On February 29, 2024, LinkedIn officially discontinued its Lookalike Audiences feature, marking a significant shift in how marketers can reach new audiences on the platform. This change affects every advertiser who relied on Lookalike Audiences to expand their reach and find prospects similar to their best customers. Understanding what replaced this functionality--and how to leverage it effectively--is now essential for any social media marketing strategy.
This guide explores the sunset of LinkedIn Lookalike Audiences, the two new targeting options that emerged in their place, and actionable strategies to maintain and improve your LinkedIn advertising performance.
What Was LinkedIn Lookalike Audiences?
LinkedIn Lookalike Audiences was a powerful targeting feature that allowed advertisers to reach new professionals who shared similar characteristics with their existing customers or website visitors. By analyzing the attributes of a source audience--such as job titles, industries, company sizes, or engagement patterns--LinkedIn's algorithm would identify and target LinkedIn members who exhibited similar professional profiles.
This feature became popular among B2B marketers because it offered a scalable way to expand reach beyond immediate networks while maintaining targeting relevance. Rather than manually building out complex audience segments based on dozens of individual attributes, Lookalike Audiences simplified the process by letting machine learning do the heavy lifting.
Key Capabilities of Lookalike Audiences
- Automated audience building: Used AI to find similar professionals
- Scalable targeting: Expanded reach beyond direct customer lists
- Multi-attribute matching: Considered various professional characteristics
- Simplified campaign setup: Reduced manual targeting complexity
The discontinuation of Lookalike Audiences reflects LinkedIn's broader investment in more sophisticated AI-driven targeting that focuses on behavioral prediction rather than static attribute matching. This evolution presents opportunities for advertisers who approach the new features strategically.
The LinkedIn Lookalike Audiences Sunset: What Happened and Why
On February 29, 2024, LinkedIn discontinued Lookalike Audiences as part of a broader evolution of its advertising platform. Existing Lookalike Audiences stopped refreshing and became static representations of the audience data at the time of sunset. Active ad sets continued to deliver using these static audiences, but no new members could be added based on similar characteristics.
According to LinkedIn's official announcement, the sunset aligned with the platform's investment in more sophisticated AI-driven targeting capabilities and privacy-conscious approaches to audience building.
Key Implications for Advertisers
For Active Campaigns: Ad sets using Lookalike Audiences continued running but with static audience data. This meant audiences would not grow or adapt over time, potentially reducing campaign effectiveness as market conditions changed.
For Future Targeting: New Lookalike Audiences could no longer be created, and existing ones could not be edited. Advertisers needed to rebuild targeting strategies using the new options LinkedIn introduced.
For API Integrations: The Lookalike Audiences API was also sunset, affecting marketing platforms and tools that relied on this functionality for automated audience management.
Important Timeline Dates
| Date | Event |
|---|---|
| February 29, 2024 | Lookalike Audiences officially discontinued |
| March 30, 2024 | Inactive Lookalike Audiences archived |
| Ongoing | Predictive Audiences and Audience Expansion become recommended alternatives |
The transition from Lookalike Audiences to Predictive Audiences represents LinkedIn's shift from static attribute matching to dynamic behavioral prediction. This evolution offers opportunities for more precise targeting when combined with quality first-party data.
Predictive Audiences: LinkedIn's New AI-Powered Solution
Predictive Audiences represents LinkedIn's next-generation approach to finding prospects likely to take action based on your existing customer data. Unlike Lookalike Audiences, which matched members based on static attributes, Predictive Audiences uses LinkedIn's AI to analyze behavioral patterns and identify professionals most likely to convert or engage with your ads.
According to LinkedIn's official documentation, this approach offers several advantages over the original Lookalike Audiences.
How Predictive Audiences Work
When you create a Predictive Audience, you select a source audience from your existing data--typically a list of customers, website visitors, or leads. LinkedIn's AI then examines this source group to understand not just who they are, but what actions they've taken and what behaviors indicate high intent. The algorithm then predicts which other LinkedIn members are most likely to exhibit similar behaviors.
This shift toward AI-powered targeting mirrors broader trends in AI automation for marketing, where machine learning enhances audience selection and campaign optimization.
Key Advantages Over Lookalike Audiences
Behavioral Focus: Predictive Audiences emphasize what members do rather than who they are, potentially identifying high-intent prospects that might be missed by traditional demographic or firmographic targeting.
Daily Refresh: Unlike the static nature of former Lookalike Audiences, Predictive Audiences refresh daily, ensuring targeting evolves with changes in member behavior and market dynamics.
Multi-Signal Analysis: The AI considers numerous signals simultaneously, including engagement patterns, content consumption, profile updates, and professional activities.
