How to create lookalike audiences using generative AI

Imagine having a crystal ball that could help you identify your next best customer. That's the promise of “lookalike” audiences: a powerful tool that’s long been a secret weapon for savvy sellers.

But what if we told you there's a way to supercharge this process? That's where generative artificial intelligence (genAI) comes into the picture, promising to transform lookalike audience creation from an art into a precise science.

In this article, we'll explore genAI's use in sales when it comes to building lookalike audiences – making it faster, more efficient, and potentially more accurate than ever before. Whether you're a seasoned pro or new to the concept, you'll discover fresh insights and practical strategies to elevate your targeting game. 

If you’re new to lookalike audiences or need a refresher on the basics, check out our FAQ section at the end of this article for some quick definitions and examples.

How are lookalike audiences created?

Creating lookalike audiences has traditionally been a multi-step process that relies on data analysis and advertising platform algorithms. 

Here's a more detailed look at how it's typically done.

Step #1: Identify your “seed” audience.

• Start by defining your best customers or most valuable leads – this is also known as your ideal customer profile (ICP).
  • This could be based on criteria like purchase history, engagement level, or lifetime value.
• Create a list of these high-value individuals, often called your "seed" audience.

Step #2: Gather comprehensive data.

• Collect as much relevant data as possible about your seed audience. This may include:
  • Demographics (e.g., age, gender, location, income level, education)
  • Psychographics (e.g., interests, values, lifestyle)
  • Behavioral data (e.g., purchase history, website interactions, app usage)
  • Professional information (e.g., job title, industry, company size)

Step #3: Choose a platform and upload your data.

• Select an advertising platform that offers lookalike audience creation (e.g., Facebook, Google Ads, LinkedIn).
• Upload your seed audience data to the platform, ensuring it meets the platform's format requirements.

Step #4: Set parameters for your lookalike audience.

• Decide on the size and specificity of your lookalike audience.
• Some ad platforms allow you to choose a percentage (e.g., 1% lookalike means finding the top 1% of users who most closely match your seed audience).

Step #5: Let the ad platform's algorithm do its thing.

• The platform uses its proprietary algorithm to analyze your seed audience and find users with similar characteristics.
• This process involves complex pattern recognition across vast amounts of user data.

Step #6: Review & refine.

• Once the ad platform generates your lookalike audience, review the size and estimated reach.
• You may need to adjust your parameters or seed audience if the results aren't what you expected.

Step #7: Implement & test.

• Use your new lookalike audience in your ad campaigns.
• Monitor performance and compare it to other audience targeting methods.

Step #8: Iterate & optimize.

• Based on campaign results, refine your lookalike audience strategy.
• This might involve updating your seed audience, adjusting parameters, or creating multiple lookalike audiences for different purposes.

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How can generative AI make this process easier?

GenAI is revolutionizing the way we build lookalike audiences, offering significant improvements in efficiency, accuracy, and scalability. 

Full disclosure: this is going to be a meatier section, so we’ve provided links to each of the eight ways in which genAI makes building lookalike audiences even easier. If you want to jump ahead, review the list and click on the link to the benefit(s) you want to learn more about:

1. Advanced data analysis
2. Feature generation
3. Audience expansion
4. Continuous optimization
5. Personalization at scale
6. Predictive modeling
7. Cross-platform integration
8. Handling unstructured data

Here's a detailed look at how AI can enhance various aspects of the process:

1. Advanced data analysis
   • Pattern recognition: One of AI's most helpful characteristics is that it can identify complex patterns in your customer data that might be less obvious to human data analysts or traditional algorithms.
   • Multi-dimensional analysis: While traditional methods might focus on a handful of key attributes, AI can simultaneously analyze hundreds or thousands of data points to create more nuanced audience profiles.
2. Feature generation
   • Uncovering hidden attributes: AI can suggest new, unexpected characteristics that define your best customers. For example, it might discover that your top customers share a particular online behavior pattern you hadn't considered.
   • Creating composite features: AI can combine multiple attributes to create new, more predictive features. For instance, it might find that a combination of browsing history, purchase frequency, and social media activity is highly indicative of customer value.
3. Audience expansion
   • Synthetic profile creation: Using techniques like generative adversarial networks (GANs), AI can create synthetic customer profiles that closely resemble your existing high-value customers, effectively expanding your seed audience.
   • Lookalike prediction: AI models can predict which characteristics are most likely to indicate a good potential customer, even if those exact combinations don't exist in your current customer base.
4. Continuous optimization
   • Real-time learning: AI models can continuously update and refine lookalike audiences as new customer and prospect data becomes available, ensuring your targeting remains current.
   • Performance feedback loop: By analyzing the performance of campaigns targeting lookalike audiences, AI can automatically adjust and optimize the audience criteria.
5. Personalization at scale
   • Micro-segmentation: AI can create highly specific lookalike audiences for different products, services, or campaign objectives, allowing for more personalized targeting.
   • Dynamic audience adjustment: Some advanced AI systems can adjust lookalike audiences in real-time based on campaign performance and changing market conditions.
6. Predictive modeling
   • Future value prediction: AI doesn’t just identify customers who look like your current high-value customers - it can also predict which prospects are likely to become high-value customers in the future.
   • Churn prediction: Similarly, AI can help create lookalike audiences based on customers least likely to churn, helping with retention strategies.
7. Cross-platform integration
   • Data unification: AI can help unify customer data from multiple sources (e.g., CRM, website analytics, social media, etc.) to create more comprehensive profiles for lookalike modeling.
   • Platform-specific optimization: Advanced AI systems can tailor lookalike audience criteria to perform optimally on different advertising platforms.
8. Handling unstructured data
   • Natural language processing: AI can analyze unstructured data like customer reviews, support tickets, or social media posts to extract valuable insights for lookalike modeling.
   • Image and video analysis: For businesses where visual elements are important, AI can analyze image and video data to identify relevant customer attributes.

