Customer story
How a Wellness App Stopped Sales Reps from Calling the Same Prospect by Merging 3,000+ Duplicate Pipedrive Records with Dedupely
- 3,000+ duplicate records merged
- 1,500+ matches merged
- 52,929 records synced to Dedupely
A match is one set of duplicate records merged into a single record. Every match holds at least two records, so the number of records merged is at least double the number of matches.
Where duplicates came from
The same prospects in every new list
The company sells an employee vitality app to employers, and its sales team cold-calls HR teams from Pipedrive. Prospects come from lead lists bought from data providers, imported in batches by industry, with each batch getting its own label. Each rep works a different list.
That setup kept creating duplicates:
- Overlapping lists. Companies in the same industry came back in later lists, so the same company and the same person arrived as new Leads again.
- Two providers, two versions of a person. The company switched data providers, and each one supplied different details for the same prospect.
- Small differences. A missing letter in a name, a company with and without a dot or a country suffix, or a person saved once with a middle name and once without.
Pipedrive held about 7,500 Leads, and the team estimated that about a third were duplicates.
What duplicates broke
Several reps calling the same person
Because each rep worked a different list, one prospect sitting in two lists got called twice, sometimes three times. A prospect could ask one rep to call back later, and a colleague working another record, who never saw the note, would call again. The team worried that calling someone who was already in talks looked unprofessional and hurt trust with prospects.
No single view of a company
The team wanted one organization per company, with all its HR People and Leads underneath. Instead, the same company sat in Pipedrive as several organizations and several leads. Pipedrive's own duplicate check showed only 80 and found pairs only, while the team needed to find the hidden ones, and merging one pair at a time would have taken far too long.
How duplicates were solved
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1. Merge Organizations first
With Dedupely's customer success team, the team changed the order of the cleanup: Organizations first, then Leads, then People. Merging duplicate organizations brings the leads that belong to the same company under one organization, which shows the duplicate leads hiding underneath. The first organization search matched on a similar name and the same owner, and those matches were merged in bulk.
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2. Match Leads on title, person and label
Lead searches matched on the lead title, the person and the label, with the organization name set to Similar. A filter held back leads labeled Do Not Contact, and the team checked each page before merging. After a single test merge, 673 lead matches were merged in bulk.
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3. Handle conflicting labels by hand
In Pipedrive, a merged lead keeps every label from every record, so a lead labeled for two different lists could still show up in two reps' lists. For leads with conflicting labels, the team opened each match in View Match Details and picked the label that should stay, such as Do Not Contact, before merging.
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4. Catch the people the obvious searches missed
For People, a search on first name, last name and email found the clear duplicates. A second search, on a similar email and a similar organization, found a person saved three times under different versions of their name.
What deduplication changed
3,000+ duplicate records merged
The company has merged more than 1,500 matches, combining at least 3,000 duplicate records. At its latest count, the account held 52,929 records.
One company, one view
After the first big round, one company that had been split across several records showed up as one organization, with both of its HR People and a single label, which the sales lead running the cleanup said was exactly what they wanted. He said they could see Dedupely helping them.






