How to Clean a Google Maps Lead List (2026)

What's already clean when the file lands

Before cleaning anything, it helps to know which problems the scrape already handled, so you don't spend an afternoon hunting for duplicates that can't be there. In a MapsHarvest export:

• Same listing twice in one job: When a business matches several of your searches or sits near a city boundary, it can surface more than once. Rows are deduplicated on the Google Maps URL before they're written, so each listing appears once per export.
• Listings from your earlier jobs: A listing whose Maps URL appeared in any of your previous jobs is skipped, so a second scrape of an overlapping area doesn't hand you rows you already have.
• Rows that fail your filters: Minimum rating, minimum review count, has-website and has-phone run during the scrape. A listing that fails them never reaches the file.

So exact-duplicate removal is mostly done. What remains is judgment work the scraper can't do for you: deciding which categories you actually sell to, recognizing that two different listings are one business, and knowing which rows your CRM already owns. Exact duplicates come back mainly when you merge files from different tools or different accounts — that's the case the first row of the table below covers.

The cleaning order, step by step

Order matters because each step shrinks the file for the next one, and because some checks give wrong answers if you run them early. Counting shared phone numbers before you've removed closed listings, for example, makes a relocated business look like a duplicate of itself.

Steps 3 to 5 depend on the website and website_domain columns, which are included from the Starter plan up. On the free plan you can still run steps 1, 2, 6, 7 and 8 on the core fields.

Reading the duplicates: what each match means

"Duplicate" covers several different situations in Maps data, and only one of them should be deleted automatically. The column that matches tells you which situation you're in:

Address is missing from that list on purpose. Shared addresses are normal on Maps — every tenant of a medical office building, a strip mall, or a coworking space has the same street address, often down to the suite. Two businesses at one address are two leads.

The checks as Google Sheets formulas

These assume the phone is in column D and website_domain in column F; adjust the letters to your file. Put the first formula in a helper column R and the rest reference it. For the social-site check, make a second tab named Social with one domain per row in column A — facebook.com, instagram.com, linktr.ee, yelp.com and whatever else shows up in your files.

In Excel, Data → Remove Duplicates on the Google Maps URL column handles exact duplicates, and the COUNTIF formulas work unchanged. For the regex ones, use Excel's REGEXREPLACE if your version has it, or strip spaces, dashes, dots and parentheses with nested SUBSTITUTE calls. If you're still at the export stage, the Excel export guide covers getting the file in cleanly in the first place.

Sort by the shared-phone count, highest first, and review the top of the list. The first few groups are almost always the interesting ones: a franchise hotline on 30 listings, a dental group on 8. Past a count of 2 or 3, the reason is usually obvious from the name column.

The same checks as a pandas script

If you clean a file like this every week, a script saves the formula-copying and gives the same result every time. This one runs steps 1 through 6 and step 8 and writes the flags as new columns rather than deleting rows, so you can still sort and review before anything is thrown away. It assumes US or Canadian numbers; for UK, Australian, German or Indian lists, swap the last-10-digits rule for a proper parser such as the phonenumbers library.

import pandas as pd

df = pd.read_csv("leads.csv", dtype=str).fillna("")

# 1. Rows you'll never contact
if "temporarily_closed" in df.columns:
    df = df[df["temporarily_closed"].str.lower() != "true"]
df = df[df["phone"] != ""]

# 2. Category decisions, made once per category (edit this list)
DROP_CATEGORIES = {"Plumbing supply store", "Water utility company"}
df = df[~df["category"].isin(DROP_CATEGORIES)]

# 3-4. Shared phones and shared domains (US/Canada: last 10 digits)
df["phone_digits"] = df["phone"].str.replace(r"\D", "", regex=True).str[-10:]
df["listings_on_phone"] = df.groupby("phone_digits")["google_maps_url"].transform("count")
df["listings_on_domain"] = df.groupby("website_domain")["google_maps_url"].transform("count")
df.loc[df["website_domain"] == "", "listings_on_domain"] = 0

# 5. Websites that aren't websites
SOCIAL = {"facebook.com", "instagram.com", "linktr.ee", "yelp.com",
          "tiktok.com", "x.com", "twitter.com", "nextdoor.com"}
df["own_website"] = (df["website_domain"] != "") & ~df["website_domain"].isin(SOCIAL)

# 6. Dialer format and toll-free flag
df["phone_e164"] = "+1" + df["phone_digits"]
df["toll_free"] = df["phone_digits"].str[:3].isin(
    ["800", "833", "844", "855", "866", "877", "888"])

# 8. Already in the CRM?
crm = pd.read_csv("crm_export.csv", dtype=str).fillna("")
crm_phones = set(crm["phone"].str.replace(r"\D", "", regex=True).str[-10:])
df["in_crm"] = df["phone_digits"].isin(crm_phones)

df.to_csv("leads_clean.csv", index=False)

On the Growth plan and up you can skip the CSV download step and feed the script straight from the REST API: GET /jobs/{id}/download?fmt=csv returns the same file; wrap the response text in io.StringIO and pass it to pd.read_csv. Pair it with a webhook on job completion and the cleaned file is ready without anyone opening the dashboard.

What not to clean out

The most expensive cleaning mistake with Maps data isn't leaving junk in. It's deleting good leads that look like junk:

For the no-website segment specifically, see how to find businesses without a website. For rows with a real website but no email, the email-finding guide picks up where this one ends.

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