When trying to grow your customer base, it’s easy to think “more people equals more success.” But if you’re not reaching the right audience, your marketing efforts can fall flat, even with a huge base. That’s where lookalike audiences come in—they help you find new customers who resemble your best customers. By leveraging data, lookalike audiences can expand your reach without sacrificing relevance.
While it may sound like a simple tactic, not every lookalike audience delivers great results. If you’ve ever struggled with wasted ad spend or low engagement, it might be because your lookalike audience isn’t as effective or intelligent as it could be.
In this article, we’ll explore the factors that influence the effectiveness of lookalike audiences and how refining these models can lead to stronger, more impactful marketing campaigns.
Defining Lookalike Audiences
Lookalike audiences are just what they sound like: a group of people who represent a specific demographic. They’re used to help businesses expand their reach by targeting individuals who closely match the overall profile of their best customers, which includes data from demographics to geographic to purchase history. Businesses typically craft these audiences using first-party data—information directly collected from customer interactions, such as transaction histories, website visits, and engagement metrics.
Replicating your best customers is no easy task. Creating a lookalike audience involves developing customized data models tailored to individual data sets to extract valuable insights. Data scientists employ advanced algorithms and machine learning techniques to analyze this data and identify patterns. Once the groundwork is laid, these models sift through the data to spot similarities and rank potential customers by how similar they are to your current ones. Marketers can then test multiple lookalike audiences against each other.
Defining lookalike audiences is a fairly sophisticated data science activity requiring expertise in data aggregation and analysis. The process results in a smaller lookalike audience accurately aligned with your target customer profile.
How Lookalike Audiences are Used in Marketing
Marketers often start with a broad lookalike source audience to enhance the effectiveness of campaigns across various media channels. They use insights from these audience profiles to craft strategies that target individuals resembling the brand’s top customers. Although this approach is somewhat imprecise, it effectively expands reach and promotes higher conversion rates.
Narrowing down this seed audience to a finely-tuned, smaller lookalike audience poses more of a challenge. However, the trade-off in audience size for increased relevance can lead to a potentially higher ROI. This new custom audience source lays the groundwork for creating a similar audience that is even more targeted and effective.
To create a custom audience, marketers must integrate additional insights and data points. One effective way to refine audience targeting is to use third-party data to sharpen these custom audience profiles. These refined profiles are powerful and drive better results.
Refining Audience Characteristics with Third-Party Data
Integrating lookalike audiences into marketing strategies involves several key steps. Initially, they use the business’s first-party CRM data to build a new audience, identifying key characteristics of top customers. This data is enhanced with third-party attributes to refine the audience further. Here is how data from three well-known third-party data vendors can play a role in refining your lookalike audience:
- Acxiom: Companies rely on Acxiom’s comprehensive spending and interest data to create highly targeted lookalike audiences. By leveraging this data, businesses can achieve more precise targeting based on consumer spending patterns and interests.
- Epsilon: Businesses turn to Epsilon for its ability to project behavioral trends and capture recency data, providing insights into current consumer behavior. They leverage this data to effectively target broader customer segments and refine engagement strategies.
- Experian: Companies use Experian’s extensive credit data and geographic information to explore new markets and optimize customer targeting based on location. Their data offers insights into customers’ financial behaviors, allowing for more targeted marketing in dispersed campaigns.
Third-party data helps marketers fine-tune their targeting, reaching the right people with messages that really resonate based on their behaviors and interests. It’s a more effective way to drive engagement and make sure your marketing efforts hit the mark.
Optimizing for Engagement with Machine Learning Algorithms
Once you’ve created a lookalike audience, combine the data with machine learning algorithms to optimize your campaign for engagement. For instance, supervised learning models can predict which audience segments are most likely to engage based on previous behavior. By factoring in additional data, such as recent purchase behavior, an intelligent algorithm can generate personalized offers like tailored product recommendations to boost response rates.
