What Is Waterfall Enrichment? How It Works, Costs and Tools in 2026

No single B2B data provider has everyone. In an independent test on 1,000 B2B leads, Apollo found 78% of emails and 41% of mobile numbers, and even ZoomInfo found only 84% and 67%. Waterfall enrichment fills those gaps by checking several data providers in sequence until one returns a result, which is why Clay now runs waterfalls across 150+ databases and Apollo has built one into its platform.

This guide explains what waterfall enrichment is, how it works step by step, the coverage math behind it, what it costs, how Apollo and Clay implement it and how to set up a waterfall that improves your data without wasting credits.

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What is waterfall enrichment?

Waterfall enrichment is a method of filling in missing data, such as an email address or phone number, by querying several data providers one after another. If the first provider doesn't have the data, the request "falls" to the next provider, and so on, until a result is found or every source has been tried.

The name comes from the shape of the process: each provider catches some of what the one above missed. The result is higher coverage than any single provider can deliver on its own.

Why one waterfall enrichment provider is never enough

Every data provider builds its database differently, from public web sources, contributor networks, partnerships and its own verification. That means each one is strong in some regions, industries and company sizes and weak in others. A provider with excellent coverage of US software companies may have thin data on German manufacturers or small local businesses.

The gap is biggest for phone numbers. In Cleanlist's 1,000-lead test, the difference between the two providers was six points on emails but 26 points on mobile numbers. If your team calls a lot, relying on one source leaves a large share of your list unreachable.

Gaps are expensive: Gartner estimates poor data quality costs organizations an average of $12.9 million a year.

How waterfall enrichment works

A typical waterfall runs through the same steps for every record:

Start with what you know. Usually a name and company, a LinkedIn profile or a company domain.
Query the first provider. If it returns a result, the waterfall can stop or continue to validation.
Fall through to the next provider if the first one has nothing, and repeat down the list.
Validate the result, for example by checking that an email address will deliver, before accepting it.
Stop when a validated result is found or every provider has been tried, and record which source supplied the data.

The order matters. Most teams put the providers with the best hit rate for their market and the lowest cost first, and save expensive or niche sources for the records nobody else can fill.

The coverage math behind waterfalls

Here's why waterfalls work, and why they have diminishing returns. Imagine four providers. The first finds 60% of your emails. The second finds half of what's left, the third finds 40% of what's left after that, and so does the fourth.

Illustrative waterfall coverage: 60% with one provider, 80% with two, 88% with three and about 93% with four, showing diminishing returns — How coverage grows with each provider in a waterfall (illustrative). Source: SPONA

Coverage climbs from 60% to 80%, then 88%, then about 93%. Each extra provider adds less than the one before, while every lookup still costs time and credits. That's the core trade-off in waterfall design: the first two or three providers deliver most of the gain, and long waterfalls mainly add cost. These figures are illustrative, but the pattern holds in practice because each new provider is only searching the hardest records.

Email vs. phone waterfalls

Email and phone waterfalls behave differently:

Emails are cheaper to find and easy to verify with an email validator, so email waterfalls can be longer and still pay off.
Phone numbers cost more per lookup and are harder to verify. Apollo notes in its own documentation that phone data typically costs more per provider than email.

Many teams run a short, high-quality phone waterfall only for their best-fit accounts, and a longer email waterfall for everyone else.

A waterfall example, step by step

Here's how one record moves through a typical email waterfall. You start with a name, a job title and a company domain.

Provider A checks its database and finds nothing. On tools that charge only for results, this step costs nothing.
Provider B returns an email address. That return uses credits.
The validator checks the address and flags it as invalid, because the person has left the company. Validation is charged separately.
Provider C returns a different address at the same company. It uses credits.
The validator confirms this address will deliver. The waterfall stops, and the record is saved with the email, the source and the date.

In this example, the record cost two provider charges and two validations. That's why the order of providers and the stop rules matter so much: a well-ordered waterfall finds most results at the first or second step and rarely pays for four.

What data can you get from a waterfall?

Waterfalls are most often used for contact details, but the same approach works for almost any field:

Work emails, the most common use and the easiest to verify.
Mobile numbers and direct dials, where waterfalls make the biggest difference to coverage.
Job titles, seniority and LinkedIn profiles, to confirm you have the right, current person.
Company data such as industry, employee count and location.
Technographics, meaning the software a company uses.
Custom data points, such as "has more than ten locations" or "sells to retailers", found through AI research.

Be careful with personal email addresses and personal phone numbers, especially in Europe. Business contact details are usually the right choice for B2B outreach.

The pros and cons of waterfall enrichment

Pros

Higher coverage than any single provider, especially for phone numbers.
Better results in regions and segments where your main provider is weak.
Less dependence on one vendor's database and contract.
More accurate data when validation is built into each step.

Cons

Costs can add up quickly, because each provider that returns data charges for it.
Someone has to choose providers, set the order and maintain the setup.
More providers mean more vendor relationships, rules and compliance questions.
Long waterfalls add cost with little extra coverage.

Waterfall enrichment API and automation

Most teams don't run waterfalls by hand. They trigger them automatically:

When a record is created, for example when an inbound form is submitted or a new lead lands in your CRM.
On a schedule, to refresh records that haven't been checked recently.
Before outreach, when contacts are added to a sequence and still have missing or unverified details.

Dedicated enrichment tools offer APIs and webhooks for this, and workflow tools like Clay can sync results straight back to your CRM. Two technical details matter: cache recent results so you don't pay twice for the same lookup, and respect each provider's rate limits so large batches don't fail halfway through.

