Classifying Google Maps Emails: A Complete Guide to Optimize Your Campaigns

You extract contact lists from Google Maps. You get 5, 10, sometimes 15 emails per company. But which one to use? The generic email "contact@"? The one from the director? From sales?

This is the problem that every B2B prospector faces: emails are not sorted by importance. They arrive in your CSV file in the order they appear on the Google Maps listing, not in the order they convert.

This article shows you how to automatically classify your emails to identify the best contact — the one who truly has decision-making power.

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Why The Order Of Emails Matters For Your Prospecting

Before discussing classification, let’s understand the real problem.

When you extract 10 emails from a company, you get a raw list: - [email protected] - [email protected] - [email protected] - [email protected] - [email protected]

The problem: sending your pitch to the first email on the list is like playing the lottery. You might reach customer support who can’t make any decisions, or a "noreply" address that rejects all your messages.

The stakes: your response rate directly depends on the right email at the right time. A HubSpot study shows that contacting a decision-maker rather than an intermediary increases your conversion rate by 40% to 60%.

Email classification solves this problem by analyzing the structure of each address to identify its role in the organization.

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How Email Classification Works

Email classification uses a simple yet effective analysis: examine the part before the "@" to identify the type of contact.

The Basic Principle

Each email address contains clues about the contact's role:

• "director" or "ceo" → Management
• "sales" or "business" → Sales
• "support" or "customer service" → Customer Support
• "hr" or "recruitment" → Recruitment
• "contact" or "info" → Generic address

This isn’t magic; it’s pattern matching — recognizing keywords that reveal the contact's role.

The 10 Categories To Know

For effective prospecting, classify your emails into these categories:

1. Management (Priority 1) - Keywords: CEO, director, president, manager, executive, leadership - Example: [email protected], [email protected] - Why: these are the real decision-makers

2. Sales (Priority 2) - Keywords: sales, business development, account - Example: [email protected], [email protected] - Why: they understand sales and can speed up decisions

3. Partnership (Priority 3) - Keywords: partnership, collaboration, media, communication, press - Example: [email protected], [email protected] - Why: they manage external relations and opportunities

4. General Info/Contact (Priority 4) - Keywords: contact, info, hello, general - Example: [email protected], [email protected] - Why: this is an entry point, not a decision-maker

5. Customer Support - Keywords: support, help, service, assistance - Example: [email protected], [email protected] - Why: they don’t decide; they execute

6. Technical - Keywords: tech, IT, dev, webmaster, admin - Example: [email protected], [email protected] - Why: useful for IT projects, not for general prospecting

7. Recruitment - Keywords: HR, recruitment, jobs, career - Example: [email protected], [email protected] - Why: they recruit, don’t decide on your services

8. Finance - Keywords: finance, accounting, billing - Example: [email protected], [email protected] - Why: they manage budgets, can approve expenses

9. Legal/Compliance - Keywords: legal, compliance, attorney - Example: [email protected], [email protected] - Why: important for certain sectors, less for general prospecting

10. Addresses To Absolutely Avoid - Keywords: noreply, unsubscribe, newsletter, bounce - Example: [email protected], [email protected] - Why: these addresses reject incoming emails

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The Junk Emails You Need To Eliminate

Before classifying, remove unusable emails. Sending to these addresses harms your deliverability and sender reputation.

The 4 Types Of Emails To Ban

1. "Noreply" Addresses

Example: [email protected], [email protected]

Why it’s poison: these addresses are set up to reject all incoming emails. Your message bounces, and your sending domain is marked as a "bad sender". This is a strong signal for mail servers.

2. Unsubscribe Addresses

Example: [email protected], [email protected]

Why it’s poison: sending a prospecting email to an unsubscribe address is considered aggressive spam. You risk being blacklisted.

3. Newsletter Addresses

Example: [email protected], [email protected]

Why it’s poison: these are broadcast addresses, not inboxes. Your message disappears into massive mailing lists.

4. Generic Addresses Without Clear Role

Example: [email protected] (if it’s the only available address and you have alternatives)

Why it’s less effective: you don’t know who reads your message. It could be an intern or a decision-maker — you’re not targeting anyone.

Simple rule: if an address contains "noreply", "unsubscribe", "bounce", or "newsletter", remove it from your prospecting list.

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Method 1: Manual Classification With Spreadsheet

If you have a small list (fewer than 100 contacts), you can manually classify in 30 minutes.

Step 1: Export Your CSV File

Retrieve your email list from your extraction tool. You need at least two columns: - Company Name - Email(s)

Open the file in Excel or Google Sheets.

Step 2: Create A "Category" Column

Add a column titled "Category" next to the email column.

Step 3: Analyze Each Email

For each email, look at the part before the "@" and ask yourself: "What is this person's role?"

Practical examples:

Email	Analysis	Category
[email protected]	Contains "director"	Management
[email protected]	Generic	Info/Contact
[email protected]	Contains "sales"	Sales
[email protected]	Contains "support"	Support
[email protected]	Contains "noreply"	❌ TO REMOVE

Step 4: Identify The "Best Email"

For each company, mark the best email with a "Best Email" column = YES/NO.

Order of priority: 1. Management (CEO, director) 2. Sales (sales) 3. Partnership (media, collaboration) 4. Info/Contact 5. All others

If a company has these three emails: - [email protected] (Info/Contact) - [email protected] (Sales) - [email protected] (Management)

Mark [email protected] as "Best Email" = YES.

Step 5: Export And Use

Once classified, sort your list by "Best Email" = YES and start your prospecting with these contacts.

