Lookalike Modeling Best Practices to Engage Customers

What is lookalike modeling?

Lookalike modeling is a data-driven ad targeting approach that shows you how to target your audience better by analyzing your current customers' characteristics and finding new people with similar traits.

This targeted marketing strategy uses machine learning to take the guesswork out of finding new customers by focusing on prospects who closely match your existing successful customer profiles.

The result you get from lookalike modeling is your lookalike audience, which consists of potential customers who share key behavioral and demographic characteristics with your best existing customers and are, therefore, more likely to be interested in your offerings.

Rather than starting from scratch when looking for new customers, lookalike modeling lets you build on what's already working.

Building an effective lookalike model starts with understanding your best customers, those who become your seed audience for finding similar prospects.

Think of it as creating a digital template based on your most valuable customers – the ones who buy frequently, stay loyal and engage meaningfully with your brand.

With one audience of your best customers as your target audience, the template then becomes the foundation for finding new customers who share similar characteristics to help you build more audiences and advertise to prospects most likely to convert into loyal customers.

The data-driven process

Lookalike modeling works by analyzing patterns in your customer data to identify what makes your best and most profitable customers unique.

The system examines hundreds of data points about your existing customers – from shopping habits to online behavior. These patterns help identify new prospects who show similar traits and are more likely to become customers.

Data collection and audience analysis

Lookalike modeling relies on your customers' data, so you'll need to gather as much information about your existing customer base as possible. Machine learning models can use data like:

• Demographics (age, location, income level)
• Purchase history (frequency, average order value, preferred products)
• Online behavior (website visits, email engagement, social media interaction)
• Customer service interactions
• Brand engagement patterns

Once collected, this data is analyzed to identify the most significant patterns and characteristics that define your successful customers. The analysis helps create a detailed profile that can be used to find similar prospects in larger databases. This step is crucial because it determines which characteristics carry the most weight in predicting customer success.

When businesses use lookalike modeling, they often see significant improvements in their marketing performance and bottom line. Focusing on prospects who share traits with your best customers can make your marketing budget work harder while achieving better results.

Let's explore the advantages that make lookalike modeling such a powerful tool for business growth.

Improved ad targeting and cost efficiency

Lookalike modeling helps businesses spend their advertising dollars more wisely by targeting people most likely to become customers. Instead of showing ads to everyone, you focus on prospects who match the patterns of your successful customers.

This targeted approach typically leads to the following:

• Lower cost per acquisition
• Higher return on ad spend
• Better ad engagement rates
• More efficient use of marketing budgets
• Reduced wasted impressions on unlikely prospects

Higher likelihood of conversions

When you target audiences who mirror your existing customers' characteristics, you're more likely to attract people ready to buy.

These lookalike audiences tend to convert better because they share important traits with people who already value your products or services. The benefits include:

• Increased conversion rates
• Shorter sales cycles
• Higher average order values
• Better customer retention rates
• More qualified leads entering your sales funnel

Key elements of successful lookalike modeling

The success of your lookalike modeling efforts depends heavily on the foundation you build. Your lookalike models need clean, accurate data and the right mix of customer insights to perform well.

Understanding these essential elements will help you create more effective campaigns that deliver better results.

Data quality and relevance

The strength of your lookalike modeling depends on the quality of data you feed into it.

Poor or outdated data can lead to inaccurate targeting and wasted marketing spend. To build effective lookalike audiences, focus on:

• Recent customer data: Collect data from the past 12 months, as it best reflects current customer behaviors and preferences. Older data might not capture changes in buying patterns or market trends.
• Data accuracy: Remove duplicate entries and verify information accuracy. Bad data leads to poor targeting decisions and can waste your marketing budget on the wrong audiences.
• Consistent formatting: Ensure all data follows the same format and structure. This consistency makes it easier to analyze patterns and build accurate models, while inconsistent formats can lead to errors in your targeting.
• Regular updates: Set up a schedule for cleaning and updating your customer data. Fresh data helps you spot new trends and adjust your targeting accordingly.

Important data points to consider

Successful lookalike models rely on diverse data points that paint a complete picture of your customers.

