Lookalike Model

Lookalike modeling creates better segmentation and targeting for marketing campaigns, which leads to improved advertising effectiveness and higher ROI.

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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. Lookalike models seek audiences with similar behaviors, 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 audience segments 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 customer 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 analyze 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.

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