The Best Data for Lookalike Modeling in 2025(And Where to Find It)

If you want great results from lookalike modeling, it’s not just about the algorithm.

It’s about the data.

The best models in the world can’t save you from bad inputs. Just like you wouldn’t train a chef with rotten ingredients, you can’t train a model with outdated, messy, or incomplete data and expect it to perform.

In this article, we’re breaking down what kind of data actually fuels high-performing lookalike models—and where to find it.

Let’s get into it.

Why the Right Data Matters

Lookalike modeling is only as good as two things:

The quality of your seed audience

and
The depth and accuracy of the data used to model from it

We’ve said it before, and we’ll say it again: garbage in = garbage out.

Most marketers get tripped up here. They’ll pull a basic list of past customers, plug it into a platform’s native lookalike engine (Facebook, Google, etc.), and hope for the best.

The result? Fluffy segments, platform lock-in, and bloated CAC.

The brands that win are enriching their audiences with deeper signals—behavioral, contextual, deterministic and preditive—and they’re doing it across channels, not just inside one walled garden.

The 5 Types of Data That Matter Most

First-Party Data

This is your owned data—CRM records, purchase history, app engagement, email click behavior. It’s foundational and incredibly valuable.

Use it for:

Building seed audiences
Training lookalike models with actual buyer behavior
Linking to other data sources for enrichment

For example, a subscription skincare brand can pull its top 1,500 customers who’ve purchased 3+ times and engaged with email flows in the last 90 days. This is the seed audience.

Behavioral Data

What people do matters more than what they say. Behavioral signals include site activity, app usage, purchase patterns, media consumption, and more.

Use it for:

Enriching seed audiences
Feature engineering in your model
Predicting intent

For example, a fitness app can enrich its seed audience by layering in session frequency, feature usage (e.g., meditation vs. cardio), and device type. Now the model understands how top users behave, not just who they are.

Location Data

Location history is one of the most powerful—and underused—data sets in lookalike modeling. It tells you where people go in the real world, which is often a better indicator of intent than online clicks.

Use it for:

CPG, QSR, automotive, and retail campaigns
Connecting digital and physical behavior
Creating high-intent visit-based segments

For example, a national QSR brand can create a seed audience from users who visited 3+ times in a month, then model a lookalike audience based on shared location patterns, like frequent gym visits or gas station stops before work.

App Ownership & Usage

The apps someone has on their phone are a strong proxy for lifestyle, interests, and intent. And yes—this data is available.

Use it for:

Enriching seed audiences
Defining lifestyle clusters
Targeting high-intent prospects

For example, an insurance company building a lookalike audience of price-sensitive mobile users enriches their data with app usage. People with finance tools, coupon apps, and gig work platforms are strong matches.

Demographics + Lifestyle Segments

These include age, income, household size, occupation, education, and interest-based clusters (like “new parents” or “urban commuters”). They shouldn’t be your only data—but they can refine the model.

Use it for:

Filtering for buying power
Audience expansion
Tailoring creative

For example, a luxury car brand layers income ($150K+), zip code, and known brand affinity into their model to isolate high-potential CTV viewers for a launch campaign.

Where to Find the Data (Top Sources)

You don’t need a data science team to access great data—you just need the right partners.

Here’s a quick cheat sheet:

Data Type	Top Sources	Notes
First-party CRM data	Your internal systems (Shopify, HubSpot, Salesforce)	Clean it up before using
Behavioral Data	Your site/app analytics + enrichment partners like Skydeo	Combine real-time and historical
Location Data	Skydeo, Foursquare, Near	Opt-in deterministic data only
App Usage	Skydeo, Kochava	Great for segmenting by lifestyle
Demographics	LiveRamp, Oracle, Acxiom, Skydeo	Use to refine—not define—audiences
Predictive Signals	Skydeo, Bluecore, custom CDPs	Key for next-level modeling

Pro Tips for Getting It Right

Clean your CRM
Remove duplicates, normalize formatting, and standardize your events (e.g. “purchase,” “subscribe,” etc.)
Use deterministic over probabilistic data
If you can get opted-in, verified behavior—do it. It’s more reliable for model training.
Don’t go it alone
Partner with data providers who offer audience enrichment and predictive modeling—not just raw data dumps.
Match data to activation channels
If you’re running programmatic, CTV, and email, make sure your lookalike audience is portable across all three.

Not All Data is Created Equal

If lookalike modeling is the rocket, data is the fuel. And not all fuel is created equal. You need clean, consented, multi-dimensional data that captures how people actually behave, not just what they clicked last month.

The best marketers in the world aren’t guessing who their next customer is—they’re training models on the right data to go find them.

So before you press “launch” on your next campaign, ask yourself: Are you giving your model the best inputs possible?

Start with Predictive Segments

Don’t have strong first-party data? Get started with Skydeo Audience Marketplace (SAM) — it’s free and gives you instant access to 30,000+ predictive audience segments.

– Try Skydeo SAM for free