Duplicate company names are a nightmare. They clutter your CRM, mess up reports, and make it hard to track customer interactions.
But fixing them manually? That’s even worse. The good news? There’s an easier way.
In this guide, we’ll show you how to quickly find and merge duplicate company names in CSV/Excel files or CRM systems. You’ll learn how to:
Let's clean up your data!
Step 1: Import Your Company Name List
Start by importing your company name list into Datablist.
Once imported, review your data to make sure the company names and other fields are loaded correctly.
Step 2: Identify Duplicates Using Algorithms
Use Datablist’s duplicate finder to detect matching company names.
Select the column containing company names as the target field.
Now, choose the best deduplication algorithm:
- Smart Algorithm: Best for standard duplicates after normalization.
- Distance Algorithm: Finds similar names with slight variations or typos. Set a similarity threshold (default 80, minimum 50).
Run the duplicates check to detect matching records.
Understanding the Company Name Processor
The Company Name Processor is a special tool that normalizes company names before detecting duplicates. This is crucial because companies often have different variations of their names in your data.
What It Does:
- Removes Legal Suffixes: LLC, Inc., Ltd., etc.
- Ignores Geographic Terms: Europe, USA, UK, etc.
- Eliminates Business Keywords: Partners, Group, Technologies, etc.
Example:
| Original Name | Normalized Name |
|---|---|
| Apple Inc. | Apple |
| Apple USA LLC | Apple |
| Apple Technologies | Apple |
| Microsoft Corporation | Microsoft |
| Microsoft Ltd. UK | Microsoft |
This ensures that “Apple Inc.” and “Apple Technologies” are correctly detected as duplicates.
Step 3: Merge Duplicate Company Records
Duplicate records are grouped together.
Note: If you just need the list of duplicates, just export the matching results. For merging duplicates, proceed with the following steps.
Resolving Conflicts
A conflicting property indicates differing values for the same field across the identified duplicate records.
If duplicate records have different values in fields, you need to decide how to merge them.
Datablist offer two merging options:
- Combine Values: Merges multiple values from different duplicate records into a single field in the master record (e.g., multiple phone numbers).
- Keep One Value and Delete the Others: Selects a value from one of the duplicate records to retain, discarding the values from the other duplicates for that specific field (e.g., retaining the most complete address).
A Shortcuts Link is available to apply the same merging rule to all conflicting properties.
Choosing a Master Record
One record will be the "master", and the others will merge into it.
You can select based on:
- Most Complete: The record with the highest number of populated fields is selected.
- Last Updated: The most recently modified record is selected.
- First Created: The oldest record based on creation date is selected.
- Highest Value: The record with the highest value in a specified property is selected (ties are broken by the most recent record).
- Lowest Value: The record with the lowest value in a specified property is selected (ties are broken by the most recent record).
- Matching Value: The record containing a specific value in a selected property is chosen. Records without this value will not be merged.
After configuring merging rules and master record selection, refresh the preview to visualize the outcome of the merge operation before committing changes.
The preview displays the values that will be deleted, combined, and the designated master record for each duplicate group.
Manual merging of specific records may also be possible if required.
Initiate the automatic merging process where feasible by selecting an appropriate option (e.g., "Auto-merge duplicates when possible").
After merging, your dataset will be clean and ready to export. To export the deduped data, select the "Export" button in the header. You can export in CSV or Excel format.
Conclusion
Cleaning up duplicate company names doesn’t have to be overwhelming.
By using the right tools and strategies:
- You prevent data inconsistencies
- Improve CRM efficiency
- Save time on manual reviews
Start today and keep your company data clean and organized!