |
| U002 | 0.34 | 0.29 | +0.05 |
| U003 | 0.71 | 0.31 | +0.40 |
| U004 | 0.91 | 0.88 | +0.03 |
Understand why
Every prediction includes feature attributions — no black boxes
User U003 — new_user segment
Predicted: +40% uplift from paid_search
Top contributing features
New user with no prior brand awareness
new_user
35% attribution
Paid search was first-ever touchpoint
first touch
28% attribution
Campaign — prospecting (top-of-funnel)
prospecting
17% attribution
No organic or referral touchpoints present
0 organic
12% attribution
Similar new users converted 3x more with paid search
3x lift
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: Counterfactual attribution reveals that paid search drives 40% incremental lift for new users but only 3% for existing high-value customers — enabling precise budget reallocation that can save $2M+ annually in wasted ad spend.
Related use cases
Explore more acquisition 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.
