How to Merge Multiple CSV Files Without Creating Duplicate Leads

Tools like merge-csv.com and CSV Combiner do exactly one job: stack rows from multiple files into one file. That's fine when the files don't overlap. It's a problem when they do, which is the normal case if you've ever scraped two adjacent neighborhoods, two overlapping radius searches, or re-run the same city search a month later. Merge those blindly and the same business shows up two or three times.

The Problem Generic CSV Mergers Don't Solve

A generic merge tool concatenates rows. It has no concept of "this row and that row describe the same business." That's a matching problem, not a merging problem, and it's the part that actually determines whether your final list is usable or full of duplicate outreach targets.

According to Validity's State of CRM Data Management 2025 report, 37% of CRM users reported losing revenue as a direct consequence of poor data quality. Duplicate records are one of the most common categories of that problem: a rep works the same account twice, a sequence emails the same contact from two different rows, or a report double-counts pipeline because one business exists in the CRM under two IDs.

Why Overlapping Maps Searches Create Duplicates

Google Maps search results are bounded by whatever area and radius you search. If you search "plumbers in Dallas" and then "plumbers in Dallas-Fort Worth" a week later to widen coverage, a meaningful share of the second export is businesses you already have from the first one, just re-scraped with a fresh timestamp. Same story if you split a metro into ZIP codes or neighborhoods to stay under a results cap: the boundaries usually overlap at least a little, and a business sitting near the edge of two search areas ends up in both exports.

None of that is a bug in the scraping. It's just what happens when you export from several overlapping searches and then need one clean list.

Manual Method: Excel / Google Sheets

For a small number of files, this works without any tooling:

1. Open each CSV as its own sheet, or use Excel's Data → Get & Transform → Combine Files feature, which Microsoft documents for pulling multiple CSVs from one folder into a single table.
2. Add a helper column that normalizes the business name (lowercase, trimmed, punctuation stripped) plus the phone number digits only. This is your practical match key when you don't have a place ID.
3. Sort by that helper column, then use conditional formatting to highlight duplicate values so you can see clusters before you delete anything.
4. Keep the most complete row from each duplicate cluster (the one with a phone number, website, and review count filled in) and discard the rest.

This is slow past a few thousand rows, but it's transparent: you can see exactly which rows got flagged and why before anything is deleted.

Script Method: Python + Pandas

For anything larger, a short pandas script does the same logic without the manual review step:

import pandas as pd
import glob

files = glob.glob("exports/*.csv")
df = pd.concat([pd.read_csv(f) for f in files], ignore_index=True)

df["match_key"] = (
    df["businessName"].str.lower().str.strip().str.replace(r"[^\w\s]", "", regex=True)
    + "_"
    + df["phone"].astype(str).str.replace(r"\D", "", regex=True)
)

df_deduped = df.sort_values("reviewCount", ascending=False).drop_duplicates(
    subset="match_key", keep="first"
)

df_deduped.to_csv("merged_no_duplicates.csv", index=False)

The keep="first" combined with sorting by review count means that when two rows match, you keep the one with more reviews on the (reasonable) assumption it's the more complete, more current record. Swap the sort key for whatever field matters most in your data.

If you have a Google Place ID for any of the rows, use that as the match key instead of the name/phone combination. It's a more reliable identifier precisely because it doesn't depend on how consistently a business name was formatted across exports. See the guide to Google Place IDs for what that identifier is and how to get one.

How MapsLeadExtractor Handles This on Import

If you're re-importing exported leads back into MapsLeadExtractor (for example, after cleaning a merged file, or adding a manually sourced list), the bulk import step checks each row against your existing leads before creating anything, and reports how many rows were skipped as duplicates rather than silently dropping them. It isn't magic: without a place ID available, it falls back to matching on business name, so a manual pre-clean using the method above still helps if your source data has inconsistent name formatting.

For the step before this, turning a single Google Maps search into a clean CSV in the first place, see the guide to exporting Google Maps search results to Excel. This post picks up after that: once you have two or more of those exports and need to combine them.

Sources

• Validity: The State of CRM Data Management in 2025: 37% of CRM users reported losing revenue due to poor data quality.
• Microsoft Learn: Combine CSV Files in Power Query: the Get & Transform method for merging multiple CSVs in Excel.
• Google Developers: Place IDs: why a place ID is a more stable match key than a business name.