Data Enrichment

By Federico Donatone · Updated October 15, 2026

Definition

Data enrichment is the process of adding missing information to an existing record, such as a lead or an account, from outside sources. In B2B sales, data enrichment fills fields like a verified work email, a direct phone number, job title, company size or a buying signal, so a team can qualify an account and reach the right person.

How data enrichment works

You start with a partial record, usually a company domain and a name. Enrichment finds the fields you are missing and records where each one came from.

Decide which accounts you will actually work. Enrich those rows and no others.
Write a field contract: the fields the campaign needs and what makes each value acceptable.
Run the first lookup. If it returns nothing usable, try the next provider. This is called a waterfall.
Stop the waterfall as soon as the record meets the contract, so you do not pay twice for one field.
Verify the contact channel and save the source and date next to every value.
Send accepted records to a campaign with a named owner, and route the rest to review.

The order is where most budgets go wrong. Enriching every row before deciding which accounts matter buys data nobody will use, as our guide to B2B data enrichment tools explains.

Common data enrichment fields

Field	What it is used for
Verified work email	Sending without bounces
Mobile or direct dial	Cold calling the decision maker
Current job title	Checking the person still owns the problem
Company size and industry	Matching the account to the ICP
Technology used	Spotting fit, gaps or a competitor's product
Buying signal	Timing outreach to a new leader, funding or hiring

A field is worth paying for only when something uses it. If no message, routing rule or score reads the field, leave it out. Before a campaign, write one sentence per field that says what happens when it arrives: the email enters a sequence, the funding date shapes the opening line, the headcount decides who owns the account.

How accurate is enriched data?

Accuracy varies a lot between providers, and coverage and precision are different things. In the Growth Cab test of 8 contact data providers, run on the same 600 leads with every email verified, the results looked like this:

Provider	Verified emails found	Dead emails labeled valid
Apollo	68.0%	24
Prospeo	65.7%	1
RocketReach	62.8%	63
Lusha	56.5%	49
Hunter	39.3%	3

A provider can find more addresses and still send more bad ones into your sequences, and every bounce hurts your sending reputation. That is why every address should be verified before it enters a sequence, whichever provider found it. The full results, including mobile numbers, are in our lead generation database test. Run a sample of your own accounts before you trust any provider's claim.

How to keep data enrichment costs under control

Judge enrichment by the cost of each record your team can actually use, not by the price per credit.

FormulaCost per usable record = Total enrichment spend ÷ Records accepted into a campaignAs an illustration, if a month of credits and review time costs $1,200 and 400 records pass every check, each usable record cost $3.

From our workRemove duplicates, current customers and excluded accounts before any paid lookup. In tools like Clay, run AI research columns only on rows that already passed the cheaper filters.

If you build enrichment in Clay, our Clay consultant page shows how we set a monthly credit budget and tie every table to a live campaign or CRM field.

Full guideLead Enrichment Tools and What Changes When the Data Is Right

Data Enrichment FAQ

What is a waterfall in data enrichment?

A waterfall sends a record to one data provider and, if that provider returns nothing acceptable, passes it to the next one in a set order. It raises coverage because no single provider finds everyone. Set a stop rule for each field so you do not pay several providers for the same answer.

What is the difference between data enrichment and data cleansing?

Data cleansing fixes what is already in your records: duplicates, typos, wrong formats and outdated values. Data enrichment adds fields that are missing. Most teams clean first, so they do not pay to enrich duplicates or bad rows.

How often should enriched data be refreshed?

People change jobs and companies change domains, so enriched data goes stale. Store the verification date with every field and re-verify important ones before a campaign uses them. Set the interval from the errors you actually see, such as bounces and wrong titles.

Related terms

Waterfall EnrichmentWaterfall enrichment is a method for filling missing contact or company data by querying several data providers in sequence. The first provider runs on every record, and each next provider runs only on the records still missing a valid result. The goal is higher coverage than any single database, at the lowest cost per usable record. Ideal Customer ProfileAn ideal customer profile (ICP) is a description of the type of company that gets the most value from your product and brings you the most value in return. A B2B ideal customer profile lists testable traits such as industry, size, location and a buying trigger, so sales and marketing know which accounts to pursue and which to skip. Buying SignalsBuying signals are observable clues that a prospect or account is moving toward a purchase. They range from company events, such as a new executive or a funding round, to research behavior, engagement with your outreach and questions asked on a sales call. Sellers use them to decide who to contact, when, and with what message. Email Bounce RateEmail bounce rate is the percentage of sent emails that are returned as undeliverable instead of reaching a mailbox. A hard bounce means the address does not exist or the domain cannot receive mail. A soft bounce is a temporary failure, such as a full inbox. High bounce rates damage the sender's domain reputation. Lead ScoringLead scoring is a method of ranking leads by giving each one points for how closely it matches your ideal customer profile and how strongly it shows interest in buying. The total score tells sales who to call first, who marketing should nurture longer, and who to drop from the list. GTM EngineeringGTM engineering is the practice of building the data and automation systems behind a company's go-to-market: finding target accounts, enriching contacts, running AI research, scoring and routing leads, and syncing clean records to the CRM and outreach tools. A GTM engineer builds these workflows, often in Clay, so sellers spend their time on conversations instead of spreadsheets.