The ad industry spent five years panicking about cookie deprecation. CTV advertisers watched from the sideline, mildly confused.
Cookies never worked on your television. That panic was always a browser problem. And while B2C marketers scrambled to rebuild targeting infrastructure they’d grown dependent on, B2B CTV advertising quietly developed a different set of tools, ones that turn out to be more accurate and more privacy-compliant than anything cookies ever delivered.
This is what cookieless targeting actually means on CTV, why it matters for account-based campaigns, and what you need to know before you run your first program.
Why CTV Never Had Cookies
Cookies are a browser technology. They were created in the mid-1990s to track sessions and behavior on web pages. They require a browser, an HTTP request, and storage on the user’s device.
Your television has none of those things. A Roku, Samsung Smart TV, or Amazon Fire TV doesn’t run a web browser. It runs a streaming app that talks directly to ad servers via APIs. There’s no mechanism to drop a cookie, and there never was.
This is important context: when you read headlines about “cookieless targeting” in CTV, that’s not a workaround for a problem that was solved and then broken. CTV built its targeting infrastructure from scratch, without cookies, using completely different mechanisms. The question was never “how do we replace cookies on CTV?” It was always “how do we identify households and reach specific audiences without a browser?”
The answer involves four distinct approaches. B2B advertisers use all of them.
How CTV Targeting Actually Works
IP-Based Household Targeting
Every streaming device in a home shares a household IP address. That IP is the anchor for CTV targeting.
When you upload a list of target accounts to a CTV platform, the platform’s data partners match those company records to residential IP addresses. The chain looks like this: company, employee records, home address, household IP, ad delivery.
IP targeting is accurate at the household level, not the individual level. If a CFO lives in a household with a spouse who works somewhere else, both people in that household will see your ad. For B2B campaigns, that’s an acceptable tradeoff. You’re building brand with people likely to be in professional roles, and occasional waste doesn’t undermine the program.
IP addresses also change over time. Most home internet connections use dynamic IPs that rotate periodically. Good CTV platforms manage this through continuous refresh of household IP data, not a one-time match.
Device Graphs and Household Graphs
A device graph maps the relationships between devices in a household. The same household IP connects a smart TV, a laptop, two smartphones, and a tablet. A household graph ties those devices together so advertisers can understand cross-device behavior and ensure consistent frequency.
For B2B advertisers, device graphs serve a specific purpose: connecting a CTV impression to downstream digital behavior. Someone sees your CTV ad on Tuesday night. Your sales rep reaches out Wednesday morning. Did the CTV impression affect the response rate? To answer that, you need to connect the living room TV to the work laptop they opened your email on. That’s what a device graph enables.
Third-party identity infrastructure companies maintain these graphs. They’re the plumbing behind most sophisticated CTV targeting.
Deterministic Matching
Deterministic matching uses verified identity data to confirm a connection between your target list and an addressable household. Rather than inferring who lives at a household based on IP behavior, deterministic approaches rely on a confirmed data point: a verified email, a known physical address, or an identity record from a third-party data provider.
Third-party identity providers maintain deterministic identity graphs that connect business contacts to household addresses. You upload your account list or contact data, the identity graph matches those records to households, and the CTV platform targets those matched households.
This is more accurate than IP-only matching because it’s based on confirmed records rather than inferred connections. The tradeoff is scale. Not every contact on your list will have a deterministic match. Most B2B campaigns combine deterministic matching with IP-based probabilistic approaches to balance accuracy and reach.
First-Party Data: Your CRM and Account Lists
The most direct path to your target accounts is your own data. Upload a CRM export of target companies, key contacts, or open opportunities to your CTV platform. The platform runs that list through its identity graph to match records to addressable households.
This is where CTV account-based targeting gets practical. Your target account list already exists. You built it for your ABM program. CTV is just another activation channel for the same list.
What you can upload:
- Company names and domains (matched to employee household data)
- Contact email addresses (matched through identity graphs to household data)
- Physical addresses for known contacts (matched directly to household IPs)
The richer your data, the better your match rates. A list of 500 company names with no contact information will match at lower rates than a list of 500 contacts with email addresses and company domains.
Contextual Targeting
Contextual targeting on CTV means placing ads in content that matches your target audience’s interests rather than targeting by identity. Advertise around business news content, industry-specific programming, or financial programming when you’re targeting finance executives.
Contextual is the lowest-cost approach and the most privacy-safe. It doesn’t require any data matching. The tradeoff is precision: you’re reaching audiences who watch certain content, not the specific accounts on your target list.
For B2B campaigns, contextual works best as a complement to account-based targeting, not a replacement. Run account-based campaigns to reach your known target accounts precisely, and layer in contextual to extend reach to adjacent audiences you might be missing.
