Local Prospecting: Extract and Customize 1000+ Google Maps Contacts in 30 Minutes

Local businesses are everywhere on Google Maps. Plumbers, restaurants, hair salons, real estate agencies — they are all there. The problem? You can't contact them one by one. You need data at scale, but also personalized so your emails don't look like spam.

This article shows you how to do it. No vague theory. Just concrete: steps, examples, real numbers.

Why Personalized Local Prospecting Works Better

Personalization is not a luxury — it's a necessity. A generic email ("Hello, do you want to earn more money?") has a response rate of 1-3%. A personalized email ("Hello [name], I saw that your restaurant has 247 reviews at 4.8 stars on Google Maps") has a response rate of 8-15%.

That's 5 times better.

Why? Because the recipient sees that you took 30 seconds to learn about them. You're not sending 10,000 identical emails. You're speaking to their situation.

Local businesses are ideal for this. Unlike large companies (which have little interesting public data), small businesses have a rich Google Maps listing:

• Number of reviews (and their content)
• Average rating
• Opening hours
• Photos
• Exact address
• Phone number
• Website (often)
• Social media

All of this is public. All of this can be used for personalization.

Where to Find the Data: Google Maps is Your Gold Mine

Google Maps is not just an app to find restaurants. It's a public database of 50 million+ businesses in 37 countries.

Search for "plumbers in Lyon" or "hair salons in Marseille" — you'll see 50, 100, sometimes 500 results. Each has a listing with actionable information.

The challenge: extracting this data manually is impossible. Clicking on each listing, copying the name, phone, email... it takes 2-3 minutes per business. For 1000 contacts, that's 33-50 hours of manual work.

This is where data extraction (data scraping) comes in.

Three Approaches to Extract Google Maps Data

Approach 1: The Manual Method (Free, but Time-Consuming)

You open Google Maps, search for "restaurants in Nashville", and copy-paste the information into an Excel spreadsheet.

Result: 187 restaurants in 3-4 hours of work.

Advantages: - Free - You control each data point

Disadvantages: - Very long - Frequent errors (wrong numbers copied, missing emails) - No access to detailed reviews - No smart filtering

This approach works for 50-100 contacts. Beyond that, it's wasted time.

Approach 2: Code It Yourself with Python (Technical, but Limited)

You write a Python script using the pandas library to automate the extraction. You import an Excel file, manipulate the columns, and create custom variables.

Example code:

import pandas as pd

df = pd.read_excel('restaurants.xlsx')
df['message'] = "Hello " + df['name'] + ", I saw that you have " + df['number_of_reviews'].astype(str) + " reviews at " + df['rating'].astype(str) + " stars."

df.to_csv('personalized_messages.csv', index=False)

Result: Personalized messages in a few minutes.

Advantages: - Automated - Flexible - Low cost

Disadvantages: - Requires technical skills - You must first extract the data (Method 1 or a basic scraper) - Detailed reviews are not accessible - No detection of technologies on the website - No email enrichment

For 1000 contacts, it's doable. For 10,000, it gets complicated.

Approach 3: Use a Specialized Tool (The Best Compromise)

A tool like IBLead extracts data directly from Google Maps, with 70+ columns available:

• Basic information (name, address, phone)
• Google Maps data (rating, number of reviews, full reviews)
• Emails enriched from the website
• Detected technologies (WordPress, Shopify, HubSpot, etc.)
• Social media
• SIRET/SIREN (in France)

You only need 5 minutes to extract 1000 contacts. No coding. No manual manipulation.

Result: 1000 contacts ready to personalize in 30 minutes (extraction + filtering + preparation).

Advantages: - Very fast - Complete and reliable data - Advanced filtering built-in - Google reviews included - Technology detection - Customer support

Disadvantages: - Monthly cost (starting at €44/month)

For most prospectors, this is the best choice.

How to Extract 1000 Contacts in 30 Minutes (Step by Step)

Here’s the exact process you will follow.

Step 1: Define Your Target (2 Minutes)

Before extracting anything, ask yourself these questions:

• What sector? Restaurants, plumbers, real estate agencies, hair salons, etc.
• What geographical area? A city, a department, a region, an entire country?
• What type of business? All, or only those with a website, or only claimed (verified)?

Example: "I’m looking for all restaurants in Paris with at least 50 reviews and a website."

Step 2: Extract the Data (5 Minutes)

You go to IBLead, select your category (4000+ available), your location (city, region, or entire country), and start the extraction.

The tool shows you how many results match. For "restaurants in Paris", you’ll easily get 2000-5000 results.

You click on "Export" and wait. Usually, it’s ready in 1-2 minutes.

Step 3: Filter the Data (5 Minutes)

You have 2000 restaurants. But you only want those with:

• At least 50 reviews (to avoid new or inactive listings)
• A rating ≥ 4 stars (to avoid bad restaurants)
• An accessible email (to be able to contact them)

IBLead allows you to filter before export. You check your criteria and restart the export. You go from 2000 to 800 qualified restaurants.

Step 4: Prepare the Columns (5 Minutes)

You download the CSV or Excel file. You have 70+ columns available. For personalization, you only need a few:

• name (restaurant name)
• number_of_reviews (total number of reviews)
• average_rating (4.8, 4.5, etc.)
• 5_star_reviews (number of 5-star reviews — exclusive to IBLead)
• address (or just the city)
• email
• phone
• technologies (WordPress, Shopify, etc. — exclusive to IBLead)

You keep only these columns in your file. You delete the rest to simplify.

Step 5: Create the Personalized Messages (10 Minutes)

This is where it gets interesting. You will create multiple variants of messages, each using different variables.

Variant 1: The "Review" Message

Hello [name],

I saw that your restaurant has [number_of_reviews] reviews at [average_rating] stars on Google Maps. 
Congratulations! It’s a great testament to the quality of your service.

