Fix that before you scale outbound
To increase your outbound reps productivity
Welcome to a new 🔒 subscriber-only edition 🔒 of my weekly newsletter of Outbound Kitchen. Each week, I dive into reader questions about scaling outbound and making it your #1 growth engine. For more: Launching Outbound, and Scaling Outbound
A few days ago, I saw this question on LinkedIn:
SDR leader:
"I want to leave our only B2B contact data provider. What’s the best tool?"
Me:
"Unfortunately, there's no one magic tool that does everything. I've been doing outbound since 2018, starting with Hunter.io, and have used ZoomInfo, Cognism, Lusha, LeadIQ, Apollo, FullEnrich, etc."
Most outbound leaders make these expensive mistakes with their data stack:
Rely on one provider, creating massive coverage gaps in target accounts
Sign 12-month contracts before testing their data - then discover 40% of numbers don't work
Use the same stack that everyone uses on LinkedIn
Forcing reps to “source their own data” across multiple tools, turning salespeople into research assistants
Track only email bounces, ignoring the bigger problem of wrong phone numbers
Buy AI tools before fixing data quality - garbage in, GPT-garbage out
Never validate and score if these numbers are even accurate.
Invest in a strong stack for “rep productivity, "but invest a few in data
Score their accounts with unvalidated data
The result?
Your outbound team wastes 20–50% of their time on: Calling disconnected numbers, chasing the wrong accounts, and doing manual research that should’ve been automated.
That’s not okay.
That’s like saying: “It’s fine if my reps only work 3 days a week.”
It stops here.
This guide walks you through how to build an outbound data infrastructure that: Eliminates manual data hunting, reduces waste, and makes your reps 3× more productive
Why "Best Tool" is the Wrong Question
Outbound leaders waste months testing random tools, hoping to find magic.
Each tool switch costs weeks of onboarding and lost momentum.
Stop tool-hopping: focus on coverage for YOUR specific context
Test multiple providers simultaneously: different tools excel in different regions. On one podcast that I recorded recently. An ex-ops-leader at Doordash told me they had different data providers for each country, what works in the USD doesn't work in Japan.
Score on quality AND coverage: not just features and pricing
The right question becomes "what works for my context (ICP/persona)?"
ZoomInfo dominates US mid-market but fails in EMEA. Cognism crushes European data. Kaspr owns France-specific contacts. No single tool covers everything.
This is why data infrastructure beats your best tool selection approach.
You need a system that combines the best of each provider. Let's build that system next.
Build Your Outbound Data Infrastructure
One of the biggest lessons I’ve learned in the past 2 years:
If you want a productive outbound team, you need to build a strong data infrastructure.
Back then, it required:
Freelancers for data entry
Technical knowledge to stitch tools together
Hours of manual setup
But in 2025, you’ve got no excuse.
With the right tools (and now AI), you can build a lean, powerful data engine without needing to code or hire engineers.
And if you’re leading outbound, this is your responsibility.
It’s one of the biggest pillars of outbound success.
Before we start…
If you want to build this right for your team, ask yourself one question:
Where does your contact and account data actually live?
LinkedIn?
Public databases?
Government websites?
Some niche third-party source?
Because here’s the thing:
I recently spoke with a company targeting an industry where most businesses aren’t even on LinkedIn.
No websites either.
If they only relied on LinkedIn, they’d miss 80% of their market.
You can’t rely on a single data source anymore.
Your TAM isn’t hiding in one tool.
You need to stitch it together.
Mindset Shift: From “Best Tool” to “Best Data Source for My Context”
Multi-source philosophy: treat data like ingredients, not a finished meal.
Don't rely on just one source for your data. Get at least two or three best providers for your context and test them against each other so you get better coverage and fewer overlaps.
Use different data sources for:
Account data (technographics, demographics, pain points, buying signals)
Contact info (email, phone numbers)
Here are a few examples for 3 industries:
Tech/SaaS Companies
Account & contact data sources: LinkedIn Sales Nav, ZoomInfo, Cognism
Key signals: Funding rounds, tech stack changes, hiring patterns
Manufacturing/Industrial
Account data sources: Government databases, trade associations, Dun & Bradstreet
Key signals: Permits, expansions, regulatory filings
Healthcare/Life Sciences
Account data sources: Medical directories, licensing boards, conference attendees
Compliance considerations (HIPAA)
Personally, I use Clay to centralize all my account and contact data.
I’m tool-agnostic, but Clay is the only no-code tool I’ve found that truly helps me do this at scale. It lets me build a system that works without needing a data engineer.
