These articles cover connected TV, ACR viewing data and the measurement designs that depend on them. They explain panel stability, household matching and how to test incrementality before a campaign scales. The CTV and ACR guide lists the questions measurement teams ask in an RFP.
An addressable TV audience is built in five passes: write the audience logic, resolve identity to the household, map households to inventory you can actually buy, activate without losing keys, and validate reach against the file you started with.
, 7 min read
A sourced comparison of eleven smart TV ACR data providers, from Inscape, Samsung Ads and LG Ad Solutions to Samba TV, Nielsen and GSDSI, sorted by what each one actually sells: an ad platform, a licensed feed, or measurement.
, 13 min read
Ghost bids and PSA controls both try to build the same counterfactual, but they fail differently. A measurement scientist's guide to which control-group design to use when, and the bias each one carries.
, 9 min read
Share of wallet by retailer is a per-customer spend ratio, not a survey guess. This guide shows how to fix the denominator, avoid recall bias, and check the number against rank.
, 10 min read
Dwell time measures how long a device stays inside a place, and it moves before sales do. This guide explains how to build a dwell-time signal that reads as a leading indicator of revenue, not a lagging report.
, 9 min read
The third-party cookie did not die on schedule, but the addressable web already moved on. Here is what the cookieless stack actually looks like in 2026, and where cookieless audiences are built and activated now.
, 11 min read
A frequency cap you cannot validate is a wish, not a control. Here is how to measure real cross-screen exposure across CTV, desktop, and mobile, and how to tell when the cap is actually holding.
, 9 min read
Connected TV promises incremental reach, but most CTV measurement counts exposure and calls it lift. Here are the experiment designs that hold up: ghost bids, geo holdouts, and identity-anchored control groups.
, 10 min read
A practical 2026 diligence guide for buyers evaluating bidstream-derived data: source paths, consent evidence, field minimization, sensitive categories, retention, and activation limits.
, 12 min read
How advertisers design CTV measurement tests that connect ACR exposure, household identity, foot traffic, and transaction outcomes without overclaiming attribution.
, 12 min read
Clean rooms are the default post-cookie activation and measurement surface. What a buyer actually receives, and what they don't, depends on computation boundaries, output restrictions, and match-key economics that aren't in the marketing deck.
, 16 min read
Retail measurement in the privacy-safe era stacks in-store foot traffic, card-spend panels, and CTV attribution into a coherent signal chain. Where each signal is operator-grade, where panels overlap, and the measurement questions the MRC-grade accreditation frame actually enforces.
, 12 min read
ACR is the only signal that ties bid decisions to what actually played on the television screen. How it enters a DSP, what it replaces, where it struggles, and the three measurement applications that actually matter.
, 12 min read
How privacy-first advertisers measure across CTV, mobile, web, and in-store without relying on third-party cookies or unscoped device IDs.
, 12 min read
Connected TV measurement depends on automatic content recognition. Here's what a monthly CTV ID panel can, and can't: tell advertisers about reach, frequency, and duplication against linear.
, 12 min read
Post-cookie audience reach does not collapse to a single replacement: it fragments into first-party CRM, clean rooms, authenticated identity, and contextual. Here's the practitioner's map.
, 12 min read
Connected TV advertising spend is surging, but proving ROI remains the industry's biggest challenge. Here's how deterministic cross-channel data solves the attribution gap.
, 12 min read
Google, Meta, and Amazon each measure their own channels. If you want a unified view of how CTV, mobile, and in-store work together, you need an independent data layer.
, 12 min read
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