Total time: 30 minutes for 100 emails.

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Method 2: Automated Classification With ChatGPT

For larger lists (500+ emails), use ChatGPT with a structured prompt to automate the process.

Why ChatGPT?

ChatGPT can analyze your 500 emails in 2 minutes instead of 2 hours. It applies the same classification rules consistently, without fatigue.

Preparation: Create Your CSV File

Export your list with these minimum columns:

Company,Email1,Email2,Email3,Email4,Email5
Pizzeria Roma,[email protected],[email protected],[email protected],,
Restaurant Le Gourmet,[email protected],[email protected],[email protected],,
Bakery Dupont,[email protected],[email protected],,,

The Complete Prompt For ChatGPT

Copy this prompt and adapt it to your needs:

You are a commercial prospecting expert. I will provide you with a CSV file with companies and their email addresses. 

Your goal: classify each email address into one of these categories, analyzing ONLY the part before the "@" (before the at sign).

CLASSIFICATION CATEGORIES:

1. MANAGEMENT (CEO, director, president, manager, executive)
   Keywords: ceo, director, president, executive, manager, leadership, chief

2. SALES (Sales, business development)
   Keywords: sales, business, development, account, client, prospect

3. PARTNERSHIP (Collaboration, media, communication, press)
   Keywords: partnership, media, press, communication, collaboration

4. INFO/CONTACT (Generic address)
   Keywords: contact, info, hello, general, inquiry

5. SUPPORT (Customer service, assistance)
   Keywords: support, help, service, assistance

6. TECHNICAL (IT, development, webmaster)
   Keywords: tech, it, dev, webmaster, admin

7. RECRUITMENT (HR, jobs, career)
   Keywords: hr, recruitment, jobs, career

8. FINANCE (Accounting, billing)
   Keywords: finance, accounting, billing

9. LEGAL (Legal, compliance)
   Keywords: legal, compliance, attorney

10. TO AVOID (Noreply, newsletter, unsubscribe)
    Keywords: noreply, newsletter, unsubscribe, bounce

PRIORITY ORDER (if multiple categories match):
1. Management
2. Sales
3. Partnership
4. Info/Contact
5. Others

INSTRUCTIONS:
- Analyze EACH email (column Email1, Email2, etc.)
- Classify it into ONE category only
- Identify the "Best Email" for each company (the most prioritized according to the order above)
- Exclude "TO AVOID" emails
- Return a table with:
  * Company
  * Email
  * Category
  * Best Email (YES/NO)

Here is my CSV file:

[PASTE YOUR CSV FILE HERE]

Execution: Copy-Paste In ChatGPT

1. Open ChatGPT (https://chat.openai.com)
2. Create a new conversation
3. Copy the prompt above
4. Replace [PASTE YOUR CSV FILE HERE] with your actual file
5. Press Enter

ChatGPT will generate a classified table in seconds.

Expected Result

ChatGPT will return something like:

| Company | Email | Category | Best Email |
|---------|-------|----------|------------|
| Pizzeria Roma | [email protected] | Info/Contact | NO |
| Pizzeria Roma | [email protected] | Management | YES |
| Pizzeria Roma | [email protected] | Sales | NO |
| Restaurant Le Gourmet | [email protected] | Info/Contact | NO |
| Restaurant Le Gourmet | [email protected] | Management | YES |

Verification And Adjustments

Sometimes ChatGPT "hallucinates" — it generates inaccurate results. Check:

• Does the number of rows match your original file?
• Do the categories make sense?
• Are there any obvious errors?

If you find errors, send a correction message:

"I noticed you classified '[email protected]' as 'Management'.
That's incorrect — 'support' should be classified as 'Customer Support'.
Correct the table and try again."

ChatGPT will correct and reapply the rule to the entire file.

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Method 3: Use A Dedicated Tool (Recommended Approach)

The previous two methods work, but they require manual work or constant adjustments.

The best approach: use a platform that already does this classification for you.

Why A Specialized Platform?

Modern Google Maps extraction tools integrate email classification directly into the export. You don’t need to: - Copy-paste into ChatGPT - Manually check results - Clean data afterward

Everything is done automatically, with over 95% accuracy.

What A Good Tool Should Do

A proper lead generation tool should:

✅ Extract all emails from a Google Maps listing (not just the main one) ✅ Automatically classify each email by contact type ✅ Identify the "best email" — the one with the highest response rate ✅ Exclude "junk" addresses (noreply, newsletter, etc.) ✅ Enrich emails with other data (phone, address, website) ✅ Allow filtering by Google rating, number of reviews, claimed listing ✅ Export in ready-to-use CSV

Advantages Of An Integrated Platform

Time-saving: instead of 2 hours for 500 emails, you get the result in 30 seconds.

Accuracy: classification algorithms are tested on millions of emails. No hallucinations like with ChatGPT.

Compliance: the best platforms comply with GDPR and only collect public data.

Integration: export directly to your CRM, email tool, or spreadsheet.

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Real Use Cases: Where This Classification Changes Everything

Case 1: Restaurant Prospecting

Your goal: sell online reservation software to restaurants.

Without classification: - You send to [email protected] - A server receives your email, deletes it - 0% response

With classification: - You send to [email protected] - The director reads your message - He sees it could increase his reservations - 15-20% response

Impact: you multiply your responses by 15.

Case 2: B2B SaaS Prospecting

Your goal: sell a CRM to SMEs.

Without classification: - You send to [email protected] - It goes to reception - Never read by a decision-maker - 2-3% response

With classification: - You send to [email protected] AND [email protected] - Both decision-makers receive your message