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 DeskLog-level feedsCTV incrementality
Quick answer

Programmatic and CTV data engineering: The Trade Desk and DSP log-level ingestion, CTV incrementality measurement, and reach and frequency across screens.

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

  • 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

  1. Ingest DSP reporting first to establish a reliable delivery and cost baseline

  2. Add log-level processing where it exists, since frequency and overlap live there

  3. Model deduplicated reach across screens rather than summing per-channel reach

  4. Design and read out a geo holdout to establish incremental effect

Typical stack

The Trade Desk APIDV360Amazon DSPSnowflakeBigQuerySparkdbt

Frequently asked questions

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.

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 that 30% of your budget reached people already saturated at frequency 15, and that finding usually pays for the work.

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 MMM viable, which is the most reliable way to value upper-funnel spend.

Go deeper

Measure your programmatic spend

Tell us which DSPs you run and whether you get log-level feeds, and we will scope the measurement build.

Start a project

Proof from our work

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