Compliance considerations

More providers means more data sources to account for. Before adding a provider to your waterfall, check how it collects data, whether it offers a data processing agreement and how it handles opt-outs and deletion requests. Record which provider supplied each field, so you can answer if someone asks where you got their details. And make sure opt-outs apply across your whole stack, not just in one tool. This isn't legal advice, but these steps cover the questions that come up most often under GDPR and CCPA.

In Europe, the GDPR applies to every provider in your waterfall, so check each one's data sources and agreements.

Common waterfall mistakes

Too many providers. Many providers license data from the same underlying sources, so adding a fifth or sixth often adds cost without new coverage.
No validation. Unvalidated results from a long waterfall can bounce just as often as a single provider's.
Enriching unqualified records. Waterfalls cost the most when they run on records you were never going to contact. Qualify first, then enrich.
No caching. Re-enriching the same contacts every month wastes credits.
Never reviewing the order. Provider performance changes, so a setup that worked six months ago may now waste money.

Unvalidated results also put your domains at risk, since Google's email sender guidelines penalize senders with high bounce and spam rates.

How to set up a waterfall that pays off

Whatever tool you use, these steps keep coverage high and costs under control:

Decide which fields you need. Emails for everyone, phone numbers only for top-tier accounts, for example.
Test providers on your own records. Run 200 records from your real target market through each candidate and measure hit rate and accuracy.
Order providers by hit rate and cost. Put the best value for your market first.
Add validation. Accept only results that pass an email or phone check.
Set stop rules. Stop at the first validated result, and cap the number of providers per record.
Deduplicate and cache. Don't enrich the same contact twice, and reuse recent results.
Review monthly. Check each provider's hit rate and cost per valid result, and reorder or remove providers that underperform.

How to measure waterfall performance

Track a few numbers for each provider and for the waterfall as a whole:

Fill rate: the share of records that end up with the field you needed.
Valid rate: the share of returned results that pass validation.
Cost per valid contact: total enrichment cost divided by valid results. This is the number that tells you whether a provider earns its place.
Bounce rate and connect rate: the real-world test of whether the data works.

If a provider rarely fills records that earlier providers missed, it's adding cost, not coverage.

When you don't need a waterfall

Waterfalls aren't always worth the setup. You can usually skip one when:

Your main provider already covers your market well, for example 90% or more of the emails you need in a test on your own records.
Your lists are small, so the time spent designing and maintaining a waterfall outweighs the extra contacts it finds.
You only need emails and run campaigns where a few missing contacts don't matter.
Your real problem is the list, not the contact details. If you're unsure which companies and people to target, enrichment won't fix that. You need better list building first.
You sell to local businesses or e-commerce stores, where the gaps come from missing segments rather than missing fields, and dedicated data sources work better than stacking general databases.

Single-source, waterfall or AI research?

There are now three ways to get contact data, and each suits a different team:

Comparison of single-source enrichment, waterfall enrichment and AI research by starting point, coverage, effort and pricing — Single-source vs. waterfall vs. AI research. Source: SPONA

Single-source enrichment is the simplest: one database, one contract, fast results, but gaps wherever that provider is weak.

Waterfall enrichment maximizes coverage for lists you already have, but someone has to design, run and maintain it, and costs grow with every provider in the chain.

AI research platforms start one step earlier, from a description of your ideal customer rather than an existing list. They search many sources, find the right companies and people, verify each lead and explain why it fits.

AI-based research is spreading quickly: Salesforce's State of Sales research found 87% of sales organizations already use AI for prospecting and related tasks.

How Spona covers multiple sources without a waterfall to build

Spona gives you the coverage of multiple sources without designing a waterfall yourself. You describe your ideal customer in a chat, and Spona's AI aggregates 40+ data sources, validates every lead across its digital footprint and delivers a scored list with the reasoning behind each match in about four minutes. Spona states that its leads are 95% accurate.

Enrichment is priced per record. Credits cost $0.008 to $0.01 each, and enrichment uses 1 to 30 credits per record, or about $0.01 to $0.30.
It builds the list, too. Bulk leads cost about $0.01 to $0.09 each, and qualified, scored leads about $0.32 to $4.00, with free samples before you buy.
It works on your CRM. CRM enrichment updates existing records and uses whitelisting and blacklisting, so you don't pay for contacts you already have.
It covers niche markets. Local businesses, Shopify stores and venture-backed companies are included alongside standard B2B data.

Customers see the difference in their results: Qonto reports an 80% higher connect rate and Kaiko a 65% higher connect rate after switching to Spona data. For more on enrichment in general, see our guides to data enrichment and CRM data enrichment.

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FAQ

What is waterfall enrichment?

Waterfall enrichment fills missing data, such as emails or phone numbers, by checking several data providers in sequence until one returns a result. It achieves higher coverage than any single provider.

What is waterfall enrichment on Apollo?

Apollo's waterfall enrichment checks Apollo's own data first, then your connected third-party sources in the order your admin defines, until it finds a match. Each source that returns data charges credits.

What are the best waterfall enrichment tools?

Clay is the most flexible for building custom waterfalls, FullEnrich and BetterContact are simpler and charge only for found or valid data, and Apollo includes waterfall enrichment in its platform. Spona offers multi-source verified data without building a waterfall.

Is waterfall enrichment worth it?

Usually, yes, for teams whose main provider is weak in their market or who need phone numbers. Keep waterfalls short, order providers by value and track cost per valid contact to make sure each step pays off.