How to Use Lookalike Audiences for Scalable Growth

Lookalike audiences are a key part of any paid media strategy, but many brands only scratch the surface. To truly scale performance, lookalikes should be seen as an evolving tool that adapts with your audience, uses data insights effectively, and works seamlessly across platforms.

In this guide, we’ll dive deeper into how to make lookalike targeting work harder. You’ll learn how to apply it across Meta and Google Ads, segment audiences by intent and lifetime value, cut down on wasted spend, and refine your targeting as your first-party data becomes more powerful.

Why Lookalike Audiences Still Matter

Even with growing automation and AI-driven targeting, lookalike audiences continue to play a vital role. They give ad platforms the high-quality data they need to make smarter decisions, especially in an environment where privacy limits more direct targeting options.

When built the right way, lookalikes can:

1. Reduce CPA fluctuations by grounding algorithms in valuable data
2. Expand reach while maintaining strong returns
3. Speed up learning phases when entering new markets or testing new funnel stages

How to Build Lookalike Audiences Using Seed Data Strategy

The quality of your seed audience directly impacts your outcome. It’s not about scale; it’s about signal strength.

Best-Practice Seed Segments:

• High-LTV converters – this can be based on conversion value or 90-day repeat rate.
• Engaged but unconverted leads for mid-funnel expansion.
• Top percentile purchasers by AOV, margin, or engagement depth.

Meta:

• Upload hashed customer lists or use value-based audiences
• Use CRM integrations (Klaviyo, HubSpot, Segment) for real-time sync
• Using a lookalike of 3% or under is recommended to ensure there is a close relevancy to the sample audience

Google Ads:

• Similar audiences (now deprecated) have transitioned into optimised targeting in Display and Performance Max
• Instead of traditional “lookalikes,” use Customer Match and layer with audience signals like in-market and affinity
• You can also use lookalike audiences in Demand gen campaigns which can be a good way of increasing your brand awareness visibility

Our Paid Team’s Top Tip:

“If you’re segmenting your 1^st party data, make sure there’s enough people in it to create a good Lookalike. How much information you have in to give the platform will impact your match rate. With this in mind aiming for an audience size of ideally over 1000, means that even accounting for people who can’t be matched, it will give the platform more than enough to work out who your audience is.”

Smarter audience segmentation

Many advertisers create just one lookalike per data source, but real growth comes from digging deeper. Advanced segmentation looks at:

• Intent: distinguishing between someone who browses a product and someone who starts checkout
• Lifecycle stage: identifying first-time buyers versus loyal customers
• Location: adjusting performance expectations and offers by market

This approach allows you to test messaging, CTAs, and bidding strategies in parallel, giving you clearer insights into what drives conversions.

Example: Create one audience from subscribers who purchased within seven days and another from leads who converted after 30 days or more. Use the first group for time-sensitive campaigns and the second for nurture-focused activity.

Cross-Platform Deployment: Meta vs Google Ads

While Meta offers native LAL creation, Google Ads has deprioritised static similar audiences in favour of predictive models.

Meta (LALs):

• 1–10% range with user-defined similarity
• High-performing when paired with a creative tailored to seed behaviour
• Exclude overlapping LALs or Custom Audiences to reduce auction cannibalisation

Google Ads:

• Segment by engagement tier: add-purchasers to Shopping, demo-downloaders to Performance Max
• Leverage audience signals, but understand Google will still optimise based on conversion goals

Deployment Strategy:

Scaling: Lookalike Expansion vs. Data Enrichment

There are two primary methods of scale:

1. Expanding LAL similarity range (e.g., from 1% to 3–5%)
2. Enriching your source audience to maintain relevance at higher volumes

The first provides volume but dilutes quality. The second, when paired with dynamic integrations like Segment or CDPs, preserves targeting precision while increasing reach.

Enrichment ideas:

• Append behavioural or transactional tags (RFM segmentation)
• Feed in predictive LTV or churn propensity scores
• Use zero-party data (quiz responses, stated preferences)

On Meta, test stacked LALs (e.g., 1% purchasers + 1% subscribers) with unique exclusions to prevent audience collision.

Controlling for Overlap, Fatigue, and Cannibalisation

As you scale, overlap becomes a hidden tax on performance.

How to mitigate:

• Use Meta’s Audience Overlap tool to monitor intersections
• Create dedicated campaign structures for different LAL cohorts
• Enforce exclusion lists (e.g., exclude 1% LAL from broader 5% campaign)

Frequency Capping:

Especially important on Meta. Watch for creative fatigue signs:

• Declining CTR
• Rising CPC with static CPM
• CPA lift without corresponding CAC efficiency

Refresh creative every 2–3 weeks in high-frequency environments.

Attribution & Measurement

Lookalike audience performance shouldn’t be evaluated in isolation. You need to understand how these users influence downstream metrics across the funnel.

Use:

• Incrementality testing (geo splits, holdouts, ghost bids)
• Cohort analysis: how does LAL-3% behave vs LAL-1% 30 days post-click?

On Google Ads, Performance Max requires extra diligence, rely on tools like GA4 explorations, offline conversion imports, or MMM to validate value.

Our Paid Team’s Top Tip:

“Don’t forget the power of a custom landing page to test audiences on, or using audiences of specific web pages to create the perfect audience for remarketing to.”

First-Party Data Readiness: Preparing for the Future

With third-party cookie deprecation underway, your ability to feed high-quality, structured data into ad platforms will determine your targeting performance.

Make sure you:

• Have a unified data layer (CDP or integrated CRM)
• Map key events across platforms using server-side tracking
• Integrate offline conversion data into ad platforms (Meta Conversions API, Google Offline Conversion Tracking)

The better your data, the stronger your algorithmic targeting, even in the absence of traditional lookalike tools.

Final takeaways

Lookalike audiences are changing, not disappearing. What started as a simple way to reach new customers has become a more sophisticated strategy that blends data, creative, and platform performance.

Remember:

• Seed quality > audience size: Prioritise high-LTV and behaviour-rich segments
• Segment by intent, lifecycle, and geography to create modular audience frameworks
• Avoid audience overlap and internal competition with clean exclusions
• Test across platforms, especially as Meta and Google evolve their audience tools
• Invest in first-party data infrastructure now to future-proof targeting

By treating lookalikes as a living system rather than a one-time setup, you can continue to scale efficiently, even as targeting signals become harder to capture.