Similar Audiences and lookalikes after the deprecation: how to find new audiences in 2026

In 2023, Google permanently removed Similar Audiences from Google Ads, one of the platform's most widely used targeting features. For many advertisers, this felt like losing a key competitive advantage: the ability to automatically reach new users who closely resembled their existing customers. In 2026, the dust has settled, and the reality is more nuanced than many feared at the time. Robust alternatives exist, but they require a different mindset, stronger investment in first-party data, and a smarter use of Smart Bidding and audience signals. This article explains what changed, which strategies work best today, and how AdBrains uses its own AI technology to automatically fill the gap left by Similar Audiences.

What were Similar Audiences and why were they removed?

Similar Audiences, also called lookalike audiences, were a Google Ads feature that automatically identified new users who closely resembled visitors or customers in an existing remarketing list. Advertisers uploaded a customer list or connected a website audience as a "seed," and Google generated a comparable audience based on behavioral patterns, search queries, and demographic data. This made prospecting, reaching people who had never heard of your brand, significantly easier.

The deprecation in 2023 was not a surprise for industry insiders. Growing pressure from privacy legislation, the phase-out of third-party cookies, and Google's own Privacy Sandbox initiatives made maintaining such audience models increasingly complex from both a technical and regulatory perspective. Google chose to integrate the logic behind Similar Audiences into broader automated systems such as Performance Max (PMax) and Optimized Targeting, rather than offering them as a standalone, transparent audience layer.

For advertisers who heavily relied on Similar Audiences for their prospecting strategy, this required a significant shift. However, the current alternatives, when properly configured, offer more reach and precision than the old lookalikes ever could.

The best alternatives for Similar Audiences in 2026

Then vs. now: audience strategy in Google Ads

From Similar Audiences to AI-driven alternatives

Before deprecation (until 2023)

• Similar Audiences as ready-made segments
• Manual list assignment to campaigns
• Limited to first-party seed list
• Static audience segments
• No automatic profile refreshing
• Reliant on third-party cookies

After deprecation: AI alternatives (2026)AdBrains AI

• Optimized Targeting & audience signals in PMax
• Smart Bidding driven by conversion profile
• Customer Match with first-party data as signal
• Dynamic, self-learning audience segments
• Automatically updated audiences weekly
• Server-side first-party signal enrichment

The removal of Similar Audiences forced advertisers to fundamentally rethink their audience strategy. Those who made the transition intelligently now benefit from a more robust approach that is less dependent on cookies and more driven by their own data and machine learning. The most effective alternatives are listed below.

• Customer Match: Upload first-party customer data (email addresses, phone numbers, postal addresses) to Google Ads. Google matches this data with logged-in users and can use the signal as input for Smart Bidding and Optimized Targeting. The richer and more up-to-date your customer list, the stronger the signal.
• Optimized Targeting in Performance Max: PMax uses Google's full machine learning capacity to find new converting users beyond your specified audience signals. The system learns which profiles are most valuable based on actual conversions, similar to Similar Audiences but fully automated and privacy-proof.
• Broad match combined with Smart Bidding: Broad match keywords give Google's bidding algorithm the freedom to reach relevant queries that phrase match or exact match would miss. Paired with Target ROAS or Target CPA, Smart Bidding optimizes for value rather than volume.
• RLSA (Remarketing Lists for Search Ads): Connecting remarketing lists to search ads lets you adjust bids and ad copy for users already familiar with your brand, without needing new audiences.
• In-market and affinity audiences: Google's own audience segments based on current search behavior remain available as a targeting layer for Display, YouTube, and Discovery campaigns.
• Server-side tracking and Enhanced Conversions: Better conversion signals give Smart Bidding more reliable data to optimize on, which indirectly strengthens audience optimization as well.

The key is not one single alternative, but the combination. Advertisers who integrate Customer Match, Optimized Targeting, and Smart Bidding into a coherent strategy see the strongest results. This requires continuous data refreshment, correct tracking setup, and a good understanding of which signals you are feeding to the algorithm.

Effectiveness compared: which strategy works best?

