[GTM glossary](/glossary.md) Fill rate Data & Enrichment # Fill rate In short Fill rate is the share of records that have a non-empty value in a specific field. If 7,000 of 10,000 contacts have a phone number, the phone fill rate is 70%. It measures completeness, not correctness. A field can be 100% filled with wrong or placeholder values, so fill rate is a starting signal that you read alongside accuracy and freshness. ## How to calculate fill rate Divide the number of records with a valid value by the total number of records, then multiply by 100. Calculate it per field and per segment, not only for the whole database. * **Formula:** Fill rate = records with a value / total records x 100. * **Valid values only:** Exclude blanks, "N/A", "unknown", and obvious junk such as "test@test.com" from the numerator. * **Per segment:** A 90% overall rate can hide a 40% rate in your target region or tier. --- ## Fill rate in enrichment When you run enrichment, fill rate is the main way to judge how much a provider or workflow added. Compare the rate before and after on the same set of records. ### Fill rate vs. match rate Match rate is the share of records the provider could identify at all. Fill rate is the share of fields populated afterward. A provider can match 95% of companies yet fill only 50% of the revenue fields. ### An example Say a team imports 5,000 accounts. Before enrichment, industry is filled on 60% and employee count on 35%. After enrichment, both reach 90%. That tells the team the run worked, but they should still spot-check a sample for accuracy. --- ## Why fill rate matters for GTM * **Segmentation:** You cannot filter or score on a field that is mostly empty. * **Routing:** Territory and round-robin rules break when country or company size is missing. * **Personalization:** Merge fields without values produce blank or awkward email copy. * **Reporting:** Dashboards on sparse fields mislead because they cover only the records that happen to be filled. --- ## Common mistakes * **Chasing 100%:** Filling every field with low-confidence guesses lowers trust. Prioritize the fields that drive decisions. * **Ignoring accuracy:** Report fill rate next to a sampled accuracy check. * **Measuring once:** Fill rate drops as new records arrive and data ages, so track it monthly. --- ## Fill rate FAQs **What is fill rate in data enrichment?** Fill rate in data enrichment is the percentage of records that have a valid value in a given field after enrichment. It shows how much of your database is complete for that field, such as email, industry, or employee count. **How do you calculate fill rate for a CRM field?** Count the records with a valid, non-placeholder value in the field, divide by the total number of records, and multiply by 100. Run it per field and per segment so gaps in your target accounts are not hidden by averages. **What is a good fill rate for B2B data?** There is no universal number. A good fill rate is high enough on the fields your routing, scoring, and outreach depend on that those processes work. Set targets per field based on how each field is used, and check accuracy too. **What is the difference between fill rate and match rate?** Match rate is how many of your records a data provider could identify. Fill rate is how many fields were populated afterward. A high match rate with a low fill rate means records were found but few details were returned. Share term --- ## More related phrases * [Enriching data](/glossary/enriching-data.md) * [CRM data enrichment](/glossary/crm-data-enrichment.md) * [Match rate](/glossary/match-rate.md) ## Similar phrases * [Data coverage](/glossary/data-coverage.md) Data coverage is the share of the accounts and contacts you care about that a dataset actually contains, and how many of the fields you need are filled in for them. * [Data accuracy](/glossary/data-accuracy.md) Data accuracy is how closely the information in your records matches reality, such as whether an email, job title, or company size is correct, and it determines whether outreach reaches real buyers. * [Data decay](/glossary/data-decay.md) Data decay is the gradual loss of accuracy in contact and company records as people change jobs, companies change details, and information goes out of date over time. * [Data enrichment](/glossary/data-enrichment.md) Data enrichment is the process of adding missing or updated information, such as firmographics, contact details, and technographics, to records you already have from internal or external sources. * [Data quality](/glossary/data-quality.md) Data quality describes how well a dataset is fit for its intended use, measured by traits such as accuracy, completeness, consistency, timeliness, and uniqueness.