What is lookalike audience targeting on CTV?

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Last updated: April 8, 2026

Quick Answer: Lookalike audience targeting on CTV (Connected TV) is a digital advertising technique that uses machine learning algorithms to identify new viewers who share similar characteristics with a brand's existing high-value customers. This approach leverages first-party data from sources like customer lists or website visitors to create seed audiences, then expands reach by finding users with matching demographics, behaviors, and viewing patterns across CTV platforms. Major CTV platforms like Roku, Amazon Fire TV, and smart TV operating systems have integrated lookalike targeting capabilities, with adoption growing significantly since 2020 as CTV advertising spending increased by over 50% annually. This method helps advertisers achieve higher conversion rates by reaching audiences more likely to engage with their content.

Key Facts

Lookalike modeling typically identifies audiences with 70-90% similarity to seed audiences based on hundreds of data points
CTV advertising spending reached $21.2 billion in 2023, with lookalike targeting becoming increasingly common
Major platforms like Roku introduced lookalike audience tools in 2019, with Amazon following in 2020
Lookalike audiences on CTV can achieve 2-3x higher engagement rates compared to traditional demographic targeting
The technique relies on machine learning algorithms that analyze thousands of data signals including viewing habits, device usage, and purchase behaviors

Overview

Lookalike audience targeting represents a sophisticated evolution in digital advertising that has become particularly significant in the CTV (Connected TV) space. The concept originated in social media advertising around 2013 when Facebook introduced its lookalike audience feature, allowing advertisers to find new users similar to their existing customers. As CTV adoption accelerated—with over 85% of U.S. households having at least one connected TV device by 2023—advertisers sought more precise targeting methods beyond traditional demographic approaches. The technology gained mainstream adoption in CTV advertising between 2019-2021 as platforms like Roku, Amazon Fire TV, and smart TV manufacturers integrated machine learning capabilities. This shift coincided with the decline of third-party cookies and increased privacy regulations, making first-party data strategies more valuable. CTV's unique position as both a premium video environment and a digital platform created ideal conditions for lookalike targeting to flourish, combining television's brand-building power with digital advertising's precision.

How It Works

Lookalike audience targeting on CTV operates through a multi-step process that begins with creating a seed audience from first-party data sources. Advertisers typically upload customer email lists, mobile advertising IDs (MAIDs), or use pixel data from their websites to establish this initial group. The CTV platform's machine learning algorithms then analyze hundreds to thousands of data points about these seed users, including their viewing habits, content preferences, device usage patterns, geographic locations, and inferred demographic characteristics. The system compares these patterns against the broader CTV user base to identify users who share similar attributes but haven't yet interacted with the brand. Most platforms allow advertisers to adjust similarity thresholds, typically offering options from 1% (most similar but smallest audience) to 10% (broader reach with slightly lower similarity). The algorithms continuously refine these models as they receive performance feedback, optimizing for specific campaign objectives like brand awareness, consideration, or conversion.

Why It Matters

Lookalike audience targeting matters because it addresses fundamental challenges in CTV advertising: wastage and measurement. Traditional TV advertising reaches many viewers unlikely to be interested in a product, while lookalike targeting can increase relevance by 40-60% according to industry studies. For advertisers, this translates to better return on ad spend (ROAS), with some campaigns achieving 30-50% higher conversion rates compared to demographic targeting alone. The approach also helps bridge the gap between upper-funnel brand awareness and lower-funnel performance marketing, creating more cohesive customer journeys across devices. As privacy regulations tighten and third-party data becomes less available, lookalike targeting using first-party data represents a sustainable, privacy-compliant approach to audience expansion. For consumers, it can mean fewer irrelevant ads and more personalized content recommendations, though it raises important questions about data privacy and algorithmic transparency that the industry continues to address.