What Is an Identity Graph in Streaming TV Advertising?

An identity graph is a database that links different data points about the same person or household — email address, streaming device IDs, IP address, purchase history — across sources and platforms. In CTV advertising, it's the technology that connects a household's streaming devices to their email address and web activity, enabling ad targeting that doesn't rely on cookies. Your CRM is already your best targeting asset on streaming TV. Most brands haven't connected it — and the identity graph is what makes that connection possible.

What data does an identity graph connect, and how is it built?

An identity graph is built by aggregating data points that can be linked to the same household from multiple sources: email addresses from retail accounts and loyalty programs, streaming device IDs from connected TVs and set-top boxes, IP addresses from home networks, authenticated login data from streaming services, and purchase and transaction histories from retail and e-commerce platforms. Each of these is a node; the graph connects nodes that belong to the same household into a unified record.

Two matching methods power the connections. Deterministic matching starts from a confirmed identifier — typically an email address — and links it to device IDs through authenticated logins. If the same email was used to register a Roku account and sign up for a streaming service, the graph can connect that email to the streaming device with high confidence. Probabilistic matching uses behavioral signals — the same IP address appearing across multiple devices, similar browsing patterns, time-of-day activity — to infer that two data points belong to the same household. Deterministic matches are more reliable; probabilistic matches extend reach to households where confirmed identifiers aren't available.

The practical result: an identity graph covers a large portion of streaming TV households by linking the data that already exists across platforms, without requiring any individual to share new information. Accuracy varies by data source quality and recency — a graph built on recent, authenticated first-party data is more accurate than one built primarily on probabilistic inference.

What is the difference between an identity graph and cookies for ad targeting?

Cookies live in a browser and are tied to a single device. They expire, get blocked by browser settings or ad blockers, and don't travel across devices or into native app environments. An identity graph is device-agnostic: it links the same household across the laptop where someone visited your website, the phone where they opened your email, and the smart TV where they stream at night. The targeting follows the household, not the browser session.

This distinction matters most for CTV because streaming apps are native applications, not browsers. There's no cookie environment in a Hulu or Peacock app — a CTV ad server can't read or write browser cookies, so any CTV targeting system that doesn't use an identity graph is working with demographic proxies (age, income, geography) rather than first-party data. Identity graphs are the infrastructure that makes first-party data targeting possible on streaming TV.

The other difference is durability. A third-party cookie can be deleted in seconds. An identity record built from authenticated logins, purchase histories, and device registrations is more stable — it doesn't disappear when someone clears their browser cache. For performance marketers, this means CRM-based CTV audiences built on identity graphs behave more predictably than cookie-based retargeting audiences, which erode as cookies expire or get blocked.

How does CTV advertising use an identity graph for targeting?

At targeting time, the CTV platform queries the identity graph with your audience data — a hashed email list from your CRM, or the IP addresses captured by your site pixel — and the graph returns matched streaming device IDs. Those device IDs receive your ad in their next streaming session. The match happens before delivery, so the viewer never shares new data at the moment of serving; the graph has already connected what's known.

Three targeting applications use this mechanism. Retargeting matches recent website visitors via the IP address your pixel captured to streaming households on the same network. CRM audience targeting matches your Klaviyo or Shopify segments via email to streaming device IDs in the graph — the non-purchaser list, the lapsed-customer segment, the high-LTV buyer cohort. Lookalike expansion identifies households in the graph that pattern-match against your best customers, extending reach beyond your known audience to new households that share similar characteristics.

Your CRM is already your best targeting asset on streaming TV. The Klaviyo list you've built over years — non-purchasers, lapsed customers by LTV, email subscribers who haven't converted — maps directly to streaming TV households through an identity graph. What most brands are missing isn't the audience. It's the connection to the channel.

On Vibe.co, a self-serve streaming TV platform with dedicated account support, the native Klaviyo sync connects directly to the identity graph at campaign launch — no CSV export, no manual audience upload, no agency middleman. The Identity Intelligence targeting layer receives the Klaviyo segment in real time: you configure the audience in Klaviyo, it flows to CTV targeting, and you're in the platform adjusting bids and creative the same day. That direct access to identity-graph-powered targeting — without needing a DSP seat or a managed service — is the actual differentiator, not the size of the graph itself. Branded Bills, a custom headwear and apparel brand, used Klaviyo IP matching on Vibe to retarget warm audiences across premium streaming channels: 311% ROAS, a peak retargeting day of 2,209% ROAS, and $0.33 cost per session.

How do you connect your own customer data to a CTV identity graph?

The starting point is your CRM. Connect your Klaviyo or Shopify account and define the segments you want to target: non-purchasers, lapsed customers past a certain LTV threshold, email subscribers who haven't bought. These segments sync to the identity graph and match against streaming device IDs. The audience in your CRM becomes the audience on CTV without a separate data upload workflow.

