What is waterfall enrichment?
Waterfall enrichment chains data providers in sequence: the first source fills what it can, the next takes the misses, then the next, until coverage is maxed. It exists because single-source data has a hard ceiling — contact accuracy plateaus near 78–84%, and a single database has no fallback when a record isn't in its index. A well-configured waterfall regularly pushes match rates past 90% and lifts direct-dial coverage 20–40% above any one provider.
How it works
Instead of accepting whatever one database returns, a waterfall sends the unmatched records to a second provider, then a third, and so on. Source A might fill 60% of a list; the remaining 40% flow to source B, then C, until the chain is exhausted.
The output is higher coverage than any single tool, because no one database covers everyone — but collectively they cover far more.
The single-source ceiling
Major databases are effectively single-source for enrichment: if a record isn't in the index, there's no native fallback and you absorb the blank. That's why accuracy plateaus around 78–84% and drops sharply outside mainstream North American B2B.
Where waterfalls still fall short
A waterfall is only as good as the sources in it. If none of your providers cover a market — common for niche verticals and data-poor segments — chaining them just stacks the same blanks. That's the point where custom sourcing, not more providers, is the fix.
Common questions
A tool helps, but the result depends entirely on how well the chain is built and which sources are in it. Done poorly, a waterfall underperforms a single good source. Done well, it clears 90%.
Then a waterfall can't help — you have to source the data at its origin. That's the gap custom scraping fills.