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One provider misses. A data waterfall doesn't.

Cascade through sources until the contact verifies.

A data waterfall is what happens when one provider isn't enough: a record cascades from source to source until a verified detail turns up, instead of stopping at the first lookup that returns something plausible.

recruiter-lexikon / data-waterfall
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Data waterfall
Data waterfall
Defined
Definition

An enrichment technique that cascades a contact or company record through multiple data providers in sequence, one after another, until a verified email, phone number or direct dial is found.

At a glance
Term Data waterfall
Used for Maximising verified match rate on contacts and accounts
In boilr Runs automatically behind every enrichment, no manual setup
b
boilr turns this term into a task
Defined here · operationalised by your AI employee

Data waterfall, explained for the desk.

What it is, why it matters, and how your AI employee runs it.

What it is

A data waterfall, also called cascade enrichment, is a method for enriching a contact or company record by querying multiple data providers in a fixed sequence rather than relying on a single source. Provider A is tried first; whatever it cannot verify, an email, a direct dial, a job title, passes to provider B; whatever B cannot resolve passes to provider C, and so on, until a verified data point is found or the list of sources is exhausted. The record only moves on to the next provider in the chain if the current one falls short.

The technique comes from sales-ops and RevOps tooling, where teams stitch together several enrichment vendors, a contact database, a phone-verification tool, a professional-network lookup, because no single vendor covers every contact well. Recruitment BD faces the identical problem: a hiring decision-maker's email might sit in one provider's database and their direct dial in another's, and neither has both on its own.

One provider missing a contact is normal. Stopping the search there is the mistake.

Why it matters

A single data provider typically verifies only part of the contacts you ask it about; coverage in the 40 to 60 percent range from one source alone is common industry experience, which means close to half your target accounts can come back with no usable contact at all. Waterfalling several providers against the same record is how that gap closes, because each additional source only has to fill in what the ones before it missed.

The alternative to a waterfall is not "no enrichment", it is a single-source lookup that quietly fails on a large share of accounts and leaves a consultant guessing at an email format or working from a stale number. That failure is invisible until a message bounces or a call reaches the wrong desk, by which point the account has already been touched with a mistake attached to it.

How boilr handles it

When boilr enriches a company or contact, it does not stop at the first source it checks. Your AI sales employee already draws on more than 10,000 sources; where one does not return a verified detail, the agent moves to the next until it lands a verified email, phone number or direct dial, or is confident enough in what it found to act. You never see the individual provider calls, only the outcome: a contact that has actually been checked.

That is different from stitching a waterfall together yourself in a tool like Clay, where someone still has to design the sequence, maintain the rules and decide when a lower-confidence result is good enough to use. In boilr the waterfall runs automatically behind every enrichment, and the verified result is stored in the Company Brain, so the next task for that account starts from a checked record instead of repeating the search.

Questions, answered.

Everything a working consultant asks about data waterfall, and how boilr puts it to work.

What is the difference between a data waterfall and data enrichment?

Data enrichment is the broader goal, filling in the fields a raw record is missing. A data waterfall is one technique for doing that: querying multiple providers in sequence rather than a single one, so a gap in provider A's coverage gets picked up by provider B or C instead of being reported as unknown.

Why not just use the single best data provider?

No single provider has full coverage. One will verify more contacts in one sector, another more in a different role type, and match rates for any one source commonly sit well under 100 percent. A waterfall accepts that no provider is complete and uses several to cover each other's gaps rather than betting the whole search on one.

Does a longer waterfall always mean better data?

Not necessarily. Each additional provider adds cost and, if the sequence isn't ordered sensibly, redundant lookups on data the first source already covered. A well-built waterfall orders providers by strength for the fields you actually need and stops as soon as a verified result appears, rather than querying every source regardless.

Can a data waterfall be used for candidates as well as companies?

Yes. The same cascade logic applies to verifying a candidate's current email or number across several sources, not only to enriching a target company's decision-maker. The technique is provider-agnostic, it works on any record with a gap to fill.

How does boilr use a data waterfall in practice?

Whenever boilr enriches a company or contact, it checks across its network of 10,000+ sources and moves to the next one whenever the current source can't verify a detail, until it has a verified email, phone number or direct dial. You never configure or watch the sequence, you just receive a task built on a contact that has actually been checked, with the result stored in the Company Brain for next time.

Helen Wright
Boilr gave us the BD structure and follow-up support to sign our first client and secure a job brief in under a month.
Helen Wright
Managing Director, 923 Jobs

Never work an unverified contact again.

boilr cascades every enrichment across 10,000+ sources until a detail is verified, no waterfall to build or maintain. One AI sales employee per consultant, doing the checking for you.