Purchaser suppression works the same way as Meta exclusion audiences: your all-time purchaser list syncs as an exclusion, so CTV impressions go only to households that haven't converted yet. Lookalike expansion builds from your best customers outward — the graph identifies streaming households that share characteristics with your highest-LTV buyers and extends reach beyond the CRM list.

Shinesty, a DTC underwear and apparel brand, synced 4M+ email non-purchasers via Klaviyo as their CTV audience — suppressing all-time purchasers to ensure impressions went only to households that hadn't bought yet. The result: 70% of Vibe-driven purchases were net-new customers, validated through Northbeam's native integration with Vibe at campaign launch. The graph-matched audience delivered precision at scale because the segmentation was built from a decade of CRM data, not demographic filters — and because Northbeam's Clicks + Deterministic Views model attributed the CTV conversions in the same dashboard as Meta and Google from day one.

How do you know if identity graph targeting is working?

View-through attribution connects the match to the outcome. The same household that matched for targeting is tracked through conversion within the attribution window — typically 7 days — without requiring a click. The conversion is credited to CTV when it occurs within that window for a matched household. The attribution window setup guide covers how to configure this in Northbeam or Triple Whale so CTV conversions appear in the same dashboard as paid social and search.

The more rigorous validation is incrementality: run a holdout against a suppressed CTV audience to confirm the graph-matched households are converting above the baseline, not just claiming credit from organic or programmatic-driven traffic. The CTV retargeting ROI guide covers holdout setup; the How to Measure True Incrementality for Streaming TV Ads guide goes deeper on methodology.

Search uses intent signals — what someone typed into Google. Social uses the logged-in social graph — who someone follows and engages with. TV uses the identity graph — where that same household watches at night, matched to the purchase history and email behavior you already know. All three point at the same household, and all three are measurable in the same stack. For the implementation detail on how the identity graph connects your site visitors specifically, the Can Streaming TV Ads Target Your Website Visitors? guide covers the IP and deterministic matching mechanics. For the broader channel picture, the full-funnel streaming TV strategy guide maps how identity graph targeting fits across prospecting and retargeting.

FAQ

What is an identity graph in streaming TV advertising?

An identity graph is a database that links different data points about the same household — email address, streaming device IDs, IP address, purchase history — across sources and platforms. In streaming TV advertising, it's the infrastructure that enables first-party data targeting without cookies. A CTV platform queries the graph with your audience data (a hashed email list or pixel IP logs) and returns matched streaming device IDs, which then receive your ad in their next streaming session.

How does an identity graph work for streaming TV ad targeting?

The CTV platform matches your audience data — your Klaviyo email list, your Shopify purchaser file, your site pixel IP logs — against an identity graph that links email addresses and IP addresses to streaming device IDs. The match returns a set of streaming device IDs associated with your target households. Those devices receive your ad when the household next opens a streaming app. The match happens before delivery; no new data is shared at the moment of serving.

Which streaming TV platforms support direct Klaviyo integration for identity graph targeting?

Vibe's native Klaviyo integration syncs directly to the identity graph at campaign launch — Klaviyo segments flow into Vibe's Identity Intelligence targeting layer in real time without a CSV export or manual upload. Non-purchasers, lapsed customers by LTV, and email subscribers who haven't converted become CTV audiences the same day you configure them in Klaviyo. Branded Bills used this integration to run Klaviyo IP-matched retargeting on Vibe: 311% ROAS, 2,209% peak retargeting ROAS, $0.33 cost per session.

What is the difference between an identity graph and cookies for ad targeting?

Cookies are browser-based, device-specific, and expire or get blocked. An identity graph is device-agnostic — it connects the same household across the laptop where they visited your site, the phone where they opened your email, and the smart TV where they stream at night. CTV apps are native streaming applications with no cookie environment, so identity graphs are the only viable infrastructure for first-party data targeting on streaming TV. CRM audiences built on identity graphs are also more durable than cookie-based retargeting audiences, which erode as cookies expire.

How does CTV use identity resolution for targeting?

CTV identity resolution works through two mechanisms: deterministic matching (a confirmed email address links to a streaming device ID via authenticated logins — a fact-based connection) and probabilistic matching (behavioral signals like shared IP address and time-of-day patterns infer that two data points belong to the same household). Deterministic matches are more reliable; probabilistic matches extend reach to households where confirmed identifiers aren't available. Most CTV platforms use both to maximize match coverage.

How do streaming TV platforms build audience segments without cookies?

Streaming TV platforms build audience segments using identity graph matching instead of cookies. Your CRM data (Klaviyo segments, Shopify purchaser lists) is hashed and matched against the graph's email-to-device-ID records (deterministic matching), while your site pixel captures household IP addresses that match to streaming devices on the same network (IP matching). The result is a CTV audience built from your first-party data — non-purchasers, lapsed customers, high-LTV lookalikes — without any reliance on third-party cookies.