The B2B Account-Based Targeting Workflow
Here’s how a typical B2B CTV program actually moves from account list to ad delivery.
Step 1: Account list preparation
Pull your target accounts from your CRM. Most teams start with their top 200-500 companies, or the accounts with active opportunities and high-intent signals. There’s no technical reason you can’t load a larger list, but focused campaigns typically perform better than broad ones when you’re measuring account engagement.
Step 2: Identity resolution
Your CTV platform (or its data partner) runs the account list through an identity graph. The graph cross-references company records against:
- Employee databases (LinkedIn profile data, business contact databases)
- Consumer identity records (voter registration, credit bureau files, marketing databases)
- Device registration records
Output: a list of residential IP addresses and device IDs associated with employees at your target companies.
Step 3: Segment activation
The matched household data gets pushed into the CTV platform’s targeting infrastructure. Your campaign targets those specific IPs and device IDs when those streaming devices request ad inventory.
Step 4: Ad delivery
When someone in a matched household starts streaming a show, the ad server identifies the device, checks it against your target segment, and (if it matches and wins the auction) serves your creative.
Step 5: Attribution and measurement
After the campaign, you measure account engagement: did target accounts visit your website more frequently? Did exposed accounts move through pipeline stages faster? This is where device graphs close the loop, connecting the living room CTV impression to subsequent website visits from a work laptop.
Match Rates: What to Realistically Expect
Match rates on B2B account lists vary based on data quality and list composition. Not every account gets matched in a given campaign.
That means if you upload 500 companies, you’ll typically reach employees at a meaningful subset of them. Unmatched accounts aren’t necessarily unreachable (they may appear in later campaigns as their device data refreshes), but you won’t hit 100% of your list in a single campaign.
What affects match rates:
Higher match rates: Contact-level data (email addresses), larger companies with more employees, US-headquartered companies, companies where employees live in standard suburban/urban areas with clean residential IP data.
Lower match rates: Company-name-only lists, small companies with fewer employees, international companies, executives who use VPNs or enterprise internet connections at home.
Don’t chase the highest match rate at the expense of accuracy. A lower match rate reaching the right households is worth more than a higher match rate reaching loosely associated records. Ask your CTV platform to explain their matching methodology, not just their headline number.
Privacy and Compliance: The Honest Version
CTV targeting doesn’t use cookies, but that doesn’t mean it exists outside privacy frameworks. State privacy laws and consumer consent requirements apply.
What you need to know:
CCPA (California), Virginia CDPA, Colorado CPA, and similar state laws regulate the use of personal data in advertising. They apply to the data companies feeding your CTV campaigns (identity graph providers, data brokers, and streaming platforms), not typically to you as the advertiser directly, but you should understand what data your platform uses and whether it’s sourced compliantly.
Legitimate CTV data providers source their identity data from consumers who’ve opted in to marketing use, or from publicly available records (voter files, business registrations). They honor opt-outs and suppression requests.
Questions to ask your CTV platform:
- Where does your household IP data come from?
- How do you handle opt-outs and suppression?
- Do your data partners comply with state privacy laws?
- Can I see documentation of your data sourcing?
Any reputable platform should answer these without hesitation. If they deflect, that’s a signal.
Household targeting is not individual surveillance. You’re delivering an ad to a household IP, not tracking an individual’s behavior across sites. This is meaningfully different from the kind of cross-site tracking that drove cookie deprecation concerns. The privacy posture is closer to traditional direct mail (delivered to a household address) than to behavioral retargeting.
Why Cookieless Is Actually Better for B2B
This is the counterintuitive part. The absence of cookies on CTV isn’t a limitation to work around. For B2B account-based targeting, it’s an architectural advantage.
Cookie-based targeting was built for individuals. Cookies track individual browser behavior: this person visited your pricing page three times, add them to your retargeting segment. That’s useful for B2C, where individual purchase decisions matter. B2B decisions don’t work that way. You need to reach buying committees: multiple people at multiple seniority levels within a target company. Tracking one person’s browsing behavior doesn’t tell you about the five other stakeholders involved in the deal.
Household targeting matches how B2B buying actually works. Executives review proposals at home. They watch demos during commutes or on tablets at night. They discuss vendor options over dinner. Reaching a household (multiple people, multiple devices) is actually more appropriate for B2B than hyper-targeting one individual who happened to click an ad.
Account-based identity is more stable than cookies. Cookies expire. Users clear them. Browser updates break them. An IP address tied to a company’s employee, matched through an identity graph, doesn’t disappear when someone clears their browser history. The data infrastructure behind CTV targeting is more durable than cookie-based approaches.
