Why LEAP Software Duplicated Contacts Across Matters and the Deduplication Script That Cleaned My CRM

If you’ve ever managed a Client Relationship Management system (CRM) that integrates with LEAP legal software, you’ve likely encountered the unexpected frustration of duplicated contacts. What started as an efficient and organized system for managing client relationships can quickly descend into a web of redundant entries, bloated records, and compromised data integrity. So why does LEAP duplicate contacts across matters, and how can a well-structured deduplication script solve the issue? In this article, I’ll share my experience uncovering this problem, its root cause, and how a custom script cleaned up our CRM for good.

TLDR

LEAP Software duplicates contacts across matters due to its matter-centric data model, where contacts are often treated as unique records per matter instead of globally linked entities. This creates inflated CRM records and leads to inconsistent client data. A custom deduplication script helped us identify, merge, and standardize contact information. The result? A clean, reliable CRM and smoother workflows across departments.

Understanding LEAP’s Contact Structure

LEAP is purpose-built for legal practices, offering a robust suite of features including automated document creation, time-tracking, and matter management. However, its contact management architecture can often lead to confusion. Unlike other CRMs or Practice Management Systems that centralize contact information across all interactions, LEAP adopts a matter-centric approach.

In LEAP, a “matter” represents a distinct case or legal task. Contacts such as clients, opposing counsel, and witnesses are added to matters as needed. Unfortunately, that means:

• If the same client is involved in more than one matter, LEAP may treat them as separate entities.
• Contact information may be copied—not referenced—across different matters.
• Any edits to a contact in one matter are not automatically reflected in their entries under other matters.

This model may be useful when details for an individual genuinely differ between matters. However, in most firms, a client’s phone number and address don’t change from case to case. That’s where the duplication begins—and left unchecked, it snowballs.

Real-World Impact of Duplicated Contacts

Initially, the consequences of LEAP’s duplication behavior seemed minor. But over time, we began noticing several critical problems:

1. Inconsistent Communication: Some team members were calling outdated phone numbers or sending emails to old addresses.
2. Reporting Errors: CRM exports inflated contact counts and skewed analytics due to duplicates.
3. Loss of Client Trust: Our clients noticed when they received duplicate emails or misaddressed documents.
4. Confused Staff: Employees didn’t know which contact record to trust or use, often leading to re-entering contact information manually.

As our firm grew and the number of matters increased, so too did the duplicate contacts. Cleaning them manually wasn’t sustainable. A more reliable, automated solution was necessary.

Challenges in Deduplication

Before creating the script, we had to understand the data structure thoroughly. LEAP doesn’t provide an out-of-the-box way to deduplicate contacts across matters. This presented a few challenges:

• Ambiguity of “Same” Contacts: Two contacts may be visually identical but have minor data differences (e.g., “Street” vs. “St.”).
• Relational Integrity: Contacts are tied to specific matters, so merging them improperly could break connections and history logs.
• API Limitations: LEAP’s public API doesn’t expose certain internal identifiers needed for merging or deleting contacts.

We realized that a clean merge process needed multiple steps, with strong safeguards and rollback options. The deduplication script had to be both cautious and intelligent.

The Deduplication Script: Strategy and Execution

We developed a Python-based deduplication tool using CRM exports and custom identifiers. Here’s how it worked:

1. Export Contacts: We pulled all contact records into CSV format, including name, email, phone, and matter association.
2. Normalize Data: The script cleaned common formatting discrepancies (e.g., trimming whitespace, unifying case).
3. Generate Match Keys: Using key fields like email + last name, the script grouped contacts likely to be duplicates.
4. Name Similarity Score: Using fuzzy matching algorithms (Levenshtein distance), the script compared name closeness.
5. User Verification: Potential merges were flagged for manual review before executing automated updates to ensure safety.

The final step involved using CRM APIs (where available) to update master records and link previously separate matter entries to a unified contact profile. The rest were either archived or flagged for historical purposes.

Precautions and Safeguards

If you are planning to run a similar deduplication process, make sure you prepare adequately. Some key precautions we took include:

• Full Backups: We backed up the CRM database before any operations started.
• Logging: Every action performed by the script was logged with timestamps, reasoning, and affected records.
• Rollback Plan: We had a version-controlled file of original data that allowed us to revert if necessary.
• Dry Run Mode: A test run of the script was executed first to show what changes it “would” make, without making any actual edits.

Thanks to these safeguards, when we finally ran the script in production mode, there were no unintended changes and no data was lost.

The Results

After implementing the deduplication process, the impact was immediate and measurable:

• We reduced total contact records by 30%, from over 12,000 to 8,500 unique, accurate contacts.
• Internal team productivity improved because employees could find and trust the contact data they needed.
• Email campaign bounce rates dropped by 15% due to the elimination of outdated or malformed email addresses.
• Client satisfaction improved—no more miscommunications or duplicated documents.

Moreover, by aligning our CRM more closely with best practices and introducing periodic data hygiene audits, we’ve kept our system consistently reliable ever since.

Lessons Learned and Best Practices

This experience taught us several important lessons about managing contact data, especially when using platforms like LEAP alongside more traditional CRMs:

• Understand Your Software’s Philosophy: LEAP is engineered for legal professionals, not CRM perfection. Know its strengths and its limitations.
• Never Assume Centralization: Just because a contact exists in your system doesn’t mean it exists consistently—or accurately—everywhere.
• Invest in Long-Term Solutions: Write scripts, set workflows, and create periodic checkups rather than relying on one-time fixes.

Conclusion

LEAP’s matter-based architecture may be ideal for document and case management, but it introduces redundancy when used in coordination with centralized CRMs or shared contact systems. These duplications can severely impact communication, reporting, and client relationships. However, with a custom-built deduplication script and a disciplined data hygiene approach, it’s possible to reclaim order and accuracy.

If you’re managing a growing law firm or an IT team supporting legal clients, don’t wait for duplicates to become a problem. Get proactive. Audit, clean, and maintain your CRM—because trust, both internally and with clients, begins with clean data.