A builder created a workflow that enriches, validates, and scores every inbound lead before it enters the CRM. The result: cleaner routing, fewer duplicates, and a CRM teams can trust.
ad data isn’t loud. It rarely announces itself.
Most teams only notice it after routing slows down, scoring stops making sense, or a duplicate record sends two reps to the same account.
Tosin, a builder who consistently shares clear, no-nonsense GTM workflows on LinkedIn, approached the problem from a different direction. Instead of cleaning data after it reaches the CRM, he built a workflow that cleans, enriches, and validates every inbound lead before it ever touches HubSpot or Salesforce.
The result is simple: cleaner inputs, fewer surprises, and a CRM teams can actually trust.
Here’s how the builder set it up.
TL;DR
- Inbound leads first land in Google Sheets, not the CRM.
- Lusha enriches every new record with verified emails, job titles, and company data.
- A fallback check runs if enrichment is incomplete.
- Airtable scores each lead before sync.
- Only clean, verified, complete records enter HubSpot or Salesforce.
- A weekly audit keeps CRM data fresh by catching job and company changes.
1. Capture: create a safe staging layer in Google Sheets
Instead of sending website forms, chatbot submissions, or paid traffic straight into the CRM, the builder routes everything into a Google Sheet using Zapier.
This staging layer separates raw inputs from trusted data, ensuring nothing touches HubSpot or Salesforce until it has been enriched, verified, and scored.
It also gives the team clear visibility into what’s coming in and how complete those records are before they move forward.
2. Enrich: verified fields from Lusha’s API
Every new row triggers an enrichment call to Lusha’s API.
The workflow fills in the details teams rely on:
- Verified business email
- Direct dial
- Job title and seniority
- Company name, size, industry, and location
- LinkedIn URL
- Tech stack
- Hiring activity and job-change signals
Because the enrichment is verified at the source, the CRM doesn’t get flooded with personal inboxes, outdated titles, or guessed company information.
It becomes a reliable foundation for everything downstream.
3. Validate: run a fallback check when needed
If Lusha can’t return verified data, which is extremely rare, the workflow triggers a Make scenario that performs a fallback step: an additional enrichment call, a cross-check, or a flag for review.
This keeps the process consistent.
Every record follows the same path, and no field is left incomplete by accident.
4. Score: a simple, transparent qualification layer
Next, the record moves into Airtable for scoring.
The builder uses a straightforward model:
- ICP match → +1
- Company size → +1
- Verified email → +2
- Intent signal match → +3
The goal isn’t to build a full scoring system here—just to make sure only relevant, complete, and verified records progress to the CRM.
This small step keeps routing cleaner and prevents reps from reaching out to the wrong accounts.
5. Sync: only trusted data enters HubSpot or Salesforce
Once enriched, validated, and scored, the record syncs automatically into the CRM.
What doesn’t get through?
- Duplicates
- Personal inboxes
- Leads with missing verified fields
- Incomplete records
- Anything below scoring thresholds
The CRM becomes a home for trusted data, not a mix of clean and dirty records.
This strengthens every workflow that depends on it—routing, scoring, attribution, and forecasting.
6. Audit: weekly updates to keep CRM data fresh
Data decays quickly, so the builder added a weekly audit.
Every seven days, Make rechecks CRM records against Lusha and updates:
- Job changes
- New roles
- Company switches
- Updated company profiles
- Shifts in hiring or growth signals
This keeps the CRM aligned with what’s happening in the market—without the usual quarterly cleanup projects.
The impact: cleaner pipeline, faster routing, better decisions
After a few cycles, the builder saw clear improvements:
95%+ email accuracy
Reps spend less time chasing bounced inboxes.
Zero duplicates
The staging and scoring layers remove overlap before it reaches the CRM.
Better routing and follow-up
Verified inputs make it easier to connect with the right people.
More reliable scoring and forecasting
Consistent enrichment gives GTM teams a clearer picture of their pipeline.
No more reactive cleanup work
Because the workflow prevents the problems before they happen.
Why this works well with Lusha
This workflow leans on Lusha because it provides:
- Verified emails and direct dials
- One of the largest continuously refreshed datasets (280M+ contacts; 100M newly added)
- Real-time job changes and growth signals
- Strong accuracy in job titles and company matching
- A fast enrichment API that handles high inbound volume
This gives the workflow a single, consistent source of truth—and when the CRM starts with clean data, every GTM process becomes easier.
This workflow has been running quietly in Tosin’s stack, keeping his CRM clean without the usual maintenance overhead.
It’s the kind of practical, repeatable system he’s known for—quick to build, easy to adapt, and reliable for real GTM teams.
You can read his original breakdown on LinkedIn.
If you’re looking to build something similar, this workflow is a strong place to start.
Start free and see verified data in action >>
Related reading:
How to cut your lead response time from 20 to 2 minutes
FAQs
Yes. It’s used as a pre-CRM layer for both.
Yes. Lusha’s API, Zapier, and Make handle thousands of records reliably.
The fallback step handles it. You can choose to enrich from another source, flag the record, or hold it for review.
No. It keeps the inputs to scoring clean so your broader scoring model works better.
Yes. The weekly audit step is common in RevOps workflows and easy to build.
