Value-Based Lookalikes
Paid MediaAlso: Value-Based Lookalike Audiences · VBL
Quick definition
Value-based lookalikes are an audience-targeting method where an ad platform builds a new audience by matching the traits of your highest-value existing customers, not just any customer. Instead of finding people similar to everyone who ever bought, it prioritises similarity to the ones who spent the most.
How it varies across Australia
Value-based lookalikes tend to outperform standard lookalikes on return on ad spend (ROAS) across most Australian ecommerce accounts, though the gap narrows once a customer file lacks enough high-value buyers to seed a strong match. Adoption still lags in smaller Australian advertisers who default to standard lookalikes out of habit.
See acquisition benchmarks across Australian industries →What it actually means
Imagine asking a friend to introduce you to more people at a party. A standard introduction just points you at anyone who showed up. A value-based introduction points you at the guests who actually buy rounds. Value-based lookalikes work the same way inside ad platforms like Meta and Google.
You upload a customer list with a value attached to each person, usually total spend or lifetime value (LTV). The platform's algorithm studies the traits of your highest spenders specifically, not your full customer base, and finds new people who share those traits. The resulting audience skews toward people who are statistically more likely to become high-value customers, not just any customer.
The catch is data quality. The model is only as good as the seed list. A file of two hundred customers with messy value data will produce a weaker lookalike than a clean file of two thousand. Most Australian advertisers running value-based lookalikes underestimate how much seed-list hygiene affects the output. Garbage value data in means a garbage audience out, dressed up in confident-looking targeting language.
A standard lookalike finds people who look like your customers. A value-based lookalike finds people who look like your best customers. That difference is the whole point.
How it shows up
Value-based lookalikes show up as an audience type inside Meta Ads Manager and Google Ads customer match campaigns. In reporting, they usually show up as a lift in average order value (AOV) or ROAS compared with standard lookalike audiences run in parallel, even when cost per click (CPC) looks similar or slightly higher. The signal to watch is quality of conversion, not volume of conversion.
The Australian context
Australian advertisers often run value-based lookalikes off customer lists that are too small to seed a strong model, simply because the local market is smaller than the US or UK. Meta generally recommends a seed audience in the thousands for the model to find reliable patterns. Smaller Australian ecommerce brands sometimes need to combine multiple value tiers or extend the lookback window on their customer export to hit a workable seed size.
Where people get this wrong
Value-Based Lookalikes vs Lookalike Audience
| Value-Based Lookalikes | Lookalike Audience | |
|---|---|---|
| What it matches on | Traits of your highest-value customers | Traits of any customer in the seed list |
| Data required | Customer list with value attached | Customer list, no value needed |
| Typical outcome | Higher average order value, similar reach | Larger reach, more variable quality |
| Best used when | You have a clean, sizeable value file | You need scale fast and value data is thin |
Related terms
Common questions
How big does my customer list need to be for value-based lookalikes?
Most platforms recommend a seed list in the low thousands for reliable pattern matching. Smaller Australian advertisers sometimes need to widen the lookback window or combine value tiers to reach a workable size. Below a few hundred, the model has too little signal to differentiate from a standard lookalike.
What value should I use to seed the audience?
Total historical spend or lifetime value (LTV) are the most common choices. Some advertisers use average order value (AOV) instead if they want to bias toward big single purchases rather than repeat buyers. Pick whichever value best reflects the customer behaviour you're trying to replicate.
Do value-based lookalikes always outperform standard lookalikes?
Not always. If your value data is thin, outdated or inconsistent, a value-based lookalike can perform the same as or worse than a standard one. It only earns its keep when the underlying customer file is clean and large enough to give the algorithm something real to learn from.
Can I use value-based lookalikes for lead generation, not just ecommerce?
Yes, provided you have a way to assign value to leads, such as deal size or conversion likelihood from your CRM. It requires exporting that value alongside the contact list, which most CRM and ad platform integrations support directly.
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New Rebellion is a marketing intelligence consultancy. We build tools, score Australian businesses on how their marketing actually performs, and publish Debrief every day. This dictionary is part of how we work in the open.
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