The single biggest lever in outbound performance is one most teams ignore: email find rate. If your data provider finds emails for 75% of your target prospects, you are leaving 25% of your addressable market untouched. That is not a rounding error—it is a quarter of your pipeline opportunity sitting on the table.
I learned this the hard way during my first year of building pipeline systems. I was relying on ZoomInfo as my sole enrichment provider. The data quality was good—85% email accuracy on the contacts it found—but coverage was the problem. ZoomInfo only had email data for about 78% of the prospects I was targeting. That meant 22% of my carefully selected, ICP-fit, intent-showing prospects never received outreach because I did not have their email.
The solution is waterfall enrichment, and it has become the single most impactful technique in my GTM engineering toolkit. Let me explain exactly what it is, how to build one, and why it consistently delivers 90-94% email find rates across my engagements.
What Is Waterfall Enrichment?
Waterfall enrichment is a sequential data enrichment process where you query multiple data providers in priority order until you find the information you need. If Provider A does not have a prospect's email, you query Provider B. If B does not have it, you try Provider C. And so on down the waterfall until you either find the data or exhaust all sources.
The logic is simple but the impact is profound because different data providers have different strengths. ZoomInfo has excellent coverage for mid-market and enterprise companies in North America. Apollo covers a broader range of company sizes and has strong international data. Clearbit excels at technographic and firmographic enrichment. Lusha specializes in mobile numbers and direct dials. RocketReach has contacts that none of the others carry, particularly in niche industries.
No single provider covers the entire B2B landscape. But by stacking providers in a waterfall, you can achieve coverage that approaches the theoretical maximum. In my testing across thousands of enrichment runs, here are the coverage rates by approach:
- Single provider (ZoomInfo only): 75-82% email find rate
- Single provider (Apollo only): 70-78% email find rate
- Two-provider waterfall (ZoomInfo + Apollo): 85-89% email find rate
- Three-provider waterfall (ZoomInfo + Apollo + Clearbit): 88-92% email find rate
- Four-provider waterfall (ZoomInfo + Apollo + Clearbit + RocketReach): 90-94% email find rate
Each additional provider adds incremental coverage, but with diminishing returns. Going beyond four providers rarely adds more than 1-2% additional coverage and is usually not worth the added complexity and cost.
Building a Waterfall in Clay: Step by Step
Clay is the tool that makes waterfall enrichment practical. Before Clay, building a waterfall required custom code, multiple API integrations, and significant engineering time. Clay turns it into a configuration exercise that takes about two hours.
Here is how I build a production waterfall:
Step 1: Set Up the Clay Table. Create a new Clay table with your target prospect list. The minimum required fields are: company name, company domain, first name, last name, and title. If you have LinkedIn URLs, include those as well—they dramatically improve match rates across all providers.
Step 2: Configure Provider Priority. Add enrichment columns in waterfall order. I recommend this priority based on cost-efficiency and accuracy: ZoomInfo first (highest accuracy, highest cost), Apollo second (good accuracy, moderate cost), Clearbit third (strong for certain segments), then RocketReach or Lusha as the final fallback. Each enrichment column queries its respective provider and returns the available data.
Step 3: Build the Waterfall Logic. This is the critical step. Add a formula column that implements the waterfall: if ZoomInfo returned a valid email, use that. If not, check if Apollo returned one. If not, check Clearbit. If not, check the fallback provider. The formula looks something like: IF(ZoomInfo_email is not empty AND ZoomInfo_email contains "@", ZoomInfo_email, IF(Apollo_email is not empty AND Apollo_email contains "@", Apollo_email, IF(Clearbit_email is not empty, Clearbit_email, RocketReach_email))).
Step 4: Add Verification. This step is non-negotiable. Every email that comes out of the waterfall must be verified. I add a verification column using a service like ZeroBounce, NeverBounce, or Clay's built-in email verification. Invalid, catch-all, and disposable emails are flagged and excluded from outbound sequences. Sending to unverified emails destroys deliverability, and rebuilding sender reputation takes months.
Step 5: Enrich Beyond Email. While the waterfall is running, I add columns for additional enrichment: company revenue, employee count, industry, technologies used, funding status, recent news, and LinkedIn URL. This data feeds into the ICP scoring model and the AI personalization engine. Every additional data point makes the eventual outreach more relevant and more likely to generate a reply.
Need help with this? I build outbound and pipeline systems for B2B companies — and get results in 30–60 days.
Fix your pipeline →Advanced Waterfall Techniques
Beyond the basic waterfall, there are several advanced techniques I use to maximize coverage and quality:
Conditional Routing. Not every prospect needs to go through the full waterfall. If ZoomInfo returns a verified email with high confidence, there is no need to query three more providers and incur additional cost. I build conditional logic that short-circuits the waterfall when high-confidence data is returned early in the sequence. This typically reduces enrichment costs by 30-40% without affecting coverage.