By leveraging these capabilities, genAI can significantly streamline the process of creating lookalike audiences, potentially improving their effectiveness while reducing the time and expertise required. Rather than getting bogged down in data analysis and audience construction, reps are now freed up to focus more of their attention on the strategic and creative aspects of building and running campaigns.

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Integration with existing marketing tools

Incorporating AI-powered lookalike audience creation into your current marketing stack is crucial for seamless operations. 

Here's a look at how you could integrate this new approach with your existing tools:

• CRM systems
  • Many AI tools can directly connect with popular CRM platforms like Salesforce, HubSpot, or Microsoft Dynamics. 
  • This integration allows for real-time data syncing, ensuring your AI model always has the most up-to-date customer information.
• Marketing automation platforms
  • Tools like Marketo or Mailchimp often have APIs that allow AI-powered audience creation tools to push and pull data between them.
  • This can help automate the process of creating and updating lookalike audiences based on your latest campaign results.
• Advertising platforms
  • Most major ad platforms (Facebook, Google Ads, LinkedIn) offer ways to upload custom audiences. 
  • Ensure your AI solution can export audiences in a compatible format for easy uploading.
• Analytics tools
  • Integrating with analytics platforms like Google Analytics or Adobe Analytics can provide additional behavioral data to enhance your AI models.
• Data management platforms (DMPs)
  • If you're using a DMP, look for AI tools that can integrate with it to leverage your first-party data and any second- or third-party data you've acquired.

Your goal here is to create a connected and unified ecosystem, where data flows smoothly between your AI tool and other marketing platforms. Ultimately, this will make it much easier for you to do more efficient and effective audience targeting.

Limitations of AI in lookalike audience creation (& ways to overcome them)

While AI can streamline and simplify the process of creating lookalike audiences, it's also important to have realistic expectations of what AI can do – or, most importantly, where it may fall short.

Below, we’ve provided the three most common limitations of using AI in creating lookalike audiences; for each, we’ve also provided some suggestions for potential ways to overcome those challenges:

1. Data quality dependence: AI models are only as good as the data they're trained on.
   • Potential solution(s): Consider implementing rigorous data cleaning processes and regularly auditing your data sources. Also consider using data enrichment services to fill gaps in your customer profiles.
2. “Black box” problem: Some AI models - particularly deep learning ones - can be difficult to interpret. The lack of transparency can make it hard for users to understand why certain audiences are selected.
   • Potential solution(s): Opt for AI platforms that offer explainable AI features or access to customer success teams. These kinds of resources should provide insights into how the model makes decisions, thereby increasing transparency and trust.
3. Over-reliance on historical data: Many AI models primarily learn from past data, which might not always predict future trends.
   • Potential solution(s): Make sure to connect your AI solution to the other marketing tools in your sales tech stack; this will help ensure that your AI is working with the most up-to-date information possible. 
   • When it comes to awareness of industry and/or market trends, this is also a place where you can lean more heavily on the expertise of your human reps as well.

By proactively addressing these limitations, reps can harness the full potential of AI in audience creation while mitigating potential drawbacks. 

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Comparison table: Building lookalike audiences with traditional methods vs. generative AI

To help you decide whether to adopt AI for lookalike audience creation, let's compare it with traditional methods:

While AI-powered methods offer significant advantages in speed, accuracy, and scalability, traditional methods still have their place, especially for smaller campaigns or when working with limited data. The best approach often involves a combination of both methods, leveraging the strengths of each.

