Why match rate figures vary so widely between suppliers
Ask three data enrichment suppliers for a match rate on the same CRM extract and you will get three different answers. That is not evasion. It reflects genuine differences in file depth, matching logic, and the match key your CRM provides. Understanding those differences is the first step toward evaluating any enrichment quote honestly.
Match rate is defined simply: the percentage of your input records for which the supplier can return at least one appended field. A 70% email match rate means the supplier found a business email for 70 out of every 100 input contacts. The other 30 are unmatched and stay blank in your output file.
Three variables drive most of the variation in quoted rates:
- File depth: a supplier with 3 million UK B2B contact records will match fewer of your Finance Directors than one with 8 million. Volume matters.
- Matching tolerance: a supplier that accepts a partial company-name match or a county-level postcode match will quote a higher rate but introduce more errors. Tighter tolerance equals lower rate and better precision.
- Your input quality: a CRM where 20% of company names are trading names that differ from the legal name will depress your match rate below what the same supplier achieves for a cleaner client.
Match rate benchmarks by append type: what the numbers actually look like
The table below shows typical UK B2B enrichment match rate ranges by append type. These are working benchmarks based on real file performance, not promotional claims. Your actual results will move within these ranges based on input quality and the sectors you are targeting.
| Append type | Typical UK B2B match rate | Primary match key used | Key constraint |
|---|---|---|---|
| Postal address (registered/trading) | 70-85% | Companies House number or company name + postcode | Multi-site firms may return a registered address that differs from the operational one |
| Business email (named contact) | 55-75% | Company name + job title + first/last name | SMEs with generic contact@ addresses reduce coverage |
| Direct telephone (landline) | 60-78% | Company name + postcode | TPS-registered numbers must be suppressed before use in telemarketing |
| Mobile telephone | 40-60% | Named contact + company + role | Mobile numbers are least frequently disclosed in public corporate sources |
| LinkedIn URL (personal profile) | 50-70% | Named contact + company name | Common names at large firms create ambiguity; coverage falls sharply below Manager level |
| Job title / seniority | 65-80% | Named contact + company | Titles change more frequently than contact details; decay rate around 18-24% per year |
Notice that mobile appends sit considerably lower than everything else. This is not a flaw in the supplier's file; it reflects the reality that UK professionals share their mobile numbers far less willingly in public corporate contexts than their direct-dial or business email. Any supplier claiming 80%+ mobile match rates across a general B2B file should be able to show you a verification methodology, not just a count.
B2B vs B2C enrichment: why consumer files match higher
B2C enrichment match rates are structurally higher than B2B, running 60% to 90% across postal, email, and telephone appends. The reason is the Royal Mail Postcode Address File (PAF): virtually every UK residential address sits in a standardised, nationally indexed format, and most consumer datasets are built with that postal spine at their core. When your input record contains a full postcode and surname, the supplier has a very reliable anchor to work from.
B2B contacts, particularly at SME level, lack that anchor. A sole trader operating from a home office in Sheffield, a two-person consultancy registered at an accountant's address in Bristol, a manufacturer whose trading address differs from the Companies House registered address: all of these introduce ambiguity that reduces match rates even when the supplier's file is good.
The other factor is volume. Consumer lifestyle data compiled through consented channels holds 10 million UK records and more, with multi-channel fields (email, telephone, postal) indexed against residential addresses. B2B files targeting named contacts at specific job functions across 2 million-plus UK companies have far sparser coverage per record. Density differences translate directly into match rate differences.
What drives B2C match rate variation within those ranges?
Age of the input record is the single biggest driver. A consumer address that has not been verified in three years will have drifted: the Royal Mail National Change of Address (NCOA) file tracks around 4 million UK movers per year, which means roughly 7% of your consumer file goes stale annually on the postal field alone. Email and phone churn faster still. Enrichment against a fresh, frequently updated consumer file will match higher than enrichment against a stale one, even on the same input records.
What low match rates usually tell you (and it is not always about the supplier)
A match rate below 40% on a UK B2B enrichment run is a signal worth investigating before blaming the data supplier. In our experience, the most common causes are problems with the input file rather than gaps in the enrichment database.
The four most frequent culprits are:
- Trading names instead of legal names: if your CRM records "Boots" rather than "The Boots Company plc", the matching algorithm has to work much harder and will fail on some records.
