Match Rate
Data & TrackingAlso: Identity Match Rate · Audience Match Rate
Quick definition
Match rate is the percentage of your customer records that a platform like Meta or Google successfully matches to a real user identity when you upload a customer list. Higher match rates mean more of your audience can be targeted, excluded or used to build lookalikes.
A rising match rate over time usually reflects better CRM hygiene, not a platform change. Track it the same way you track any other data-quality metric.
How it varies across Australia
Match rates vary widely depending on how clean the source data is and how many identifiers you can supply. Businesses with verified emails and phone numbers tend to sit well above those relying on email alone. The gap is rarely about the platform, it's about the quality of the CRM export feeding it.
See data and tracking benchmarks across Australian industries →What it actually means
Match rate is what happens when you hand a platform a list of your customers and ask it to recognise them. You upload emails, phone numbers, maybe names and postcodes. The platform hashes those identifiers and checks them against its own logged-in user base. Whatever percentage it can confidently match is your match rate.
This matters more than most marketers realise because match rate is the ceiling on everything downstream. Custom audiences for retargeting, exclusion lists so you don't waste spend on existing customers, lookalike audiences built from your best buyers. All of it depends on the platform first recognising who's actually in your list.
The number is closely tied to attribution and conversion tracking generally. If a platform can't match a converting customer back to an ad click, that conversion event either gets modelled or lost entirely. Segmentation quality, CRM hygiene and consent management all feed into whether your list matches well. A messy CRM with outdated emails and inconsistent phone formatting will always underperform a clean one, regardless of how good your campaigns are.
A low match rate isn't a platform problem. It's a mirror held up to your CRM.
How to calculate it
Match rate = Records matched to a known identity ÷ Total records uploaded
Worked example. You upload a customer list of 10,000 records to build a lookalike audience. The platform confirms 6,200 of those records match to known user identities. Match rate = 6,200 ÷ 10,000 = 62%.
The Australian context
Australian phone number formatting causes more match rate damage than most teams expect. A mobile number stored as 0412 345 678 in one system and +61412345678 in another will fail to match even though it's the same person. Standardising to E.164 format before any upload is a simple fix most CRMs never bother with.
Privacy Act obligations also shape what you can and can't upload. Consent needs to cover the specific use, including hashed uploads to third-party platforms, not just general marketing communication.
Where people get this wrong
Related terms
Common questions
What is a good match rate?
There's no universal good number because it depends on which identifiers you supply and how clean your CRM is. A better approach is tracking your own match rate over time and treating any decline as a data-quality signal worth investigating.
Why does my match rate drop after a CRM migration?
Migrations often introduce formatting inconsistencies, duplicate merges gone wrong, or lost fields like phone numbers. Any of these reduce the identifiers available for hashing and matching, which drags the rate down even though the underlying customers haven't changed.
Does match rate affect attribution accuracy?
Yes. If a platform can't match a converting customer to a known identity, that conversion may get modelled instead of directly counted, or dropped entirely. Poor match rate quietly degrades the reliability of your attribution reporting.
Can I improve match rate without more customer data?
Often yes. Standardising existing fields, fixing phone number formatting, trimming whitespace and lowercasing emails before hashing can lift match rate meaningfully without collecting a single new data point.
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About New Rebellion
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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