Most CRMs aren’t missing leads, they’re missing visibility. Contacts arrive incomplete and stay that way unless someone fixes them manually. This post shows how a simple workflow with Lusha automatically enriches HubSpot contacts, keeps data clean, and gives teams the clarity they need to sell.
Most RevOps and sales teams think their CRM is missing leads.
In reality, the leads are there — the context isn’t.
Contacts without emails, missing phones, half-complete company profiles, broken formatting, empty fields. The CRM isn’t empty. It’s blind.
And adding more tools won’t fix that.
What fixes it is a workflow that keeps the data complete, clean, and consistent without anyone touching a spreadsheet.
That’s what creators like Can Timağur are doing with Lusha today.
The real problem isn’t “more leads.” It’s data you can’t trust.
Almost every team hits the same wall:
- contacts with missing emails
- contacts with outdated phones
- people who filled in forms with partial info
- company details that were never enriched
- inconsistent formatting that breaks routing
- empty owner fields that send leads nowhere
And the manual fixes take hours.
Teams add tool after tool trying to patch the gaps, but the CRM stays messy.
The problem isn’t effort.
It’s visibility.
What a clean enrichment workflow looks like
Can built a simple flow that does what every team wants but rarely has time to do:
Search HubSpot for contacts with missing data
Send the available fields to Lusha
Find verified emails, phones, and company info
Update the original HubSpot record automatically
Skip contacts that are already complete
Preserve existing data without overwriting anything
No CSVs.
No duplicates.
No “who owns this lead?” threads.
Just cleaner data every day.
Let the system handle the boring work so your team can do the work that pays.
Why teams overcomplicate this
Most companies try to fix enrichment by stacking tools:
- a separate email finder
- a separate phone finder
- a separate validation tool
- a spreadsheet cleaner
- a HubSpot plugin
- and a random enrichment hack someone built two years ago
That’s how CRMs end up with 5 sources and 0 accuracy.
Can’s point is simple: you don’t need five tools.
You need one source you trust — and a workflow that runs consistently.
Why Lusha works well in this flow
This is where Lusha fits the problem cleanly:
- verified emails and phones
- real signals behind the scenes
- fast API lookups
- clean formatting
- strong coverage for mobiles
- automatic company enrichment
- works on partial inputs (name + company, domain, email, etc.)
And because the workflow only processes contacts that actually need enrichment, the system stays efficient and predictable.
The CRM gets cleaner every day instead of messier.
What changes when the CRM can “see”
Once the blind spots are gone, teams stop chasing their own data.
You start seeing:
- better routing
- better scoring
- cleaner dashboards
- faster responses to inbound
- SDRs wasting less time repairing fields
- managers trusting what’s in the CRM
- automation that finally works because the data is consistent
It’s easier to sell when the system makes sense.
This isn’t “nice to have.” It’s foundational.
Outbound is faster when you know you’re reaching the right person.
Inbound works when the contact is complete.
Scoring works when the fields are clean.
Routing works when the CRM has context.
Automation works when the data is readable.
Everything in RevOps depends on clean inputs.
Can’s workflow solves the quiet problem most teams overlook.
The shift is simple
You don’t fix a blind CRM with more dashboards.
You fix it with a workflow that keeps your data alive.
One flow.
One source.
One update at a time.
And because it runs automatically, the team doesn’t feel it. They just feel the difference: less chaos, fewer guesses, more selling.
If the CRM feels chaotic, this kind of workflow fixes more than teams expect.
Keep reading:
How Lusha uses job-change signals to re-activate high-intent prospects
Lusha + monday CRM: keep every board enriched with verified data
Waterfall data enrichment: why streaming data beats static stacking