- Missing or malformed postcodes: postcode is the second most useful match key after Companies House number. Garbled postcodes (SW1 A1AA instead of SW1A 1AA) break exact-match logic.
- Duplicate or merged records: a CRM that lists the same company under three slightly different names will dilute your apparent match rate even if the supplier matches two of the three.
- Micro-business coverage gaps: sole traders and micro-businesses with fewer than five employees have significantly lower coverage in any enrichment file, because they generate less public record data.
Cleaning your match keys before enrichment typically lifts a borderline result by 10 to 15 percentage points. Standardising company names against Companies House, correcting postcodes against the PAF, and removing obvious duplicates are all pre-enrichment steps that pay for themselves in match rate uplift. See our guide on what data enrichment involves for the full process.
Why very high match rates (above 95%) should concern you
Ninety-five percent sounds like a great result. In almost all real-world B2B enrichment scenarios, it is not. Getting from an honest 75% rate to a reported 95% requires one of three things: a genuinely exceptional file (rare), a very unusual input set (possible but unlikely), or looser matching logic (common).
Loose matching inflates counts by accepting lower-quality evidence of a match. A supplier might match on company name alone without confirming the named contact, or treat a domain-level email ([email protected]) as a named-contact append. Both approaches push the headline rate up while filling your CRM with records that will bounce, return wrong contacts, or connect to entirely different individuals at multi-site organisations.
The false positive test
Ask any supplier quoting above 90% match rates: "What percentage of matched records pass your own verification check?" A credible enrichment provider should be able to give you a verification pass rate separately from the raw match rate. If they cannot, or if they treat those figures as the same thing, treat the quoted match rate with scepticism.
The practical consequence of false positives is wasted spend. In direct mail at 80p to £1.20 per piece, mailing falsely matched records destroys campaign ROI faster than a low match rate would have. In cold email outreach, a high false-positive rate damages your sender reputation. Match quality, not match count, is what protects the economics of any enrichment project.
How match rate interacts with enrichment ROI
Match rate is one term in the ROI equation, not the whole thing. The formula that actually matters is closer to: (matched records x verification rate x conversion rate x deal value) minus (cost per matched record x matched record count). Each term matters.
Consider a practical example. A Hampshire-based software firm wants to enrich 20,000 CRM contacts with direct telephone numbers for a telemarketing campaign targeting IT Directors. Two suppliers quote:
- Supplier A: 80% match rate, £0.30 per matched record, 85% verification pass rate
- Supplier B: 60% match rate, £0.20 per matched record, 97% verification pass rate
Supplier A delivers 16,000 matched records, of which 13,600 pass verification: 13,600 usable numbers at a total cost of £4,800. Supplier B delivers 12,000 matched records, of which 11,640 pass verification: 11,640 usable numbers at a total cost of £2,400. Supplier A gives you roughly 2,000 more usable numbers but at double the cost per usable record. If the telemarketing team's call capacity is 12,000 contacts anyway, Supplier B is the better commercial choice.
The full ROI picture for CRM enrichment is explored in detail in our guide on calculating CRM enrichment ROI for UK campaigns.
How to run a pilot that gives you reliable match rate data
A properly structured pilot tells you what you will actually get, not what a supplier's average client gets. The key design decisions are:
Size and representativeness. Five thousand to 10,000 records is the right range. Fewer than 2,000 gives too little statistical confidence; more than 20,000 is unnecessary for a test. The pilot extract must reflect your full CRM's industry mix, company-size distribution, and geographic spread. Sending only your cleanest records will inflate the pilot match rate relative to your real enrichment run.
Request a breakdown by append type. A blended match rate of 70% might consist of 85% on postal, 72% on email, and 45% on telephone. Each channel has a different deployment cost and a different response dynamic. You need the breakdown to make a channel-level investment decision.
Spot-check manually. Pull 50 to 100 matched records at random and verify them independently: call the numbers, check the email addresses, cross-reference the job titles on LinkedIn or Companies House. A 5% error rate on matched records is within tolerance for most B2B campaigns. Above 10%, the match quality is too low to risk deploying at scale, regardless of what the headline rate says.
Ask about recency. When was the matched record last verified? An email address sourced from a corporate website in 2021 and never re-verified carries a much higher bounce risk than one verified six months ago. Responsible enrichment suppliers can give you a last-verified date or a confidence tier per record.