These platforms employ machine learning to analyze data from a "source" audience, such as customer lists or site visitors, to identify users with similar traits and behaviors. Facebook, Google, and LinkedIn each offer unique approaches to lookalike audience targeting, with variations in audience building, data sources, and effectiveness. This analysis compares Facebook's Lookalike Audiences, Google's Similar Audiences (now largely replaced), and LinkedIn's Lookalike Audiences, examining targeting options, data sources, and performance. Strategic insights are provided to optimize lookalike audience usage on each platform, accompanied by real-world examples.

Facebook Lookalike Audiences

Targeting Options and Data Sources:

Facebook's Lookalike Audiences allow businesses to extend their reach beyond their immediate customer base (Morgan, 2025). Advertisers create a Facebook Lookalike Audience by providing a source audience, typically a Custom Audience derived from first-party data, such as customer lists, website visitors tracked via the Facebook Pixel, app users, or engaged users (Morgan, 2025). Facebook's algorithm analyzes the source audience to identify common traits and locate other users ("lookalikes") who share those traits (Morgan, 2025). Advertisers can specify the size of the lookalike as a percentage of the target country’s population, ranging from 1% (most similar) to 10% (broader reach) (Morgan, 2025). Facebook requires a source audience of at least 100 people from a single country but recommends 1,000–5,000 users for optimal results (Morgan, 2025). Larger, high-quality source lists tend to yield more accurate lookalikes (Marketing 360, 2018). Lookalike Audiences can also be created across countries, using a U.S. customer list to find similar users in new international markets.

Effectiveness:

Facebook’s lookalike targeting is a core strategy for efficient customer acquisition (Morgan, 2025). By targeting individuals resembling current customers, businesses can enhance ad relevance and conversion rates compared to broad demographic targeting. For instance, a supplement retailer using a Facebook lookalike audience (sourced from approximately 19,000 past customers) achieved a 21.3% conversion rate. This campaign, with a $575 spend, generated roughly $3,890 in revenue – an ROI of nearly 6.8×, indicating the lookalike audience reached highly qualified prospects (Marketing 360, 2018). While some have observed mixed results due to algorithm evolution and the introduction of Meta’s Advantage+, lookalike audiences remain effective for reach expansion, particularly for boosting prospecting efficiency (Templeton, 2024).

Strategic Tips for Facebook:

To optimize lookalike audience campaigns on Facebook:

• Use High-Quality Source Data: Focus on your most valuable customers or leads as the seed (Morgan, 2025).

• Choose the Right Audience Size: Start with the smallest (1%) lookalike and test gradually larger percentages (Morgan, 2025).

• Layer or Exclude as Needed: Refine a lookalike audience further by filtering it by relevant interests or demographics (Morgan, 2025).

• Test Creatives and Measure Results: Use A/B tests on ad creatives and messaging (Marketing 360, 2018).

Real-World Example: SnackNation, a healthy snack box service, used Facebook lookalike audiences to find new subscribers similar to their existing customers, resulting in a 2X higher click-through-rate and 3X more subscriptions compared to interest-based targeting alone.

Google Ads and Similar Audiences (Lookalikes on Google)

Targeting Options and Data Sources:

Google’s equivalent to lookalike audiences, Similar Audiences (or “Similar Segments”), extends the reach of remarketing and customer lists (McCormick, 2024). The source for Similar Audiences is typically a first-party data segment in Google Ads, such as a remarketing list of website visitors, app users, YouTube viewers, or a Customer Match list (McCormick, 2024). Google requires a minimum of 100 active users in the source list to generate a similar audience (Tsang, 2022). Data signals used include browsing behavior, search history, and contextual signals (McCormick, 2024). Google aimed to "still obey your audience targeting parameters" (McCormick, 2024).

Effectiveness:

Google’s Similar Audiences broadened reach without sacrificing relevance (McCormick, 2024). Campaigns using Similar Audiences often saw improved performance metrics compared to untargeted prospecting. For example, one analysis of Google Shopping campaigns found that ad groups targeting similar audiences achieved conversion rates ~7–14% higher than those targeting non-remarketing audiences (Terzioglo, 2018). However, Google phased out Similar Audiences by 2023, replacing them with Optimized Targeting and Audience Expansion (McCormick, 2024). Optimized Targeting uses real-time conversion data and machine learning to find users likely to convert (McCormick, 2024). Audience Expansion broadens targeting to include similar viewers.

Strategic Tips for Google:

Businesses advertising on Google can adapt their strategy by:

• Leveraging First-Party Data to the Fullest: Continue to build and segment remarketing lists and Customer Match lists (McCormick, 2024).

• Enabling Automated Expansion Features: Use Optimized Targeting for Display, Discovery, and Video campaigns (McCormick, 2024).

• Monitoring Performance and Using Exclusions: Keep an eye on metrics like conversion rate and cost per conversion.

• Combining with Search Intent: Rely on Smart Bidding strategies (McCormick, 2024).

• Respecting Privacy and Transparency: Ensure your use of customer data complies with policies (McCormick, 2024).

