Answers.Body: <p>I would suggest you start by reading the section in the User Guide on <a href="https://docs.civicrm.org/user/en/latest/common-workflows/deduping-and-merging/" rel="nofollow noreferrer">Deduping and merging contacts</a> if you haven't done so already.</p>
  <p>As to identifying the contacts that need to be merged, this really depends on what information you have on record. Finding contacts with duplicate email addresses is often a good place to start. Or look for duplicate postal addresses if you have that. If you only have names it will be a bit more tricky. You can use the 'length' in the dedupe rule to specify how many letters need to match. This may require trial and error to get this right for your data. Setting the 'length' too high may miss duplicates but setting it too low may generate too many duplicates.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-01-16T17:56:44.367Z
Answers.LastActivityDate: 2021-01-16T17:56:44.367Z
Answers.UserDisplayName: William Mortada
Body: <p>We have some members using similar names to register a membership at different years.  What is the best way to merge their membership? Thank you.</p>
  
Tags: <civimember><duplicate-contacts>
Title: What is the best option to merge duplicated membership?
ThreadText: # What is the best option to merge duplicated membership?
  
  (Asked by: username_0 on 2021-01-16)
  
  We have some members using similar names to register a membership at different years. What is the best way to merge their membership? Thank you.
  
  **Tags:** civimember, duplicate-contacts
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2021-01-16. Score: 2
  
  I would suggest you start by reading the section in the User Guide on [Deduping and merging contacts](https://docs.civicrm.org/user/en/latest/common-workflows/deduping-and-merging/) if you haven't done so already.
  
  As to identifying the contacts that need to be merged, this really depends on what information you have on record. Finding contacts with duplicate email addresses is often a good place to start. Or look for duplicate postal addresses if you have that. If you only have names it will be a bit more tricky. You can use the 'length' in the dedupe rule to specify how many letters need to match. This may require trial and error to get this right for your data. Setting the 'length' too high may miss duplicates but setting it too low may generate too many duplicates.
  
  
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