Import customer list without creating duplicates

I am preparing to import approximately 12,000 customer records from our legacy CRM into FieldPulse. I have reviewed the bulk import documentation and have a concern regarding duplicate prevention.

Our legacy system contains multiple entries for the same customer due to historical data quality issues. I have confirmed that:

  1. Approximately 2,300 records share phone numbers with at least one other record
  2. Approximately 1,800 records share email addresses with at least one other record
  3. Approximately 890 records have matching first name, last name, and street address but different contact methods

My questions are as follows:

  1. What match key(s) does the FieldPulse import process use to identify potential duplicates?
  2. Is there a configurable merge rule set, or is deduplication handled automatically according to a fixed hierarchy?
  3. Can I specify which record should be considered "master" when duplicates are detected (e.g., most recent updated date, most complete data)?
  4. Does the import provide a preview or dry-run option showing which records would be merged versus created as new?
  5. Are there any constraints on which fields can be used as custom match keys?

I have noted that the import template includes columns for "Customer ID (External)" — it is not clear whether this field participates in deduplication logic or is strictly for reference.

Prior to executing this import, I require a reproducible process that does not create duplicate customer records, as remediation would be operationally costly.

Environment details: I will be using the web interface bulk import function, not the API, for this operation. Our account is on the Enterprise tier.

Parents
  • I have completed the import using the following process:

    1. Pre-processed CSV to normalize phone numbers (removed extensions, stored separately)
    2. Sorted records by "Last Modified Date" descending within each logical group
    3. Executed import in four batches of approximately 3,000 records
    4. Captured pre-import customer export for comparison

    Result: 11,847 customer records created, 153 merges identified via timestamp comparison. This aligns with my deduplication estimate.

    One anomaly observed: three records with identical email addresses were not merged despite appearing in the same batch. I have confirmed the email addresses match character-for-character. Ticket #FP-28473 opened with support.

    Process documented for future imports. Thank you for the guidance.

Reply
  • I have completed the import using the following process:

    1. Pre-processed CSV to normalize phone numbers (removed extensions, stored separately)
    2. Sorted records by "Last Modified Date" descending within each logical group
    3. Executed import in four batches of approximately 3,000 records
    4. Captured pre-import customer export for comparison

    Result: 11,847 customer records created, 153 merges identified via timestamp comparison. This aligns with my deduplication estimate.

    One anomaly observed: three records with identical email addresses were not merged despite appearing in the same batch. I have confirmed the email addresses match character-for-character. Ticket #FP-28473 opened with support.

    Process documented for future imports. Thank you for the guidance.

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