Effectiveness of audience strategies after deprecation

Average improvement in conversion rate vs. no audience strategy

Customer Match + Smart Bidding

34%

Optimized Targeting (PMax)

29%

RLSA + broad match combination

26%

In-market audiences (manual)

18%

Demographics targeting only

9%

Benchmark data from advertisers who actively tested different strategies after the deprecation of Similar Audiences shows that Customer Match combined with Smart Bidding delivers the strongest results, with an average 34% higher conversion rate compared to campaigns without any audience strategy. Optimized Targeting within PMax follows with a 29% improvement, while the combination of RLSA and broad match accounts for a 26% higher conversion rate. Demographic targeting alone, without any audience layer, scores the lowest.

This underlines a fundamental shift: the best audience strategy in 2026 is no longer about manually selecting who you want to reach, but about providing the platform with the richest possible signals so that the algorithm itself identifies the most valuable users. Similar Audiences were essentially an early, manual approximation of what now works fully automatically and more effectively.

First-party data as the new foundation

The deprecation of Similar Audiences makes one thing abundantly clear: advertisers who invest in first-party data are in a fundamentally stronger position in 2026. First-party data, information you collect yourself via your website, CRM, email platform, or customer portal, has become the most valuable asset in a cookieless advertising world.

For an e-commerce player like ToetsJeKennis.nl, this means every customer who purchases an online exam or course becomes a data point that can be fed back into Google Ads via Customer Match. By uploading regularly refreshed customer segments, for example everyone who made a purchase in the last 90 days, you give Smart Bidding a strong signal about which type of user converts. Google uses this not only for remarketing, but also as an anchor for finding similar new users within PMax campaigns.

For a lead generation client like Clima-Active.nl, active in air conditioning and heat pump installations, the same principle applies. Contact details of people who submitted a quote request are mirrored back to Google Ads via Customer Match. The result is that Smart Bidding learns which search and click behavior preceded a high-quality quote request, and subsequently bids more aggressively on similar signals in new campaigns.

How AdBrains AI automates this

The manual approach to audience management, uploading lists, creating segments, maintaining RLSA connections, keeping Customer Match files up to date, is time-consuming and error-prone. This is precisely where AdBrains' own AI technology makes a structural difference.

AdBrains has developed a fully automated audience management system that automatically creates, updates, and connects PROD audiences, Incubator audiences, and RLSA audiences to the right campaigns on a weekly basis. New visitors, buyers, and leads are continuously segmented based on behavior and conversion status, without account managers having to invest manual time. This ensures always up-to-date audience lists that serve as strong signals for Smart Bidding.

In addition, AdBrains deploys its own sGTM infrastructure for server-side signal enrichment. This enriches conversion signals with first-party data, such as CRM information and customer value, before they are sent to Google Ads. Advertisers using Enhanced Conversions via AdBrains' server-side setup see on average 28% more tracked conversions, giving Smart Bidding a significantly richer signal to optimize on.

AdBrains' multi-agent verification system ensures that every change in audience configuration, whether a new RLSA connection, a Customer Match upload, or an adjustment of audience signals in PMax, is checked by four independent AI agents before going live. This structurally prevents errors such as accidentally excluding a valuable audience layer or linking the wrong list to a campaign.

For E-4motion.com, the online shop for new electric folding bikes, this means in practice that visitors who browsed a specific bike category are automatically segmented into dynamic RLSA lists. People who requested a test ride are placed in a separate audience for lead generation optimization, while previous buyers are used as a Customer Match signal for PMax campaigns targeting new customers with a similar profile. All of this happens without manual intervention, automatically refreshed every week by the AdBrains AI.

AdBrains AI audience management: key KPIs

Average results for clients using automated audience management

+34%

higher conversion rate with Customer Match + Smart Bidding

Weekly

automatic refresh of PROD, Incubator and RLSA audiences

+28%

more tracked conversions after server-side signal enrichment

4x

faster audience building via AI vs. manual management

Practical steps for advertisers without AdBrains automation

Still managing your Google Ads account manually? These are the steps you need to take now to compensate for the absence of Similar Audiences as effectively as possible.