I’m reaching out to restaurants like yours because...

[Your pitch]

Variant 2: The "Technology" Message

You use the technologies column. If the restaurant doesn’t have WordPress (meaning it doesn’t have a modern website), you send:

Hello [name],

Looking at your online presence, I noticed that you don’t have a website yet. 
That’s unfortunate because [reason].

I can help you with...

[Your pitch]

Variant 3: The "Location" Message

You split the address to extract just the city:

Hello [name],

I was looking for restaurants in [city] with a good reputation. 
Your establishment stands out in the results. I’d like to talk to you about...

[Your pitch]

To create these messages at scale, you can:

1. Use a Python script (if you know how to code)
2. Use ChatGPT (easier)
3. Use a tool like Make.com (integration with IBLead)

ChatGPT Approach (The Simplest)

You upload your CSV file to ChatGPT and write a prompt like this:

From this CSV file, create a column "personalized_message" 
using this template:

"Hello [name], I saw that you have [number_of_reviews] reviews 
at [average_rating] stars on Google Maps. Congratulations! 
I am [your name], and I work with restaurants 
to improve their [goal]. Do you have 5 minutes tomorrow?"

Save the file as CSV.

ChatGPT will generate the messages in seconds. You download the file. It’s ready.

Concrete Example: 1000 Parisian Restaurants in 30 Minutes

Here’s what it looks like in reality.

Minute 0-5: Extraction

You go to IBLead. You select: - Category: "Restaurants" - Location: "Paris, France" - Results: ~3500 restaurants found

You start the export. 5 minutes later, you have a file with 3500 restaurants.

Minute 5-10: Filtering

You apply the filters: - At least 50 reviews - Rating ≥ 4 stars - Accessible email

Results: 1200 qualified restaurants.

Minute 10-15: Preparation

You open the Excel file. You keep only the columns: - name - number_of_reviews - average_rating - 5_star_reviews - address - email

You delete the 60+ unnecessary columns.

Minute 15-30: Personalization

You upload your file to ChatGPT. You write a prompt. ChatGPT generates 1200 personalized messages in 30 seconds.

You download the final file.

Final Result:

1200 restaurants with messages like:

Hello Chez Marie,

I saw that you have 347 reviews at 4.7 stars on Google Maps. 
Congratulations! I’m Paul, I work with restaurants to...

Each message is unique. No copy-pasting.

Data You Can't Get Elsewhere

Here’s what makes IBLead unique for local prospecting.

1. Google Reviews (Full Text, Not Just the Rating)

IBLead or other tools give you the average rating. IBLead gives you the content of the reviews:

• Full text
• Author
• Date
• Rating

Why is this useful? You can read negative reviews and personalize your message:

Hello [name],

I read your customers' reviews on Google Maps. 
One customer mentioned: "[negative review quote]"

I specialize in [solution] and I could help you with...

It’s hyper-personalized. The response rate goes up to 20-25%.

2. Detection of 160+ Technologies

IBLead detects if the restaurant's website uses:

• WordPress, Shopify, Wix (platform)
• WooCommerce (e-commerce)
• HubSpot, Mailchimp (marketing automation)
• Google Analytics, Facebook Pixel (tracking)
• Stripe, PayPal (payments)
• And 150+ other technologies

Why is this useful? You can target restaurants with outdated websites:

Hello [name],

I noticed that your site uses [dated technology].
Many restaurants are switching to [modern technology] 
to increase their bookings by [%].

Do you have 15 minutes to discuss?

3. SIRET/SIREN (In France Only)

For French restaurants, IBLead provides:

• SIRET (identification number)
• SIREN (business number)
• Legal form (SARL, EIRL, etc.)
• Creation date
• Estimated number of employees

This allows you to:

• Verify that the business really exists
• Find the name of the manager
• Know if it's a new business (created 6 months ago = no money to invest)
• Estimate the size

No competitor does this.

Advanced Filtering: How to Qualify Your 1000 Contacts

You don’t need 1000 contacts. You need 1000 qualified contacts.

IBLead allows you to filter by:

Basic Data: - Email present (or absent) - Phone present - Website present - Social media present

Google Maps Data: - Average rating (4.5-5.0, 4.0-4.5, etc.) - Number of reviews (50-100, 100-500, 500+) - Claimed listing (verified by the owner) - Number of photos

Advanced Data: - Detected technologies (WordPress, Shopify, etc.) - Specific reviews (filter by content) - Valid SIRET/SIREN (France)

Example of Smart Filtering:

You’re looking for restaurants to target for an online reservation management solution.

You filter: - Restaurants with a website ✅ - But WITHOUT Wix or Squarespace (since they already have an integrated solution) - With at least 100 reviews (so active) - With social media (so marketing-aware)

You go from 3500 restaurants to 400 highly qualified restaurants.

Your response rate increases. Your prospecting time decreases.

Automate with Make.com (Optional, but Powerful)

If you really want to automate, you can connect IBLead to Make.com to create a workflow:

1. Extract 100 new restaurants every week from IBLead
2. Enrich the data with ChatGPT (create personalized messages)
3. Send the emails via Gmail or Lemlist
4. Log the responses in a Google Sheet

All of this without manual intervention.

This is for advanced prospectors, but it works very well.

GDPR Compliance: What You Need to Know

Before sending 1000 emails, let’s talk legality.

Are the Data You Extract Public?

Yes. The information on Google Maps (name, address, phone, reviews) is visible to everyone. It’s not hacking.

But Can I Really Use Them for Prospecting?

Yes, with rules:

1. B2B data is less protected: A restaurant is a business. GDPR rules are less strict than for individuals.

2. You must respect the unsubscribe right: Every email must have a link