How to start building lookalike audiences with generative AI

Now that we’ve covered all the basics, let’s dive into how you can actually start using genAI to build your lookalike audiences. Below, we’ve provided a general eight-step framework that you can use to get started:

Step #1: Understand your current audience.‍

• Deep dive into your customer data:
  • Analyze purchase history, engagement metrics, and customer lifetime value.
  • Identify common characteristics among your top 20% of customers.
• Create detailed customer personas based on your findings.
• Use surveys or interviews to gather additional insights about your best customers.

Step #2: Set clear goals.‍

• Define specific, measurable objectives for your lookalike audience campaigns.
  • Examples:
    • Increase qualified leads by 25%;
    • Boost conversion rates by 15%; or,
    • Expand into a new market segment.
• Align these goals with your overall business and sales strategies.
• Establish key performance indicators (KPIs) to track progress.

Step #3: Collect & organize your data.

• Identify all relevant data sources:
  • Examples:
    • CRM systems
    • Email marketing platforms
    • Website analytics
    • Social media insights
• Consolidate data into a central location or data management platform.
• Clean your data:
  • Remove duplicates, correct errors, and standardize formats.
• Ensure data compliance with relevant privacy regulations (e.g., GDPR, CCPA)

Step #4: Set up your first AI-powered lookalike audience.

• Prepare your seed audience:
  • Export your top customer list from your CRM.
  • Ensure it includes all relevant data points.
• Follow the AI tool's onboarding process:
  • Connect your data sources;
  • Upload your seed audience; and,
  • Configure initial settings (e.g., audience size, targeting criteria).
• Let the AI analyze and generate your lookalike audience.
• Review the generated audience for any unexpected insights or patterns.

Step #5: Test your audience.

• Create a small-scale test campaign:
  • Use a small portion of your regular advertising budget.
  • Run the campaign across multiple channels if possible (e.g., social media, display ads).
• Set up proper tracking and attribution.
• Run the test for at least 2-4 weeks for meaningful results.
• Create a control group using your traditional targeting methods for comparison.

Step #6: Analyze & refine.‍

• Compare the performance of your AI-generated audience against your control group.
• Look at metrics like:
  • Click-through rates;
  • Conversion rates;
  • Cost per acquisition; and,
  • Return on ad spend.
• Identify strengths and weaknesses in the AI-generated audience.
• Use these insights to refine your approach:
  • Adjust your seed audience if needed.
  • Tweak AI settings or parameters.
  • Consider creating multiple lookalike audiences for different purposes.

Step #7: Scale & expand.

• Gradually increase budget allocation to successful AI-powered campaigns.
• Experiment with different types of lookalike audiences, based on:
  • High-value customers;
  • Recent converters; or
  • Engagement metrics.
• Apply learnings across other marketing and sales initiatives.

Step #8: Collaborate & share knowledge.

• Share insights with your team to support their learning and development.
• Collaborate with other departments (e.g., marketing, data science) to improve your approach.
• Consider creating internal best practices or guidelines for AI-powered audience creation.

The key here is to start small, test thoroughly, and gradually expand AI's role in building your lookalike audiences as you become more comfortable. Don't be afraid to ask for help from your team, the AI solution’s support resources, or even external consultants as you navigate this new approach to audience creation.

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Other FAQs about generative AI & lookalike audiences

Now that we've covered how to use genAI for creating lookalike audiences, let's address some common questions about lookalike audiences in general.

Here's a quick sneak peek at the topics we cover here; find the question(s) you want to learn more about and click on the link to jump there:

• What is a "lookalike" audience?
• What are some examples of lookalike audiences?
• How many people do you need to create a lookalike audience?
• Are lookalike audiences just a Facebook thing?
• What's the difference between a custom audience and a lookalike audience?
• Can you create an effective lookalike audience using just emails and no other data? 

What is a “lookalike” audience? 

A lookalike audience is a strategically generated group of potential customers who closely resemble your existing high-value clients or most promising leads in terms of demographics, interests, and behaviors.

What are some examples of lookalike audiences?

