Lookalike modeling is a machine learning technique that allows marketers to identify new prospects who share similar characteristics and behaviors with their existing high-value customers. Lookalike modeling allows marketers to identify people who look and act like their target audience. Lookalike models evaluate a cohort of people using machine learning to find a new set of people who will behave similarly to the cohort audience. For example, if one cohort of users clicked on an advertisement, a lookalike model will attempt to find other users who are likely to click as well.
Finding a target audience can be difficult for marketers that put time, effort and money into marketing campaign engage users and drive them to purchase. Lookalike models can be used to find new audience members that look like existing customers through audience segmentation. Lookalike models seek audiences with similar behavioral data, which brands can target for converting into customers.
How Do Lookalike Models Work?
Lookalike modeling starts with a small seed audience that joins into a larger audience, known as a reference set. A reference set can be supplied by a data provider. It can also be found natively in a Data Management Platform (DMP) or a Demand Side Platform (DSP).
Machine learning models analyze the attributes of the reference set to determine which ones best predict similarity to the seed audience. Lookalike modeling can provide marketers with more targeted and precise customer segmentation compared to broader audience classifications based on age, gender, income and geography. Because of their similarity to known audience segments, lookalike audiences exhibit higher engagement and conversion.
Lookalike Modeling with a Customer Data Platform
A Customer Data Platform allows you to integrate various customer data sources into a repository, where it can be accessed in one place. An extensive set of first-party data is essential to an effective lookalike model. Data stored in the CDP can include online and offline engagement and capture customer touchpoints such as website visits, email engagement, purchases, customer support tickets and product reviews.
Once customer data is stored centrally in the CDP, lookalike modeling begins by identifying people who look and act like your target audience. The CDP can analyze your seed audience, identify their key attributes, and then look for similar customers. A CDP allows you to leverage machine learning (ML) and artificial intelligence (AI) to apply predictive analytics across all of this data.
The next step is to test target segments on online platforms, and assess whether you found the right profiles. A CDP enables you to upload your targeted list to integrated services and marketing platforms.
Lookalike modeling creates better segmentation and targeting for marketing campaigns, which leads to improved advertising effectiveness and higher ROI. Lookalike marketing programs can be highly profitable, leading to improved conversion rates and more targeted marketing opportunities.
FAQ
What is a lookalike model in marketing?
A lookalike model is a machine learning technique that analyzes the attributes and behaviors of your existing high-value customers (a seed audience) and then identifies new prospects who share similar characteristics. Marketers use lookalike models to expand their reach to audiences most likely to convert, improving campaign efficiency and return on ad spend.
How much data do you need to build a lookalike model?
The quality and size of your seed audience significantly affect lookalike model accuracy. Most platforms recommend a seed audience of at least 1,000 to 5,000 customers, though larger and more well-defined seed audiences generally produce better results. A Customer Data Platform can help by consolidating data from multiple sources to create richer customer profiles for seed audience selection.
What is the difference between lookalike modeling and retargeting?
Retargeting focuses on re-engaging people who have already interacted with your brand, such as website visitors or past customers. Lookalike modeling, on the other hand, identifies entirely new prospects who resemble your best customers but have not yet engaged with your brand. The two strategies are complementary—lookalike models expand your audience while retargeting nurtures existing leads.
Related Terms
- Propensity Modeling — Scores individual likelihood while lookalikes find similar prospects
- AI Customer Segmentation — ML-driven segmentation that complements lookalike targeting
- Data Activation — Pushes lookalike audiences to ad platforms for targeting
- Customer Acquisition Cost — Metric that lookalike models help reduce