1. Audit your first-party data sources: What customer or lead data do you have available in your CRM, email platform, or POS system? Map out which data you can export for Customer Match.
2. Set up Customer Match and keep lists current: Upload an updated customer list to Google Ads monthly, preferably weekly. Maximize the match rate by including email addresses, names, and postal codes.
3. Activate Optimized Targeting in PMax: Ensure your PMax campaigns include audience signals based on your Customer Match lists and remarketing audiences. This helps the system find the right profiles faster.
4. Combine broad match with Target ROAS or Target CPA: Give Smart Bidding room to find new users by using broad match, but steer on value by setting a concrete bidding strategy.
5. Implement Enhanced Conversions: Send first-party conversion data (hashed email addresses at purchase or form submission) to Google Ads for a richer conversion signal.
6. Actively monitor search terms: Without the protection of Similar Audiences as a defined audience layer, proactive negative keyword management is more critical than ever to prevent budget leaking to irrelevant queries.

Comparison: lookalikes versus modern alternatives

The table shows that modern alternatives outperform the original Similar Audiences on virtually every dimension, provided the advertiser is willing to invest in quality first-party data and a correct technical setup. The transition requires effort upfront, but the long-term performance gains make it well worthwhile.

The role of Performance Max in the new audience strategy

Performance Max is, in 2026, the central campaign type within which Google's lookalike logic continues to live, albeit fully automated and invisible to the advertiser. PMax campaigns continuously analyze which user profiles convert and expand their reach to similar profiles through Google's own audience models. This is in effect a more advanced version of Similar Audiences, powered by more data and more sophisticated machine learning.

However, the quality of your PMax campaigns depends entirely on the quality of the audience signals you provide. A campaign without signals learns slowly and can waste significant budget on non-converting users during the learning phase. A campaign with rich Customer Match lists and conversion-focused RLSA audiences as signals learns exponentially faster and reaches the right profiles much sooner.

For HACCP-cursus.com, offering online food safety courses, this means that a PMax campaign fed with Customer Match data from previous course participants, combined with audience signals based on in-market segments for professional training, finds new relevant buyers significantly faster than a campaign starting from scratch.

Frequently asked questions about Similar Audiences and the alternatives

Are Similar Audiences permanently gone from Google Ads?

Yes. Google permanently removed Similar Audiences from all campaign types in Google Ads in August 2023. Existing audiences were automatically deactivated. The underlying logic has since been fully absorbed into Optimized Targeting within Performance Max and into Smart Bidding algorithms, but as a standalone, visible audience segment, Similar Audiences no longer exist.

Is Customer Match a full replacement for Similar Audiences?

Customer Match is not a direct one-to-one replacement, but it functions as a powerful alternative when combined with Smart Bidding and PMax. Where Similar Audiences automatically generated a lookalike segment, you use Customer Match as a signal that allows the algorithm to identify and reach similar users itself. Results are comparable or better, but it does require current and rich first-party data from the advertiser. The more frequently you update your lists and the higher the match rate, the stronger the signal.

Does Optimized Targeting in PMax replace lookalikes?

Yes, Optimized Targeting is the most direct functional successor to Similar Audiences. The system actively looks beyond your specified audience signals for new users likely to convert, based on behavioral patterns and conversion data. The advantage over the old lookalikes is that Optimized Targeting continuously learns and adapts to current conversion signals, whereas Similar Audiences used a more static profile. The drawback is that you have less direct control over who the system targets.

How do I ensure my Smart Bidding strategy finds the right audiences without Similar Audiences?

The most effective approach rests on three pillars. First, ensure rich and current conversion signals via correct conversion tracking, Enhanced Conversions, and ideally server-side tracking. Second, regularly upload updated Customer Match lists so Smart Bidding has a strong profile of what your valuable customer looks like. Third, use audience signals in PMax based on your best customer segments and remarketing lists. Combine this with broad match for search ads so the algorithm has enough room to discover and convert new relevant queries.