Alex Lindahl recently shared something that caught my attention: "Stop wasting ad spend on non-buyers!" He followed it with a very specific promise: Clay Ads (just launched) can "multiply your match rate" so your ad spend becomes more efficient, more targeted, and more optimized.

That framing is blunt on purpose. If your ads are being shown to people you cannot reliably match to real users on a platform, you are paying for reach that will never materialize. And if your audience workflows are brittle, you end up spending more time moving CSVs around than improving the campaign.

In this post, I want to expand on what Alex is pointing at: match rate is not a vanity metric. It is a direct lever on performance, measurement, and wasted budget.

When you upload a customer list or build a retargeting audience, platforms like LinkedIn and Meta try to match your records (emails, phone numbers, names, company details) to their internal user IDs.

If the platform cannot confidently match your records, those people never enter the audience. That creates a silent failure mode:

You think you are targeting 10,000 high-intent contacts.
The platform only matches 3,000.
You optimize creative and bidding against a partial, skewed slice of the market.

Low match rates do not just reduce scale. They can also distort results. The matched subset might over-index toward people with consistent personal emails, stable profiles, or recent engagement. That means your audience is not only smaller, it can be biased.

Alex described the practical pain that comes with this:

"No more... manual CSV uploads, manual orchestration, low match rates."

Most teams have lived this. Someone exports a list from a CRM, another person hashes emails, someone else uploads it to Meta, then you do it again next week because the list changed.

What Alex is really describing: identity resolution plus orchestration

Alex says Clay Ads works by using "the same approach we always have to improve fill rates." In plain English, that is a two-part system:

Identity resolution: take contact data and find the correct platform-relevant IDs.
Orchestration: keep audiences synchronized into ad platforms without manual steps.

He explains it like this: "We take contact data, run it through a waterfall of providers to find the ID, and then orchestrate the audience into the ad platforms. All synced, no manual steps."

That "waterfall" concept matters. One provider rarely has perfect coverage. A waterfall strategy queries multiple data sources in sequence, stopping when an ID is found. Done well, this increases match rates without you needing to constantly rebuild your data pipeline.

Why higher match rate changes campaign math

A higher match rate is not just more reach. It can improve:

1) Efficiency

If you intended to spend $5,000 to reach known buyers but only half match, you are effectively spending $5,000 to reach half the audience. Raising match rate can reduce the effective cost to reach the same number of people.

2) Targeting integrity

When your buyer list matches reliably, you can run tighter experiments:

High-intent accounts vs. lookalikes
Pipeline stage retargeting
Exclusion lists to prevent wasting spend on existing customers

3) Measurement and learning speed

Matched audiences give platforms more signal about who you actually want. Better signal can improve delivery and shorten the time it takes to learn what creative and offers work.

Alex shared early benchmarks that make the impact concrete:

LinkedIn: 71-93% match rate (1.7x)
Meta: 43-62% match rate (3.7x)

Even if your numbers differ, the principle holds. When you move from "some" matching to "most" matching, everything downstream gets easier.

The workflow Alex outlined (and what to watch for)

Alex breaks the process into four steps:

Step 1: Pull in an account list

This is your ICP account universe, named accounts, or ABM target list. The quality of this list matters more than people admit. Garbage in, garbage out.

What I would add: define the purpose of the list upfront.

Pipeline acceleration: target open opportunities and late-stage accounts
Pipeline creation: target high-fit accounts with known personas
Customer expansion: target existing customers for cross-sell

Step 2: Find contacts at accounts

This is where teams often cut corners. If you only have companies and not the right people, you are not building a buyer audience, you are building a logo collection.

The key is persona coverage: job titles, seniority, region, and department. For LinkedIn especially, title and company signals are strong, but only if you have enough correct contacts.

Step 3: Run a waterfall

This is the identity resolution layer Alex highlighted. You are trying to map your contact records to platform-recognizable identifiers.

Practical considerations:

Data hygiene: normalize email casing, remove obvious typos, standardize phone formats.
Deduplication: avoid inflating list size with duplicates across sources.
Consent and compliance: ensure your data use aligns with your policies and relevant regulations.

Step 4: Sync to ad platform campaigns (Meta + LinkedIn)

This is the operational win. The value is not only that audiences get built, but that they stay current.

If audiences sync automatically, you can:

Add newly qualified leads into retargeting in near real time
Remove converted customers from acquisition campaigns
Keep ABM lists up to date as your account list evolves

Alex emphasizes the outcome: "All synced, no manual steps." That is the difference between a one-off audience upload and a system that compounds.

Where this fits in a modern GTM motion

Alex helps people use AI and Clay to modernize GTM, and this is a good example of what "modernize" should mean in practice: reduce handoffs, make data more actionable, and close the loop between your CRM and your ad platforms.

In a mature motion, paid social is not just for cold awareness. It becomes an extension of your revenue workflow:

Sales identifies target accounts
Marketing builds persona coverage and messaging
Data and ops ensure identity resolution and audience integrity
Ads deliver to the right buyers and suppress the wrong ones

The real goal is not "run more ads." It is to stop paying to reach people who were never in your buyer set in the first place.

A quick self-audit you can run this week

If Alex's post made you think, here are a few questions to pressure-test your current setup:

What is your current match rate on LinkedIn and Meta for customer lists?
How often are audiences refreshed, and is it automated?
Are you excluding existing customers and closed-lost accounts from acquisition?
Can you segment by buying stage (MQL, SQL, opportunity) without a spreadsheet?
Do you know which fields are driving match failures (missing emails, personal vs work emails, phone formatting)?

If you cannot answer these quickly, you are likely paying the "manual orchestration" tax Alex called out.

Closing thought

Alex Lindahl's point is simple and sharp: stop wasting ad spend on non-buyers. The mechanism he highlights, better match rates through a provider waterfall plus automated syncing, is one of the most practical ways to make that real.

If your team is serious about efficiency, you should treat audience matching as infrastructure, not a campaign task.

This blog post expands on a viral LinkedIn post by Alex Lindahl, I help people use AI & Clay to modernize GTM. View the original LinkedIn post →

The hidden tax in paid social: low match rates