Real stories from businesses like yours
How a NYC Cleaning Company Turned Messy Records Into a CRM Its Team Uses
A three-month, on-site move from Salesforce to Jobber. Thousands of duplicate and incomplete records became a clean pipeline, with website forms, calls and texts going straight into the CRM and every new lead getting a fast, automatic first response.
Project snapshot
Led by: Paula Baez
Client: Residential and commercial cleaning company, New York
Engagement: 3-month on-site contract
Systems: Salesforce → Jobber
Focus: CRM migration, lead capture and sales workflow
The starting point
Years of customer data nobody could use
After a few years on Salesforce, the team still hadn’t been able to adopt or manage it consistently, so the company chose Jobber as a more practical platform for daily operations. But Jobber’s initial setup was generic, and the Salesforce export was a problem of its own.
- Thousands of unstructured records the team didn’t know how to sort, clean or import
- Duplicate leads and incomplete customer records
- Inconsistent tags and information stored in the wrong fields
- Paper notes and no shared data-entry rules
- A real risk of missed follow-ups and unreliable customer records
What was implemented
A CRM built around how the team really works
Clean data first, then a pipeline designed from the team’s actual sales and service process, not the software’s defaults.
Data audit
More than two weeks understanding, cleaning, testing, standardizing and validating the Salesforce records.
Data standardization
Removed duplicates, isolated incomplete contacts, and created consistent fields, tags and classification rules.
Lead segmentation
Separated recent leads for active follow-up from older records for re-engagement campaigns.
Process discovery
Reviewed digital touchpoints and interviewed sales staff to map the lead and customer journey.
Pipeline architecture
Designed Jobber stages, fields, tags, handoffs and follow-up actions around the real sales process.
Lead capture
Connected intake forms from four company websites directly to the CRM.
Speed-to-lead automation
Configured AI reception for phone and SMS, email and SMS workflows, and team notifications.
Marketing activation
Connected relevant tools and prepared segmented campaigns with audience-specific triggers.
Data governance
Documented the CRM structure, automation logic, operating guidelines and data-entry rules.
The workflow
From a messy export to a working pipeline
How the data was cleaned, how new inquiries now flow into Jobber, and what keeps the system working. Client details removed.
Results
What changed for the team
- A cleaner customer database the team can trust
- A Jobber setup aligned with the real sales and service workflow
- Clearer communication between leads, customers, sales and service staff
- More consistent follow-up, with lead-response automations in place
- Campaign-ready data, with segmented promotions prepared
- Governance rules that help keep data quality high over time
No formal performance measurements were available, so this story makes no numerical claims.
Why clean data matters
Follow-up, campaigns and reporting all depend on data you can trust. Without it, even a good CRM becomes one more place where leads get lost.
Does your CRM feel like more work, not less?
If notes still live on paper, leads are spread across several places and nobody trusts the customer list, the problem usually isn’t your people. It’s the system. That’s what our CRM & follow-up work fixes.
Another real story: how a 30-year NYC event venue stopped losing inquiries
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