Real-World Example: Company X, an e-commerce retailer, found that using Optimized Targeting on its Display campaigns yielded a 12% higher conversion volume at a similar cost-per-acquisition compared to the old approach.

LinkedIn Lookalike Audiences

Targeting Options and Data Sources:

LinkedIn's Lookalike Audiences, introduced in 2019 as part of its Matched Audiences suite, finds other LinkedIn members who share similar attributes (Bailey, 2020). The source audience can be a list of company or contact emails, a website retargeting list, or engagement from LinkedIn Lead Gen Forms (Bailey, 2020). LinkedIn matches based on job titles, seniority, industry, company size, skills, etc. (Bailey, 2020). LinkedIn requires a minimum audience size of 300 people for the source (Bailey, 2020).

Effectiveness:

LinkedIn lookalike audiences allowed B2B marketers to scale campaigns while maintaining relevance based on professional traits (Templeton, 2024). However, the effectiveness of LinkedIn’s lookalike targeting has been reported as mixed. One reason is that LinkedIn’s platform has a smaller user base and fewer conversion signals than Facebook or Google. As of early 2024, LinkedIn discontinued its Lookalike Audiences feature (Templeton, 2024). In place of traditional lookalikes, LinkedIn has introduced Predictive Audiences and an enhanced Audience Expansion (Templeton, 2024).

Strategic Tips for LinkedIn:

For businesses aiming to expand reach on LinkedIn:

• Start with a Strong Matched Audience: Use a high-quality matched audience as the seed (Bailey, 2020).

• Layer Additional Targeting Filters: Combine lookalikes with LinkedIn’s demographic/firmographic filters (Jones, 2022).

• Test and Monitor Closely: Keep a close eye on performance metrics like CTR, cost per lead, and lead quality (Jones, 2022).

• Utilize LinkedIn’s New Tools: Experiment with Predictive Audiences for a data-driven approach (Templeton, 2024).

• Combine with Content and Messaging: Tailor your ad creatives to the personas you expect in the lookalike.

Real-World Example: A B2B software company uploaded a list of 500 companies that were current customers and created a LinkedIn lookalike audience, refined to include senior IT job titles at companies of 200+ employees, resulting in 40% more form-fill leads compared to their previous targeting.

Comparative Analysis of Lookalike Capabilities

Each platform’s lookalike audience feature operates on the same fundamental idea but differs in audience creation, data usage, and overall effectiveness.

• Data Sources: Facebook primarily uses individual-level data (Morgan, 2025). Google uses intent and behavioral data (McCormick, 2024). LinkedIn uses professional identity data (Bailey, 2020).

• Targeting Controls and Options: Facebook gives the most granular control (Morgan, 2025). Google’s Similar Audiences were automatically created (McCormick, 2024). LinkedIn required at least 300 people in the source list (Bailey, 2020).

• Platform Data and Algorithm Differences: Facebook and Google benefit from massive scale and diverse data points. LinkedIn’s data is narrower, rich in professional info but not as much in personal browsing or buying intent.

• Effectiveness and Use Cases: Facebook’s lookalike audiences are highly effective for B2C customer acquisition. Google’s similar audiences supplemented intent-based campaigns. LinkedIn’s lookalikes were best for B2B marketers looking to expand reach in a targeted way.

• Recent Developments: Facebook’s lookalike capability remains in place. Google has sunsetted the standalone Similar Audiences. LinkedIn has removed lookalikes, replacing them with new tools (Templeton, 2024).

Conclusion

Lookalike audiences have become a staple in digital advertising. On Facebook, lookalike audiences offer granular control and are powerful for consumer marketing. On Google, while the explicit feature is gone, its essence lives on in Google’s automated targeting options. On LinkedIn, though the feature is retired, LinkedIn’s new predictive tools and existing rich targeting can achieve similar goals.

For businesses, success with lookalike audiences hinges on data and testing. High-quality input will yield high-quality prospects. It’s also vital to continuously monitor performance. By staying adaptive, marketers can continue to harness the power of “finding lookalikes” to drive growth. In summary, lookalike audiences remain a formidable way to expand reach in 2025, and by understanding each platform’s nuances, businesses can tap into new markets of potential customers that closely resemble their most valuable clientele, thereby maximizing campaign relevance and ROI.

Bibliography (APA Style)

• Bailey, R. (2020, October 15). Grow your business with a LinkedIn lookalike audience. Vende Digital.

• Jones, E. (2022, May 31). How to effectively use LinkedIn Ads lookalike audiences. B2Linked Blog.

• Marketing 360. (2018, March 19). Case study: Facebook lookalike audiences bring high conversion rates. Marketing 360 Blog.

• McCormick, K. (2024, March 25). Google is sunsetting Similar Audiences in 2023: What you need to know. WordStream.

• Morgan, M. (2025, February 12). Facebook ad targeting in 2025: Every option to reach your audience. WordStream.

• Templeton, S. (2024, February 3). LinkedIn retires lookalike audiences: What you need to know. Single Grain.

• Terzioglo, D. (2018). Similar audiences in Google Shopping campaigns: Test results. Netpeak.