Lookalike Audience
A lookalike audience is a targeting method that finds new people who are similar to your existing customers, based on shared characteristics and behaviors. It uses machine learning to identify people who are likely to be interested in your products or services.
How lookalike audiences work
Source audience: Start with your existing customers or website visitorsData analysis: Platforms analyze the characteristics of your source audiencePattern recognition: Identify common traits, interests, and behaviorsAudience expansion: Find new people who share similar characteristicsSimilarity matching: Use algorithms to match the closest profilesAudience creation: Generate a new audience of similar peopleTypes of source audiences
Customer lists: Email subscribers or customer databasesWebsite visitors: People who visited your websiteApp users: People who downloaded or used your appEngaged users: People who interacted with your contentHigh-value customers: Your most valuable customersEmail subscribers: People who signed up for your newsletterVideo viewers: People who watched your videosLookalike audience sizes
1% lookalike: Most similar to your source audience (smallest, highest quality)2-3% lookalike: Good balance of similarity and reach4-5% lookalike: Larger audience with moderate similarity6-10% lookalike: Largest audience with lower similarityCustom percentages: Some platforms allow custom similarity levelsMultiple sizes: Test different percentages to find the best performancePlatform-specific lookalike audiences
Facebook/Instagram: 1-10% lookalike audiencesGoogle Ads: Similar audiences for search and displayLinkedIn: Lookalike audiences for B2B targetingTwitter: Tailored audiences with lookalike expansionPinterest: Actalike audiencesTikTok: Lookalike audiences for video contentSnapchat: Lookalike audiences for younger demographicsBenefits of lookalike audiences
Scale reach: Find new customers similar to existing onesQuality targeting: Higher likelihood of conversionCost efficiency: Often lower cost per acquisitionAudience expansion: Grow beyond your existing customer baseData utilization: Leverage your first-party dataAutomated optimization: Platforms continuously improve targetingCross-platform reach: Use the same audience across platformsCreating effective lookalike audiences
Quality source data: Use high-quality, recent customer dataSufficient size: Ensure source audience is large enough (1000+ people)Clean data: Remove duplicates and invalid entriesRecent activity: Use data from the last 30-90 daysHigh-value customers: Focus on your best customers as sourceMultiple sources: Test different source audiencesRegular updates: Refresh audiences with new dataLookalike audience best practices
Test different percentages: Start with 1-3% and test larger sizesUse multiple sources: Test different customer segmentsExclude existing customers: Avoid showing ads to current customersA/B test audiences: Compare different lookalike audiencesMonitor performance: Track metrics and optimize accordinglyRefresh regularly: Update audiences with new customer dataCross-platform testing: Test the same audience on different platformsMeasuring lookalike audience performance
Reach: How many people are in your lookalike audienceClick-through rate: Percentage who click your adsConversion rate: Percentage who complete desired actionsCost per acquisition: Cost to acquire one customerReturn on ad spend: Revenue generated per dollar spentAudience quality score: Platform's assessment of audience qualityLift studies: Measure incremental impact vs other targetingCommon lookalike audience mistakes
Poor source data: Using low-quality or outdated customer dataToo small source audience: Not enough data for accurate matchingWrong percentage: Choosing size based on reach rather than performanceNo exclusions: Showing ads to existing customersSet and forget: Not updating audiences regularlySingle source: Only using one type of source audienceIgnoring performance: Not optimizing based on resultsLookalike vs other targeting methods
Lookalike vs Interest: Lookalike uses your data, interest uses platform dataLookalike vs Demographics: Lookalike is behavioral, demographics are descriptiveLookalike vs Retargeting: Lookalike finds new people, retargeting re-engages existingLookalike vs Custom Audiences: Lookalike expands, custom audiences are specificPrivacy and data considerations
Data privacy: Ensure compliance with privacy regulationsData sharing: Understand how platforms use your customer dataOpt-out options: Provide ways for customers to opt outData retention: Follow platform data retention policiesTransparency: Be clear about how you use customer dataConsent: Ensure you have proper consent for data usageAdvanced lookalike strategies
Layered targeting: Combine lookalike with other targeting methodsSequential targeting: Use different lookalike audiences in sequenceValue-based lookalike: Focus on high-value customer characteristicsBehavioral lookalike: Target based on specific behaviorsCross-platform lookalike: Use the same audience across platformsDynamic lookalike: Continuously update based on new dataExclusion lookalike: Exclude certain customer segments