Here are some examples of different types of lookalike audiences businesses can create:

• Example #1: An online fitness coach builds a lookalike audience from their email subscribers who consistently open and engage with workout tips. This will make it easier for them to target new users who share similar interests in fitness and are likely to engage with their content.
• Example #2: A luxury car dealership creates a lookalike audience based on customers who have purchased high-end vehicles in the past year. The platform finds users with similar income levels, lifestyle preferences, and browsing behaviors, enabling targeted ads for new luxury models.
• Example #3: A B2B software company creates a lookalike audience based on decision-makers who attended their recent webinar. The platform identifies professionals with similar job titles, company sizes, and industry focuses, allowing for targeted outreach with relevant business solutions.
• Example #4: An eco-friendly household products brand builds a lookalike audience from customers who frequently purchase their organic cleaning supplies. This enables them to target environmentally conscious consumers who are likely to be interested in sustainable home products.
• Example #5: A travel agency creates a lookalike audience based on customers who booked luxury vacation packages in the past six months. The platform identifies users with similar travel preferences and spending habits, allowing for targeted promotions of high-end travel experiences.
• Example #6: An online education platform builds a lookalike audience from students who completed multiple courses and left positive reviews. This allows them to target potential learners who share similar educational interests and are likely to engage deeply with their courses.
• Example #7: A streaming service creates a lookalike audience based on subscribers who frequently watch independent films. The platform identifies users with similar viewing preferences, enabling targeted promotions for new indie releases and film festival highlights.
• Example #8: A pet supply company builds a lookalike audience from customers who regularly purchase premium pet food. This allows them to target pet owners who are likely to prioritize high-quality nutrition for their animals, enabling personalized ads for premium pet products.

How many people do you need to create a lookalike audience?

The size of your seed audience can significantly impact the effectiveness of your lookalike audience:

• Minimum requirements: Most platforms recommend at least 100-1000 people in your seed audience. Facebook, for instance, requires a minimum of 100 people.
• Ideal size: For best results, aim for a seed audience of 1,000-50,000 people. This provides enough data for the algorithm to identify meaningful patterns.
• Quality over quantity: While larger seed audiences can be beneficial, it's more important that your seed audience accurately represents your ICP.
• Platform variations: Different platforms may have different requirements or recommendations, so always check the specific guidelines for the platform you're using.

Remember, creating effective lookalike audiences is often an iterative process. It requires ongoing testing, learning, and refinement to achieve optimal results. This traditional method, while powerful, can be time-consuming and may miss nuanced patterns in your data – which is where genAI comes in to potentially enhance the process.

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Are lookalike audiences just a Facebook thing? 

While Facebook popularized the term with its “Lookalike Audience” tool, the concept is used across various platforms and marketing strategies, including:

• Google Ads - called “Similar Audiences” on that platform;
• LinkedIn - called “Audience Expansion” on that platform;
• X, formerly Twitter - called “Custom Audiences” on that platform;
• Email marketing campaigns; and,
• Programmatic advertising.

What’s the difference between a custom audience and a lookalike audience?

At a high level, this is how you can think about custom audiences vs. lookalike audiences:

• Custom audiences:
  • Definition: A custom audience is a specific group of people you already have a direct relationship with or have collected data on.
  • Source: These are created from your own first-party data.
  • Examples: Your email subscribers, website visitors, mobile app users, or customers from your CRM database.
  • Purpose: To retarget or re-engage people who have already shown interest in your brand.
  • Size: Generally smaller and more focused.
  • Accuracy: Highly accurate as it's based on actual interactions with your business.
• Lookalike audiences:
  • Definition: A group of prospects who share similarities with your existing customers or another defined audience.
  • Source: Created by platforms (like Facebook or Google) using their user data and your seed audience.
  • Examples: People who resemble your best customers, newsletter subscribers, or website visitors.
  • Purpose: To expand reach and find new potential customers who are likely to be interested in your offerings.
  • Size: Can be much larger, helping to scale your sales and/or marketing efforts.
  • Accuracy: While not as precise as custom audiences, they can be highly effective for finding new prospects.

Keep in mind that different tools may have slightly different ways of defining/thinking about these two types of audiences. When in doubt, go with the definitions provided by the tech platforms you’re using.

Can you create an effective lookalike audience using just emails and no other data? 

While it's possible to create a lookalike audience using only email addresses, the effectiveness may be limited. More data points generally lead to more accurate lookalike audiences. 

However, email addresses can still provide valuable information for platforms to match and find similar users.

Final thoughts

GenI is poised to transform what was once a time-consuming and often imprecise process into a streamlined, data-driven engine that can power your outbound strategy. By leveraging the incredible pattern recognition and data analysis capabilities of AI, reps can now uncover insights and target potential customers with a level of accuracy that was previously unimaginable. 

This technology isn't just an incremental improvement - it's a quantum leap forward in how we approach customer acquisition and targeting.

The future of audience targeting is here, and it's brimming with possibility. As you continue to explore AI's applications in your team's workflows, remember that you aren’t just adopting a new platform - you're gaining a powerful ally in your quest to connect with the right customers at the right time. Embrace the potential of AI-driven lookalike audiences, and watch as your campaigns reach new heights of effectiveness and efficiency.