Information to collect includes:

• Demographics: Beyond basic age and location, look at income levels, occupation, and family status. These factors often influence purchasing decisions and help identify valuable customer segments.
• Purchase history: Track what customers buy when they buy, and how much they spend. This information reveals patterns in buying behavior and helps identify your most valuable customer types.
• Website behavior: Monitor how customers interact with your website, including pages visited, time spent, and the path to purchase. These patterns can indicate interest levels and buying intent.
• Email engagement: Look at open rates, click-through rates, and response patterns. Strong email engagement often indicates customer interest and loyalty.
• Social media activity: Track how customers interact with your brand on social platforms. This shows brand affinity and can help identify valuable customer characteristics.
• Product preferences: Understand which products appeal to different customer segments. This helps target similar customers with relevant offerings.
• Buying frequency: Track how often customers make purchases. Regular buyers often represent your ideal customer profile for lookalike modeling.
• Recency: Consider how recently customers have engaged with your brand. Active customers provide the most relevant data for finding similar audiences.

Not all targeting methods deliver the same results, and choosing the right approach can make a big difference in your marketing success.

To understand why lookalike modeling often outperforms traditional targeting methods, compare it with other common approaches and examine how data-driven decisions can improve your marketing outcomes.

Compared with interest-based targeting

Interest-based targeting is behavioral targeting that takes a different approach than lookalike modeling. It focuses primarily on what people say they like or engage with on social media and other platforms.

While this method can help reach a broad audience, it often lacks the precision that lookalike modeling provides. For example, someone might follow luxury car brands on social media but not have the means or intention to make a purchase.

The main limitation of interest-based targeting is its reliance on self-reported data and social engagement. People often say one thing but do another, making this targeting method less reliable for predicting buying behavior. While it can work well for building brand awareness or reaching new market segments, it typically results in lower conversion rates than lookalike modeling.

From a cost perspective, interest-based targeting might seem more affordable. However, extensive testing and optimization are often required to find the right audience mix.

This process of trial and error can result in higher costs over time as you work to refine your targeting parameters. In contrast, lookalike modeling starts with data from proven customers, typically leading to more efficient spending.

The benefits of data-driven personalization

Lookalike modeling offers a more personalized targeting approach using real customer data to find similar audiences, significantly improving engagement and conversion rates. Here's how this data-driven approach can impact your marketing results:

• Customer understanding: Analyzing actual customer behavior rather than just interests gives you deeper insights into what drives purchases. This helps create more relevant marketing messages that resonate with potential customers.
• Predictive power: Lookalike modeling uses patterns from your successful customers to predict who else might become a customer. As a more predictive approach, lookalike modeling typically leads to higher conversion rates than targeting based on interests alone.
• Marketing efficiency: Your marketing spend goes further when you target people who closely match your best customers' profiles. You're reaching people who are more likely to convert, which means better ROI on your campaigns.
• Message relevance: Understanding the characteristics of your best customers helps you craft messages that address specific needs and pain points. This level of relevance typically leads to higher engagement rates.
• Customer lifetime value: Targeting people similar to your high-value customers means you're more likely to attract customers who will stick around longer and spend more over time. This helps build a more sustainable customer base.

Elevate your audience targeting with lookalike modeling

Looking for a smarter way to find new customers? Lookalike modeling uses data from your current customers to find new people who are likely to love your brand.

Think of it as a shortcut to finding your next best customers. Instead of guessing who might be interested in your products, you're using real data about your existing customers to guide the way.

Mailchimp makes it simple to get started with lookalike modeling. Our tools handle the complex work of analyzing customer data and finding similar audiences so you can focus on growing your business.

Whether you're new to targeting or looking to improve your current marketing, Mailchimp gives you everything you need to create successful marketing and advertising campaigns that reach the right people. Get started with Mailchimp today.

---

Key Takeaways

• Lookalike modeling leverages existing customer data to identify and target similar high-potential audiences, significantly improving marketing efficiency and conversion rates.
• High-quality data collection and analysis are crucial for successful lookalike modeling, with key factors including customer demographics, purchase behavior, and engagement patterns.
• Compared to traditional targeting methods, lookalike modeling offers more precise audience targeting and better ROI through data-driven personalization.
• When implemented effectively, lookalike modeling can lower customer acquisition costs while increasing conversion rates by focusing on prospects who share characteristics with existing successful customers.

---