Bid Optimization
“What should we bid for this impression?”
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A real-world example
What should we bid for this impression?
DSPs bid on billions of impressions daily using models that treat each auction independently. They miss cross-auction patterns: how competitive pressure shifts by time of day, which publisher-advertiser combinations yield outsized returns, and when budget pacing should accelerate or brake. A DSP managing $500M in annual spend that improves bid efficiency by 8% saves $40M in wasted impressions.
How KumoRFM solves this
Graph-powered intelligence for advertising
Kumo connects auctions, bids, impressions, conversions, and budgets into a temporal graph. The model learns bid-to-outcome relationships across the full auction ecosystem, capturing competitive dynamics and budget pacing constraints. PQL expresses the optimal bid as a regression target conditioned on conversion probability and remaining budget.
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
AUCTIONS
| auction_id | publisher_id | floor_price | timestamp |
|---|---|---|---|
| AUC001 | PUB01 | $0.80 | 2025-03-01 08:00 |
| AUC002 | PUB02 | $1.20 | 2025-03-01 08:05 |
| AUC003 | PUB03 | $0.45 | 2025-03-01 08:10 |
BIDS
| bid_id | auction_id | campaign_id | bid_amount | won |
|---|---|---|---|---|
| BID101 | AUC001 | CMP01 | $1.05 | True |
| BID102 | AUC002 | CMP02 | $1.50 | False |
| BID103 | AUC003 | CMP01 | $0.60 | True |
IMPRESSIONS
| impression_id | bid_id | user_id | ad_id |
|---|---|---|---|
| IMP601 | BID101 | U001 | A100 |
| IMP602 | BID103 | U003 | A102 |
CONVERSIONS
| conversion_id | impression_id | value | timestamp |
|---|---|---|---|
| CVR301 | IMP601 | $25.00 | 2025-03-01 09:30 |
BUDGETS
| campaign_id | daily_budget | spent_today | pacing |
|---|---|---|---|
| CMP01 | $50,000 | $12,400 | Ahead |
| CMP02 | $30,000 | $8,200 | On-track |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT AVG(CONVERSIONS.value, 0, 24, hours) FOR EACH AUCTIONS.auction_id
Prediction output
Every entity gets a score, updated continuously
| AUCTION_ID | FLOOR_PRICE | OPTIMAL_BID | EXP_CONVERSION_VALUE |
|---|---|---|---|
| AUC001 | $0.80 | $1.12 | $22.50 |
| AUC002 | $1.20 | $0.00 | $3.10 |
| AUC003 | $0.45 | $0.68 | $18.40 |
Understand why
Every prediction includes feature attributions — no black boxes
Auction AUC001 -- PUB01 at 08:00
Predicted: Optimal bid: $1.12 (expected conversion value: $22.50)
Top contributing features
Publisher historical conversion rate
4.2%
29% attribution
User segment match to campaign target
High
24% attribution
Time-of-day conversion pattern
Morning peak
19% attribution
Campaign budget pacing status
Ahead by 8%
16% attribution
Competitive bid density this hour
Medium
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 DSP managing $500M in annual ad spend saves $40M by optimizing bids with Kumo's graph-learned auction dynamics. Cross-auction signals like budget pacing, publisher quality, and competitive density produce bids that flat models systematically get wrong.
Related use cases
Explore more ad tech 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.
