Measure it field by field, not in aggregate
An aggregate match rate hides the distribution that matters. Providers typically match company identity far more often than they match the specific attributes a revenue team routes on.
| Field | Coverage tendency | What a miss costs |
|---|---|---|
| Company identity and domain | Highest coverage | Record cannot be matched to an account |
| Industry classification | Moderate coverage | Segment reporting and territory rules break |
| Employee count | Moderate coverage | Segmentation and quota assignment misfire |
| Revenue band | Lower coverage | Deal sizing and tiering lose their input |
| Contact title and seniority | Lower coverage | Scoring and buying-committee logic degrade |
Coverage and correctness are separate problems
A provider that matches nine of ten accounts and is wrong about employee band on a third of them has handed you confident errors, which are more damaging than blanks. A blank field routes to a fallback rule. A wrong field routes to the wrong rep, the wrong sequence, and the wrong forecast segment, and nobody investigates because the field looks populated.
Test both dimensions before you commit. Take a few hundred closed-won accounts where the truth is documented in a contract, submit them, and score the returns for coverage and correctness independently. Then check staleness, since a value that was accurate two years ago is a third failure mode that neither coverage nor correctness catches.
Clean before you enrich
Match rate measured on a dirty database measures the database. Duplicate accounts get matched twice with conflicting values. Records with personal email addresses cannot resolve to a company. Dead domains return nothing. Normalizing and deduplicating first raises the observed match rate without changing the provider, which also tells you how much of your gap was ever the vendor's fault.
What enrichment cannot fix
Enrichment fills firmographic gaps. It does not fix the fields that decide revenue. No provider can tell you the real close date of an open deal, the real amount, or whether the stage reflects the buyer's position, and those are the fields a forecast reads. ORM's view on data quality is that everyone has bad data and consistency matters more than completeness. Data that is wrong in a stable, predictable way can still support accurate prediction, while data that changes definition between reps cannot.
Apply that filter to enrichment spend. Buy coverage on the attributes that route and segment, since those decide who works an account. Do not expect appended firmographics to raise forecast accuracy, because the constraint there sits in the opportunity record, and improving it looks more like the discipline described in sales forecasting best practices than like a data purchase.
Frequently Asked Questions
How is enrichment match rate calculated?
Divide the number of records the provider returned attributes for by the number of records submitted. Calculate it separately for the fields you actually route and score on. A provider can report a high overall match rate while missing employee count on half your target accounts, and employee count is the field your segmentation depends on.
Is a high match rate the same as accurate data?
No, and conflating them is the common evaluation error. Match rate measures coverage. Accuracy measures whether the appended value is correct. A provider can match nine of ten accounts and still be wrong about revenue band on a large share of them, which produces confident misrouting.
Why does match rate drop on your own database?
Vendor benchmarks are run against clean, well-formed lists. Your CRM has typos, personal email addresses, dead domains, and duplicate accounts. Normalize and deduplicate before testing a provider, otherwise you are measuring your own data quality and attributing the result to the vendor.
How do you test a provider before buying?
Submit a sample of a few hundred records where you already know the truth, ideally closed-won accounts whose details are confirmed in contracts. Measure coverage and correctness separately by field, and check how many returned values are stale rather than wrong. That test costs a day and prevents a year of bad routing.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like enrichment match rate into prescriptive action for your team.
Schedule a Demo