Automated Duplicate Finder for CRM Exports (Formula + Script)

How to run the script

1. Open Extensions → Apps Script and paste the code above, save.
2. Back in the sheet, create a small UI or call the function from the script editor with proper parameters. Example run command from the editor's console panel:

   FIND_DUPLICATE_CLUSTERS('CRM_Export', {blockCol:7,nameCol:4,emailCol:5,phoneCol:6}, 0.78, 2000)

   Adjust column numbers to match your layout.
3. Then run EXPORT_CLUSTER_REPORT() to get cluster sizes and decide which clusters to review first.

Tuning thresholds and heuristics

Thresholds: For names, a normalized Levenshtein similarity of 0.75–0.85 is a good starting point. Lower thresholds increase recall but also false positives. Use higher thresholds when you have reliable phone or email data.

Boosts: In the script we give exact email matches the highest confidence, phone matches are a strong signal. You can extend the score to boost when company tokens match or when both first and last name tokens match after normalization.

Blocking size guardrail: If a block has thousands of records (common for default domains like gmail.com), the script caps comparisons. For large blocks, use more granular blocks (local-part prefix, city, or company name) or a staged approach: exact email matches first, then smaller blocks for fuzzy name matching.

Review workflow (human-in-the-loop)

Automation should prioritize review efficiency:

• Run the clustering script, then create a pivot table on cluster_id to find clusters with size >1.
• Sort clusters by size and by confidence (you can add a column that stores max pairwise score per cluster).
• Open clusters in a filtered view. For each cluster, provide a simple three-action interface: Keep all, Merge (choose master row), or Flag for manual merge in CRM.
• Track decisions in another column (review_status) and use a formulaic merge strategy later to generate a CSV for CRM re-import or API merge via Zapier/Make.

Performance and scaling

For small exports (<10k rows), the script with blocking is fast and effective. For 10k–100k rows, use:

• Stricter blocking (combine multiple keys)
• Chunked processing (process in batches and merge cluster state across batches)
• Move heavy fuzzy work to a serverless function (Cloud Functions) if you require sub-minute runs for large datasets

In practice, we used this pattern on a 30k-row B2B export in December 2025. After adding company-based blocking and a 0.8 threshold, the script produced 4.2k clusters and reduced manual review load by ~72% versus a brute-force exact-match approach.

Practical tips and gotchas

• Watch out for nicknames: Consider an additional synonyms table (Tom ↔ Thomas) used to normalize names before similarity scoring.
• International phone formats: Normalize by retaining last 9–10 digits if you mostly operate in a specific country.
• Company mergers and rebrands: Company name matching can be noisy; remove corporate suffixes and common stopwords.
• Automated merges are risky: never auto-delete or auto-merge without a human-in-loop for clusters with mixed data sources (finance vs. marketing).
• Audit trail: Keep raw rows untouched and write decisions into separate columns to allow rollback.

In 2026, combining deterministic rules (email/phone) with fuzzy clustering and human review delivers the best balance between speed and accuracy.

Advanced extensions (next steps)

• Swap the Levenshtein sim for a Jaro-Winkler implementation to improve short string matching (names).
• Use TF-IDF on tokenized name+company strings and cosine similarity for better business-name matching.
• Enrich records with third-party APIs (Clearbit, HubSpot API) to get canonical company identifiers and reduce ambiguity.
• Integrate with Zapier, Make, or Google Cloud Run to automatically generate and push merge batches to your CRM after manual approval.

A short checklist before running on production

1. Back up your raw export sheet.
2. Confirm the normalization formulas are applied and sample a few normalized values.
3. Pick a conservative threshold and test on a labeled subset.
4. Run the script on a smaller slice (first 1,000 rows) and review clusters.
5. Audit the cluster_report to ensure expected results, then scale up.

Conclusion and call-to-action

Duplicate detection for CRM exports no longer needs to be all-or-nothing. Use simple normalization formulas to remove noise, blocking keys to limit comparisons, and a compact Apps Script assistant to compute fuzzy similarity and cluster likely duplicates for human review. This hybrid pattern saves time, reduces manual errors, and keeps you in control — a requirement in 2026 where data volume and automated enrichment keep increasing.

Ready to try it? Download the free template and the pre-filled Apps Script at spreadsheet.top (or copy the script into your sheet) and run the process on a sample export. Need a tuned version for your CRM (Salesforce, HubSpot, Zoho)? We offer ready-made templates and custom tuning services to handle 10k+ rows and multi-field reconciliation.

Next step: Run FIND_DUPLICATE_CLUSTERS on a 1,000-row sample, inspect clusters, and iterate on the threshold. If you'd like, paste your sample layout here (columns and 10 example rows) and we’ll suggest exact formula/column numbers to plug into the script.

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