Here’s a quick (non-exhaustive) list of what I plug into it:
Contact data
Public databases (state, government, etc)
Paid databases (Cognism/ZoomInfo/Apollo)
Tools that enrich based on LinkedIn profiles, and/or just full name + domain: FullEnrich (waterfall enrichment), LeadMagic, Wiza, People Data Labs, Findyemail, Prospeo, etc.
You might need a mix of 3 tools like Cognism + FullEnrich or ZoomInfo.
Every provider is different when it comes to data quality and coverage.
ZoomInfo is great in the US, mid-market and enterprise accounts.
Cognism is great in EMEA.
Kaspr or Cleon1 are great in France.
Phone accuracy: TitanX, Numverify, Twilio Lookup, Dexatel
Email deliverability: NeverBounce, Emailable, Bouncer, Mailgun Validate
For phone number accuracy, one way to measure it is with your call dispositions with your dialer
Account data
Public databases (states, government, etc)
Paid tools:
Account firmographics & technographics: BuiltWith, Stackshare, Slintel, HG Insights, 6sense
Team size from SalesNav via Clay
Tech stack from Builtwith (if available on their website) or job descriptions with Sumble or TheirStack
Crunchbase for financial data
Signal Layer
Intent data: G2, Bombora, 6sense, Cognism
Job changes/new hires in your accounts: Clay, Common Room, Champify, Usergems
Tech installs: BuiltWith change feed.
Funding/events: Crunchbase, Dealroom, PitchBook.
Product usage (if PLG): Pocus, Common Room,
Website visitors:
Person level: Common Room, Warmly, Unify
Account-level: Common Room, 6sense, Demandbase, Koala, Pocus, Clearbit, Apollo, ZoomInfo, Usergems
Test everything before you buy
Now that you have made your selection.
It's time to test the data.
Here’s a quick example with phone numbers (but you can apply the same approach to emails, account data, or signals too):
Let’s say you’re evaluating tools like Cognism or ZoomInfo, don’t lock into a 1-year contract before validating two things:
1. Coverage
Pull a sample list of 500 prospects (e.g. CFOs). Include:
Full name
Company domain
LinkedIn URL (if possible)
Run this list through each provider.
Now check:
How many phone numbers did each one return?
How much overlap is there?
Are some tools giving net-new numbers the others don’t?
What type of phone numbers do you get? (mobile vs landline)
2. Accuracy
Start simple: Use a spreadsheet or your CRM.
Log call outcomes by tagging which provider the number came from.
Example logging:
Call fails → “Incorrect number / Not in service”
Wrong person picks up → “Wrong contact”
Voicemail confirms name → “Accurate”
Right person picks up → “Accurate”
Over time, this gives you actual data on:
% of valid numbers
% of wrong/dud leads
Which provider is most reliable for your segment
What I’ve seen:
ZoomInfo → ~75-99% accuracy depending on the segment
Cognism → ~80% in many EU/UK regions
Only after this should you compare pricing.
Continuous validation loop
But testing doesn’t stop once you’ve picked a tool.
You need to keep validating your data, all the time.
Track the source of each phone number, email, or company record.
Log call outcomes (dispositions) to know what’s working:
Wrong number?
Disconnected line?
No answer?
Right person picked up?
Do the same with email and account data:
Is the email bouncing?
Is the company info correct?
This ongoing loop helps you flag issues early, and avoid scaling broken data.
Clearly assign data management tasks
You (as a leader) or your sales ops team should handle this, not your reps. If you don't have an ops person yet, hire a freelancer (call them a GTM engineer, sales ops specialist, whatever you like). Your reps should only do ONE manual thing here, marking the call outcome after each call to measure your data quality.
Your Outbound Data Infrastructure = Your Competitive Advantage
Building your outbound data infrastructure:
It’s how you scale efficiently.
It’s how you build a predictable pipeline.
It’s how you win.
While competitors waste time with single-source providers, you'll have:
Accurate scoring → so GTM focuses on the right accounts at the right time
No more manual data hunting → reps focus on prospecting, not research
Validated phone numbers → no more dead lines or front desks
Verified emails → no more bounces, no more wasted sends
Remember: Data first, AI second. Garbage in, GPT-garbage out.
Build this infrastructure once. Scale it forever.
I’m working with a customer right now on this, and we both agree:
This is a real competitive edge.
Their competitors only use 1 legacy provider.
We enrich 5+ sources.
We clean. We verify. We route.
Data quality powers everything downstream.
Outbound productivity, efficiency, and results all start with your infrastructure.
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