Make the records you already own more complete.

Every database decays and every database has gaps. A lead form may have captured a name and email but not a phone number. A customer file from a point-of-sale system may have postal addresses but no email. A CRM full of business contacts may know the company name but nothing about its industry, size or location.

Enrichment (often called append) fills those gaps. You send a file of records you already own. Each record is compared against a reference dataset using one or more match keys. When a confident match is found, the requested fields are added to your record. When no confident match is found, the record comes back unchanged — which is the correct outcome. A wrong append is worse than no append.

The key distinction from a list purchase is the starting point. With a list, you're acquiring new records. With enrichment, you supply the people or businesses, and the work is about completing and correcting what you have. That difference matters for compliance too: the relationship you have with the people in your file shapes what you can lawfully do with appended data.

Appends are usually requested by field. Most projects combine two or three of the types below, and the right mix depends on what you plan to do with the data next.

Email append

Email append adds an email address to a record that has a name and postal address (consumer) or a name and company (business). For B2B, the result is a business email address associated with the contact's role at that company. For consumer files, an email append is most appropriate where you already have a customer relationship — for example, reconnecting with past purchasers who shared a postal address but never an email. Appended addresses should always be validated before use; see email validation.

Phone append

Phone append adds a landline or mobile number. Good phone append also tells you the line type — landline, mobile or VoIP — because the rules for contacting each differ. In B2B, the goal is often a direct dial or mobile number instead of a main switchboard. Every appended number used for outreach must be checked against Do Not Call lists, and any automated calling or texting requires the right prior consent.

Postal append

Postal append adds a mailing address to a record that has a name plus another identifier, such as an email or phone number. It's widely used to enable direct mail to digital-first customers and to support identity resolution across systems. Appended addresses should be standardized and checked for moves before mailing.

Firmographic enrichment

Firmographic enrichment adds company-level attributes to business records. It's the most common B2B append because it powers segmentation, territory planning and lead scoring.

industry_naicsIndustry (NAICS / SIC)

Standard industry classification codes plus a readable description.

employee_rangeEmployee count range

Company size band, useful for segmenting small, mid-market and enterprise accounts.

revenue_rangeRevenue range

Estimated annual revenue band. Treat as directional, not audited financials.

hq_locationHeadquarters location

City, state and country of the main office, plus additional sites where known.

company_domainCompany website / domain

Often the single most useful key for joining business records across systems.

tech_stackTechnographics (optional)

Signals about software or platforms a company appears to use, where available.

Demographic and contact-level enrichment

For B2B contacts, enrichment can add job title, seniority and department. For consumer records, it can add selects like age range, household composition or homeowner/renter status. We do not offer sensitive data such as health information, precise geolocation, children's data or financial account data.

A standard append starts with a name and adds a contact point. A reverse append works the other way: it starts with a single contact point — an email address, a phone number or a postal address — and returns the name and other fields associated with it.

Typical, legitimate reasons to reverse append include:

• Identifying the person behind an inbound phone number or email that arrived without a name.
• Completing newsletter subscriber records that only captured an email address, so they can be matched to a CRM.
• Reconciling records across systems where one system stores email and another stores name and address.
• Turning an address-only file into named records so mail is addressed to a person rather than "Current Resident."

Because reverse append can identify people from a single data point, it carries more privacy weight than a typical append. We expect a clearly stated, permissible purpose, and we won't scope reverse append for uses like locating individuals who haven't chosen to interact with you or for anything that could be used to harass, discriminate or surveil.

A match key is the set of fields used to decide that a record in your file and a record in a reference dataset refer to the same person or business. Matching on a single weak field — just a last name, for example — would produce many false matches. Matching on a strong combination — full name plus complete, standardized postal address — produces far more confident ones.

Matching is rarely a simple "exact string equals exact string" comparison. Before records are compared they're normalized: names are parsed into first and last, nicknames may be considered alongside formal names, addresses are standardized, phone numbers are reduced to digits and email addresses are lowercased. The tighter the key and the cleaner the inputs, the more you can trust what comes back.

A match rate is the share of your input records that came back with the requested field appended. It's the first number most people ask about, and it's also the most misunderstood. Any provider that quotes a match rate before seeing your file is guessing. The rate on your project is a product of your data as much as the reference data.

What tends to push match rates up

• Complete inputs. Full names and complete addresses, including unit numbers, give the matching process more to work with.
• Recent records. Contact details collected recently are more likely to still describe where someone lives or works.
• Clean formatting. One value per column, consistent casing and no merged fields reduce parsing errors.
• Strong keys. A company domain or a standardized address is a far better anchor than a name alone.

What tends to push them down

• Old data. People move and change jobs; the older the file, the more records describe someone who's no longer there.
• Partial or junk entries. Initials instead of names, "test" records and placeholder values can't be matched.
• Hard-to-reach segments. Some audiences simply have a thinner footprint in reference data.
• Strict confidence thresholds. Requiring higher confidence lowers the rate — but raises the quality of what does match.

The practical way to set expectations is a test run on a representative sample of your file. You see real output on your own records, you can spot-check accuracy, and the decision to proceed is based on evidence rather than a promise.

Most append problems are input problems. A little preparation before you send a file makes a noticeable difference in what comes back.

1. 01

   Export only what's needed

   Include the fields required for matching plus a unique ID. Leave out anything sensitive or unrelated to the job — data minimization protects you and your customers.
2. 02

   Split fields into their own columns

   First name, last name, street address, city, state and ZIP should each be separate. For B2B, separate company name and domain. Avoid a single "full name" or "full address" column where possible.
3. 03

   Remove obvious junk and duplicates

   Delete test records, internal employees and exact duplicates. Running basic hygiene first means you aren't paying to enrich records you'll throw away.
4. 04

   Flag opt-outs and suppressions

   Mark records that have opted out of contact or asked you to delete their data. Those people should not be enriched for marketing.
5. 05

   Document what you want back

   List the fields you need appended, how you'd like line type or confidence indicated, and how you plan to use the results. Your stated use helps us scope the right fields and confirm a permissible purpose.
6. 06

   Transfer it securely

   Use an encrypted, access-controlled transfer method rather than email attachments. We agree on the transfer method before any file changes hands.

A clean, well-labeled input file usually includes:

• A unique record ID per row
• Parsed first and last name columns
• Street address, unit, city, state and ZIP in separate columns (consumer)
• Company name and website domain (B2B)
• Any existing email or phone values, even if you suspect they're outdated
• A suppression or opt-out flag where applicable

Appended data inherits the obligations of whatever you do with it next. Adding a field doesn't add permission. As a buyer, you remain responsible for lawful use, and we ask for a stated, permissible purpose before any project starts. A few rules come up constantly:

• Email: Commercial email must follow CAN-SPAM — accurate sender identification, a working opt-out and a valid physical postal address. For consumers, we expect email marketing to be opt-in.
• Calls and texts: Under the TCPA, autodialed or prerecorded calls and marketing texts to consumers generally require prior express written consent. Appended mobile numbers do not come with that consent.
• Do Not Call: Any appended number used for telemarketing should be scrubbed against the National Do Not Call Registry, applicable state DNC lists and your internal do-not-contact list.
• State privacy laws: Several states give consumers rights to know, delete and opt out of the sale or sharing of their personal information. If someone has exercised those rights with you, honor them before enriching their record.
• Privacy notices: Make sure your own privacy notice accurately describes how you supplement customer records with information from third parties.