Lookalike Modeling

Lookalike modeling helps you reach new users similar to your best customers by using a high-performing first-party audience as a seed and leveraging data partners to create a modeled audience with similar traits.

Guide

• Overview
• Key Benefits
• How to Get Started
• Best Practices & Strategy Tips
• Common Pitfalls & How to Avoid Them

Overview

Lookalike modeling is a powerful way to expand your reach by finding new users who resemble your best existing customers. With Choozle, this involves using a high-performing first-party audience (for example, a CRM or conversion-segment) as a seed audience, and then leveraging data partners to build a modeled audience that exhibits similar traits and behaviors.

Lookalike Modeling Overview Document

Key Benefits

• By using lookalikes, you leverage your best asset-your existing high - value users - to scale efficiently.

• It helps in acquiring new customers who are more likely to convert, improving ROI as compared to broad audience buys.

• The wider industry reports that lookalike modelling using first-party data and advanced identity resolution (cross-device, UID2) enables smarter scaling of net-new audiences.

How to Get Started

• Select a seed audience in Choozle (your best customers, high-value converters, etc.). We recommend utilizing a first party data list uploaded to Choozle via LiveRamp. 

• Submit the seed audience details (advertiser account, campaign flight, budget, goals, geography, KPIs) to Choozle’s account/operations team for modeling. You can contact your Account Manager or Submit a Ticket. 

Best Practices & Strategy Tips

• Ensure your seed audience (1st party/ CRM List) is clean, high-quality, representative (e.g., best customers, recent converters). The stronger the seed, the better the modeling.

• Activate across channels: With platforms like Choozle you can use modeled audiences across display, video, CTV, mobile. This ensures more consistent reach across user devices and contexts.

• Use exclusions: If your goal is net-new customers, exclude your existing customer lists (seed audience) to avoid spending on redundant reach. 

• Monitor, learn, optimize: After launching, review performance of your lookalike segment vs. other audiences. Adjust bids, budget allocation, or audience size accordingly.

Common Pitfalls & How to Avoid Them

• Weak seed audience: If your seed is too small or not reflective of your best customers, the model will be weak.

• Too broad lookalike: Bigger audience size often reduces precision and may raise costs or lower conversion rates.

• Neglecting exclusions: Without excluding existing customers you risk overlap, wasted spend, and inflated frequency.

• Delayed measurement: Don’t wait too long to assess performance. Early metrics (post learning phase) can indicate if the audience is working or needs adjustment.