Privacy compliance is built in, not bolted on. Because CTV never relied on the same tracking mechanisms now under regulatory pressure, you’re not inheriting years of questionable data practices. Good CTV platforms built for compliant data sourcing from the start.
The B2B advertisers who were early to CTV figured this out before the rest of the market. Cookie deprecation wasn’t a threat to their CTV programs. It was an argument for investing more in a channel that had already solved the problem. For a broader look at how B2B CTV works beyond targeting, see our B2B CTV advertising guide.
Common Misconceptions
“CTV targeting is less accurate without cookies.”
Cookies were never accurate for CTV because they didn’t exist there. IP-based matching and third-party identity graphs are not a degraded version of cookie targeting. It’s a different system with different tradeoffs: better for household-level reach, less useful for individual-level behavioral tracking. For B2B, household-level is what you want.
“You can only reach broad demographics on CTV.”
This was true of traditional TV. It’s not true of CTV. You can target the specific 300 companies on your account list with the same precision you’d apply to a direct mail program. The demographic approach is one option, not the only one.
“Private browsing or VPNs will hide my targets.”
Personal VPNs affect browser traffic, not streaming device IP behavior. Most consumers don’t run their Roku through a VPN. Commercial VPN usage at home is low enough that it doesn’t meaningfully affect CTV campaign reach.
“You need a massive account list for CTV to work.”
No. Enterprise programs with 50 named accounts run effective CTV campaigns. The minimum isn’t a huge list. It’s a list with enough people associated with it to deliver meaningful impression volume. 100-200 accounts with multiple employees each is enough to run a meaningful program.
“Lower match rates mean the platform isn’t working.”
A match rate on a precise account list is often more valuable than a higher rate on a loosely defined segment. Match rate is one input, not the only measure of targeting quality. Ask how they match, not just how many.
Frequently Asked Questions
Does CTV advertising use cookies?
No. CTV devices don't run browsers, so cookies were never part of the targeting infrastructure. CTV advertising uses IP-based household targeting, device graphs, third-party identity matching, and first-party account lists. These approaches were built from scratch for the streaming environment.
How do B2B advertisers reach target accounts on CTV without cookies?
The workflow is: upload your account list (company names, contact emails, or CRM data) → the platform's identity graph matches those records to residential IP addresses and device IDs → your ads run to those specific households when they stream content. Match rates vary based on data quality and list composition.
What is IP-based targeting on CTV?
Every streaming device in a home shares the household's internet IP address. CTV platforms match company and contact records to residential IP addresses through identity graphs, then target ads to those specific IPs. It's accurate at the household level: all devices in the home are reachable, not just one individual's device.
What is a device graph and why does it matter for CTV?
A device graph maps the relationships between devices in a household (smart TV, laptop, phones, tablets) all connected through the same IP. For B2B campaigns, device graphs let you connect a CTV impression to downstream behavior: if someone sees your ad on Tuesday night and opens your email Wednesday morning, the device graph links those two events.
What are realistic match rates for B2B account lists on CTV?
Match rates vary based on data quality and list composition. Contact-level lists (with email addresses) match higher than company-name-only lists. Larger US companies match better than small or international ones. A match rate on precise accounts is often more valuable than a higher rate on a loosely defined segment.
Is CTV targeting compliant with state privacy laws?
It can be, but you need to verify your platform's data sourcing. Legitimate CTV targeting uses identity data sourced from consumers who've opted into marketing use, or from publicly available records. Ask your platform to document their data sourcing and how they handle opt-outs. Reputable providers answer this without deflecting.
Why is cookieless targeting an advantage for B2B?
Cookie tracking was built for individual browser behavior, which is useful for B2C but misaligned with B2B buying. B2B decisions involve buying committees across multiple people. CTV's household-level targeting reaches everyone in a household, which better matches how executives review vendor options in the real world. The data infrastructure is also more durable: it doesn't break when users clear their browsers or update their settings.
What is deterministic matching in CTV advertising?
Deterministic matching uses confirmed identity data from third-party providers to connect your target contacts to addressable households. You upload a contact or account list, the identity graph matches those records to verified household data, and the CTV platform targets those households. It's more accurate than IP-only matching but lower scale, which is why most campaigns combine both approaches.
Run Account-Based CTV Without the Cookie Dependency
SpotlightIQ is purpose-built for B2B account-based CTV. Share your target account list and we handle the rest, reaching decision-makers at those companies on Hulu, Disney+, ESPN, and other premium streaming networks, with reporting on where your ads ran and which accounts engaged.
What you get:
- IP-based and identity graph account matching
- Simple list onboarding: share your target accounts, we activate them
- Account engagement measurement (not just impressions)
- A dedicated team working alongside yours, flexible commitments
Want to see how it works for your account list? Talk to us