Requirements for Creating Predictive Audiences
| Data Source Type | Minimum Requirement | Maximum Limit |
|---|---|---|
| Contact/Company Lists | 300 rows | 300,000 rows |
| Lead Gen Forms | 300+ members | No strict limit |
| Conversion Audiences | 300+ members | No strict limit |
| Retargeting Audiences | 300+ members | No strict limit |
Important Limitations:
- Maximum of 100 Predictive Audiences per ad account
- Cannot be shared across ad accounts
- Cannot be created from lists shared through Business Manager
- Audience Expansion is disabled for ad sets using Predictive Audiences
Early results from advertisers show promising performance improvements. According to 10fold's analysis, some campaigns experienced meaningful improvements in click-through rates when switching from Lookalike to Predictive Audiences, though individual results vary based on industry, audience quality, and campaign objectives.
Early Performance Results
25%
Potential CTR increase reported by some advertisers
300+
Minimum members required for Predictive Audiences
100
Maximum Predictive Audiences per ad account
Maximize your targeting effectiveness with these proven strategies
Source Data Quality Matters Most
The predictive model's accuracy depends entirely on the quality of your source data. Ensure your contact lists, website visitors, or conversion audiences represent your ideal customers.
Segment by Intent Level
Create separate predictive audiences for different segments. High-intent leads may produce different predictions than lower-funnel prospects.
Exclude Your Seed Audience When Appropriate
If you want to reach entirely new prospects, exclude your seed contact or company list from the targeting.
Monitor and Iterate
Since Predictive Audiences refresh daily, track performance over time. Consider refreshing source data or creating new audiences if results decline.
Audience Expansion: Scaling Your Existing Targeting
While Predictive Audiences focus on finding new prospects based on your data, Audience Expansion takes a different approach by automatically broadening your existing targeting criteria to reach similar professionals.
According to LinkedIn's official documentation, this feature is particularly valuable when you want to maintain your core audience definition while gaining additional scale.
How Audience Expansion Works
When you enable Audience Expansion for an ad set, LinkedIn analyzes your selected targeting criteria--such as job titles, skills, interests, or company attributes--and automatically expands to reach members who share similar professional characteristics. The expansion considers professional demographics as the primary data source.
When to Use Audience Expansion
Scaling Proven Campaigns: When you've identified targeting that works but need more reach to improve efficiency and reduce costs, Expansion provides scale without sacrificing relevance.
Awareness Campaigns: For campaigns focused on reach and brand awareness, Expansion offers an efficient way to maximize impressions.
Testing New Audiences: Use Expansion to reach a broader audience when testing new creative or messaging.
Important Limitations
- Not available for dynamic ad formats or Predictive Audiences
- Performance metrics include both core targeting and expansion audience
- Excluded attributes from targeting won't be included in expansion
- Audience count preview doesn't include expansion members
Audience Expansion works alongside your Matched Audiences--contact lists, website visitor audiences, and company followers--providing scale while retaining the specificity of your core targeting.
| Feature | Predictive Audiences | Audience Expansion |
|---|---|---|
| Primary Focus | Behavioral prediction | Attribute-based scaling |
| Refresh Rate | Daily | Real-time |
| Best For | Conversion campaigns | Awareness campaigns |
| Source Data Required | Yes (300+ members) | No |
| Max per Account | 100 audiences | Unlimited |
| Works with Expansion | N/A | Yes |
| Available for Dynamic Ads | No | No |
Migration Strategies: Moving from Lookalike to Predictive Audiences
If you were using Lookalike Audiences before the sunset, here's how to effectively transition your campaigns:
Step 1: Audit Your Existing Lookalike Audiences
Document your existing Lookalike Audiences and their performance metrics. Identify which ones delivered the best results and what source audiences you used. This information guides your transition strategy.
Step 2: Identify the Right Replacement Option
Choose Predictive Audiences when:
- You have quality source data
- Finding high-intent prospects is your priority
- Campaigns focus on conversions or lead generation
Choose Audience Expansion when:
- You want to scale existing targeting criteria
- Campaigns prioritize reach and awareness
- You prefer more control over core targeting
Step 3: Recreate Your Targeting
Create new Predictive Audiences or configure Audience Expansion based on your strategy. For Predictive Audiences, use your original source data where possible.
Step 4: Test and Optimize
Launch campaigns with new targeting and compare results to historical performance. Be prepared to iterate and refine your approach.
Common Migration Mistakes to Avoid
- Using Poor-Quality Source Data: Poor source data produces poor predictions
- Ignoring the 30-Day Archive Window: Archived audiences may be inaccessible
- Assuming Direct Equivalence: Predictive Audiences work differently and may produce different results
The transition underscores the importance of first-party data for modern advertising. As privacy regulations tighten and platform tracking capabilities evolve, first-party data--information customers directly provide--becomes increasingly valuable for targeting and personalization. Complement your LinkedIn strategy with a comprehensive SEO services approach to maximize organic and paid reach.
For deeper insights into LinkedIn's advertising tools, explore our guide on hidden Sales Navigator features that can enhance your B2B lead generation efforts.