Machine learning algorithms like collaborative filtering and content-based filtering analyze audience data and adjust targeting based on insights and performance.
- Collaborative filtering: Examines user behaviors such as purchase history, browsing patterns, and product ratings to find similar characteristics between users. It then recommends products or content that similar users have found engaging. For example, if a group of customers frequently buys eco-friendly home goods, the algorithm suggests these products to other users with similar purchasing habits.
- Content-based filtering: Focuses on the attributes of items themselves, matching product features to user preferences. It analyzes the characteristics of products a user has engaged with and recommends similar items. For instance, if a customer often reads articles about financial planning, the algorithm will suggest more content on that topic.
As a result, marketers can deliver highly personalized content and adapt campaigns dynamically as consumer behaviors shift. Along with personalizing offers, machine learning helps determine the best channels and timing for engagement, increasing the likelihood of conversion. The algorithm’s learning ability allows campaigns to scale efficiently while staying relevant.
Case Study: Online Retailer
A leading online mattress retailer used lookalike audiences to transition from traditional performance channels to direct mail, a new growth channel. They leveraged Postie’s capabilities to build modeled audiences and track sales attribution in real time. The campaign was highly successful, achieving a return on ad spend (ROAS) of 350% and an average order value (AOV) of $1800, which was among the highest seen across any of their marketing channels.
The use of lookalike audiences allowed the retailer to precisely target new customers who closely resembled their top buyers, improving the relevance of their direct mail efforts. In turn, they drove higher engagement and minimized the risk of wasted ad spend. By leveraging real-time tracking, the brand continuously optimizes its campaigns, ensuring sustained success in its direct mail strategy.
The Importance of Lookalike Audiences
Lookalike audiences have become a valuable tool for businesses seeking growth while navigating strict privacy regulations. By harnessing first-party data, companies can identify new prospects whose behaviors and interests closely align with their top customers. This highly targeted approach respects user privacy while boosting ROI by focusing on those most likely to engage in an effective and ethical way.
- Enhanced ROI: Directs marketing efforts toward individuals most likely to engage, improving advertising spend efficiency.
- Improved targeting accuracy: Analyzes detailed attributes from existing customer data, enabling finely tuned campaigns that minimize waste and maximize impact.
- Sustained campaign engagement: Continuously refreshes target segments to keep marketing content relevant and engaging, which helps reinforce brand loyalty and drive sustained engagement.
By constantly refining and optimizing campaigns with data insights, businesses can stay ahead, keep customers engaged, and see real results—all while respecting privacy and following regulations.
Lookalike Audiences for Omnichannel Marketing Success
In an omnichannel approach, businesses can effectively merge direct mail with innovative digital marketing strategies.
Let’s say you have a customer list ready to go. With a clever omnichannel strategy, you could take that direct mail piece they’re getting and amplify its impact. How? By syncing it with your email lists and social media efforts. Imagine: a customer receiving a special offer in the mail and the next thing they know, they’re seeing a matching campaign on their favorite social media platform. Maybe they even get a follow-up email reminding them to use their special offer before it expires. It’s a great way to keep your brand top of mind.
By leveraging lookalike audiences, businesses can deliver highly relevant content, personalized offers, and special promotions, ultimately boosting engagement, conversion rates, and return on ad spend (ROAS) across channels.
Postie’s Lookalike Audiences
At Postie, the ability to create lookalike audience segments is one of our core competencies. We tailor our lookalike audiences uniquely for each client using our proprietary models.
The models we use, developed by our expert team of PhDs, harness advanced algorithms and machine learning techniques to distill valuable insights from our Data Management Platform (DMP). With data on over 250 million U.S. consumers, our clients can rapidly generate lookalike models that reflect the traits of their most effective customer segments.
Ready to elevate your marketing efforts? Ready to amplify your marketing reach? Harness the power of lookalike audiences with Postie to connect with high-potential customers who mirror your best clients. Start with a demo and transform your direct mail campaign into a standout success that truly resonates with your target audience.
