One model across DSPs
The Trade Desk, DV360 and Amazon DSP reporting in one place.
Programmatic & CTV
Programmatic and CTV data, including the log-level detail.
Programmatic and connected TV generate the most data and the least clarity. We ingest DSP reporting and log-level feeds, model reach and frequency across screens, and design the measurement that tells you whether upper-funnel spend actually moved anything.
- The Trade Desk
- Log-level feeds
- CTV incrementality
What you get
Evidence for upper-funnel spend.
Reach counted once
Deduplicated reach and frequency across CTV, display, audio and social.
Incrementality you can defend
Geo holdouts, since click attribution does not apply to CTV.
Where impressions really ran
Inventory and supply path analysis, with what each cost.
The problem
Impressions are not the same as evidence.
CTV has no click, so the entire measurement approach used for search and social simply does not transfer. DSP interfaces report delivery well and outcomes poorly. Log-level data holds the real answers about frequency, overlap and inventory quality, but arrives as very large raw files that most teams never process. The predictable result is that a growing share of budget is defended with reach and impression counts rather than any evidence of incremental effect.
What we build for programmatic
What's included.
- DSP reporting ingestion from The Trade Desk, DV360 and Amazon DSP into one model
- Log-level and raw event feed processing where the DSP provides it
- Deduplicated reach and frequency across CTV, display, audio and social
- Inventory and supply path analysis: where impressions actually ran and what they cost
- CTV incrementality design using geo holdouts, since click-based attribution does not apply
- Cross-screen reporting that puts upper-funnel delivery next to lower-funnel outcomes
How we work
How we run it.
Step 1
Ingest DSP reporting first to establish a reliable delivery and cost baseline
Step 2
Add log-level processing where it exists, since frequency and overlap live there
Step 3
Model deduplicated reach across screens rather than summing per-channel reach
Step 4
Design and read out a geo holdout to establish incremental effect
What we use
Tools, platforms and methods.
- The Trade Desk API
- DV360
- Amazon DSP
- Snowflake
- BigQuery
- Spark
- dbt
FAQ
Common questions.
How do you measure CTV without clicks?
With experiments rather than attribution. Geo holdouts are the workhorse: withhold CTV spend in matched markets and measure the difference in outcomes. Where a clean room is available, exposure-level analysis adds detail. What does not work is treating an incidental site visit after an ad impression as a conversion path, which is how most CTV attribution overstates itself.
Is log-level data worth the processing cost?
If you are spending seriously on programmatic, yes. It is the only place true cross-campaign frequency, audience overlap and supply path detail exist. Aggregated reporting cannot tell you, for example, that 30% of your budget reached people already saturated at a frequency of 15, and that kind of finding usually pays for the work.
Can you combine programmatic with our search and social data?
That is normally the point. Everything lands in the same warehouse and model, so reach and frequency at the top can be read against conversions at the bottom. It also makes marketing mix modeling viable, which is the most reliable way to value upper-funnel spend.
Go deeper
Guides from our engineers.
Guide
Building a Custom Marketing Mix Model (MMM): A Practical Guide
What marketing mix modeling is, when it beats attribution, how open-source frameworks like Meridian and Robyn work, and what it takes to build an MMM that earns trust.
Read the guideGuide
Retail Media Data Engineering: Unifying Amazon, Walmart, and Instacart Reporting
Retail media spend is spread across Amazon, Walmart, Instacart, and more, each with its own dashboard. Here's how to build one data model across all of them.
Read the guideRelated
Related services.
Marketing mix modeling
Meridian or Robyn models fed by your own pipeline, to measure what each channel contributes.
Learn moreAttribution data
Your attribution platform in the same warehouse as spend and sales, on one agreed model.
Learn moreAmazon Ads & SP-API
Amazon Ads API, SP-API and AMC pipelines, dashboards and AI agents.
Learn moreDatabricks lakehouse
Delta Lake pipelines, log-level processing and ML on the same platform.
Learn moreMeasure your programmatic spend.
Tell us which DSPs you run and whether you get log-level feeds, and we will scope the measurement build.