Provider Selection by Segment. Different providers excel for different segments. For example, Apollo tends to have better coverage for startups and early-stage companies, while ZoomInfo is stronger for mid-market and enterprise. I build segment-specific waterfalls that reorder providers based on the target company's characteristics. A prospect at a 50-person startup goes through Apollo first, then ZoomInfo. A prospect at a 5,000-person enterprise goes through ZoomInfo first, then Apollo. This optimization improves both coverage and cost efficiency.
Phone Number Waterfalls. The same waterfall logic applies to phone numbers, and it is equally impactful. Direct dials and mobile numbers are harder to find than emails, with most single providers covering only 40-60% of prospects. A waterfall through ZoomInfo, Apollo, Lusha, and Cognism can push phone coverage to 70-80%, which is critical for sequences that include phone touches.
Data Freshness Scoring. Data decays rapidly in B2B—roughly 30% of contact data becomes invalid within a year due to job changes, company changes, and email migrations. I add a data freshness score based on when each provider last verified the record. If ZoomInfo's email was last verified three months ago but Apollo's was verified last week, I may prefer Apollo's data even though ZoomInfo is my primary provider. This nuance improves deliverability and reduces bounce rates.
Cost Optimization Strategies
Waterfall enrichment can get expensive if you are not strategic about it. Here are the cost optimization strategies I use:
Credit-Based Planning. Most providers charge on a credit-per-lookup basis. I plan my enrichment runs to maximize the value of each credit by only enriching prospects that pass initial ICP filters. There is no reason to spend an enrichment credit on a company that does not match your firmographic criteria. I build a pre-enrichment filter in Clay that checks company size, industry, and geography before any paid lookups are executed.
Batch vs Real-Time. For static lists, I run enrichment in batches during off-peak hours when API performance is typically better. For intent-signal-triggered enrichment, I run in real-time because speed matters—but I only enrich companies that pass the ICP score threshold, not every company that triggers a signal.
Clay's Built-In Credits. Clay includes credits for several enrichment providers as part of its subscription, which can significantly reduce incremental costs. I structure my waterfall to use Clay-included credits first before falling through to providers that incur additional costs.
Annual vs Monthly Contracts. For ZoomInfo and Apollo, annual contracts typically save 20-30% compared to monthly pricing. If you are committed to GTM engineering as a function, locking in annual pricing makes economic sense.
Measuring Waterfall Performance
I track several key metrics to ensure the waterfall is performing optimally:
Email Find Rate. The percentage of target prospects for whom the waterfall returns a valid email. Target: 90%+. If you are below 88%, you need to add or reorder providers.
Email Accuracy Rate. The percentage of found emails that pass verification. Target: 95%+. If accuracy is below 93%, a specific provider may be returning stale data and should be deprioritized or replaced.
Bounce Rate. The actual bounce rate when emails are sent. Target: under 3%. If bounces exceed 3%, your verification step may not be catching all invalid addresses, or data is decaying between enrichment and send time.
Coverage by Provider. What percentage of final emails comes from each provider. This tells you which providers are pulling their weight and which are not justifying their cost. In my typical waterfall, ZoomInfo provides 60-65% of emails, Apollo provides 20-25%, and the remaining providers cover 10-15%.
Cost per Enriched Contact. The total enrichment cost divided by the number of contacts with valid, verified email addresses. Target: under $1.50 per enriched contact. If costs are higher, optimize your pre-enrichment filtering and conditional routing.
Common Mistakes That Kill Waterfall Performance
I see these mistakes repeatedly when companies try to build waterfalls without guidance:
1. Skipping Verification. The biggest mistake by far. Unverified emails from any provider will include invalid addresses that destroy your sender reputation. Always verify. No exceptions.
2. Enriching Before Filtering. Running every prospect through the full waterfall before checking ICP fit wastes credits on prospects you will never contact. Filter first, enrich second.
3. Static Provider Order. Using the same provider order for every segment ignores the fact that providers have different coverage strengths. Match provider priority to segment characteristics.
4. Ignoring Data Freshness. Treating a six-month-old email the same as a last-week-verified email leads to unnecessary bounces. Weight data freshness in your waterfall logic.
5. Not Monitoring Provider Performance. Provider data quality changes over time. A provider that was excellent six months ago may have degraded coverage or accuracy. Review coverage and accuracy metrics monthly and adjust your waterfall accordingly.
Waterfall enrichment is foundational to everything else in GTM engineering. Without high-quality, high-coverage contact data, your messaging, timing, and targeting are irrelevant because your outreach never reaches the prospect. Build the waterfall first, build it right, and everything downstream performs better. If you want help building or optimizing your enrichment infrastructure, let us set up a call. I can typically diagnose enrichment issues within the first thirty minutes and have a pipeline system producing results within weeks. To see how enrichment fits into the broader toolset, explore the 2026 GTM engineering stack. For using enrichment data to trigger outbound, read about signal-based selling for GTM engineers.