How to Find the Best Email and Optimize Your Campaigns

Q: Which Emails Should Be Avoided in a Prospecting Campaign?

Systematically exclude: noreply@, unsubscribe@, newsletter@, desinscription@. These addresses are not monitored by humans — your messages will never be read. They can also trigger anti-spam filters and harm your sender reputation.

Q: Can This Classification Be Automated Without ChatGPT?

Yes, with Python and a library like pandas. You create a keyword dictionary by category, apply a matching function to each address, and export the result. It’s faster on large volumes and does not depend on ChatGPT's token limits. The prompt presented in this article gives you the exact logic to reproduce in code.

You export hundreds of Google Maps listings. Each listing contains several email addresses — sometimes up to 10. The problem: they are sorted by order of appearance, not by relevance. As a result, you send your campaigns to contact@ or noreply@ instead of reaching the sales director or the CEO.

Knowing how to find the best email in a CSV file makes the difference between a 2% response rate and an 8% response rate. This guide shows you a concrete method: export your Google Maps data, then use ChatGPT to automatically classify each address and identify the most useful contact for your prospecting.

Why Your Email Exports Are Not Optimized by Default

When you extract data from Google Maps, the emails retrieved come from the website of each establishment. They are collected in the order they appear on the page — not according to their strategic importance.

A restaurant may have reservation@, contact@, rh@, and direction@ in the same export. If you send your campaign to the first address on the list, you might be reaching the reservation service. Not the decision-maker.

The solution: classify these emails before sending anything.

The 3-Step Method: Export, Classification, Campaign

Step 1 — Export Your Google Maps Data with the Right Filters

Before classifying, you need a clean file. Here are the filters to apply to maximize the quality of your data:

Recommended Filters:

Establishments currently open
At least one email address present
Minimum Google rating (4+ to target active establishments)

These filters eliminate ghost listings and closed establishments. You are working with a usable database from the start.

Columns to Keep in the Export:

Company Name
Email 1, Email 2, Email 3 (individual columns)
Column "All Emails" (aggregated column)

Remove everything else. A cleaned CSV file with 4-5 columns is much easier for ChatGPT to process than a file with 50 columns.

Practical Tip: Limit your first test to 30-50 rows. ChatGPT handles smaller files better, and you can check the quality of the result before scaling up.

Step 2 — Classify Your Emails with ChatGPT

This is the core of the method. You will send your CSV file to ChatGPT with a precise classification prompt.

The Principle of Classification

ChatGPT analyzes the local part of each email address — what comes before the @. It compares this part to a list of keywords associated with professional categories.

Examples:

[email protected] → category Management
[email protected] → category Sales
[email protected] → category Recruitment
[email protected] → category Do not Use

The ultimate goal: identify the "Best Email" for each company according to a defined priority order.

The 12 Classification Categories

Here are the categories to include in your prompt, along with their associated keywords:

1. Decision Maker (default category if no other matches) Personalized addresses without a generic keyword — often the most direct contact.

2. Management Keywords: ceo, cfo, cto, president, director, executive, management

3. Sales Keywords: sales, commercial, vente, devis, marketing, business, leads, client

4. Partnership Keywords: partner, presse, media, communication, sponsor, affiliation, collaboration

5. Info / General Contact Keywords: contact, info, bonjour, hello, general, welcome, enquiry

6. Recruitment Keywords: rh, recrutement, talent, jobs, cv, carriere, emploi, hr

7. Support Keywords: sav, support, help, aide, assistance, service, faq

8. Technical Keywords: webmaster, tech, it, dev, admin, informatique, backend

9. Legal Keywords: legal, juridique, compliance, avocat, droit

10. Security Keywords: security, securite, privacy, cybersecurite, protection

11. Finance Keywords: finance, comptabilite, billing, facture, payment, tresorerie

12. Do not Use Keywords: unsubscribe, noreply, newsletter, desinscription

These addresses should be systematically excluded. Sending a campaign to noreply@ is pointless — and can harm your deliverability.

The Priority Order for the "Best Email"

When a company has multiple emails in different categories, here’s the order of preference to apply:

Decision Maker → Management → Sales → Partnership → Info/General Contact

If no email matches the top 5 categories, take the first available email by default.

The Complete Prompt to Copy-Paste

Here’s the prompt to use directly in ChatGPT. Attach your CSV file to the conversation.

"I will provide you with a CSV file containing email addresses, and I need you to classify each address into one of the following categories, based on the local part of the email (before the @):

1. Decision Maker (default if no category matches) 2. Management — keywords: ceo, cfo, cto, president, director, executive, management 3. Sales — keywords: sales, commercial, vente, devis, marketing, business, leads 4. Partnership — keywords: partner, presse, media, communication, sponsor, collaboration 5. Info/Contact — keywords: contact, info, bonjour, hello, general, welcome 6. Recruitment — keywords: rh, recrutement, talent, jobs, cv, carriere, emploi 7. Support — keywords: sav, support, help, aide, assistance, service 8. Technical — keywords: webmaster, tech, it, dev, admin 9. Legal — keywords: legal, juridique, compliance, avocat 10. Security — keywords: security, securite, privacy, protection 11. Finance — keywords: finance, comptabilite, billing, facture, payment 12. Do not Use — keywords: unsubscribe, noreply, newsletter

Instructions: - Analyze each email address (part before the "@"). - Base your analysis exclusively on the "All Emails" column to avoid duplicates. - Identify the "Best Email" for each company according to this order: Decision Maker → Management → Sales → Partnership → Info/Contact. - If the result is empty for a row, keep the original email by default. - Create a table with the columns: Company Name | Original Email | Category | Best Email (yes/no). - Present the results in a formatted table."

Handling Common ChatGPT Errors

ChatGPT can sometimes "hallucinate" — generating more rows than your source file contains. If this happens, add this instruction:

"Base your analysis exclusively on the 'All Emails' column. Do not generate new rows — only process existing data."

If the number of rows remains inconsistent, split your file into batches of 30 rows and process them separately.

Step 3 — Clean and Use Your Optimized File

Once the table is generated by ChatGPT, a few final adjustments:

Check for special characters. Accents and symbols can create import issues in your emailing tool. Convert the file to UTF-8 if necessary.

Filter on "Best Email = yes". You get a clean list with one email per company — the most relevant according to the defined hierarchy.

Exclude "Do not Use". Remove all rows where the category is "Do not Use" before importing into your sending tool.

This final file imports directly into Lemlist, Instantly, Brevo, or any cold emailing tool.

Why This Method Improves Your Response Rates

Most B2B email campaigns fail for a simple reason: they reach the wrong contact. Sending a commercial offer to support@ or rh@ generates deletions, not responses.

By directly targeting decision-makers and sales managers, you mechanically increase your chances of getting a response. An email sent to the right contact is worth 10 emails sent to generic addresses.

This method has another advantage: it protects your deliverability. Fewer bounces, fewer spam reports, better domain reputation in the long term.

IBLead: Up to 50+ Data Fields per Listing, Including Enriched Emails

If you are looking for a pre-indexed Google Maps database to fuel this workflow, IBLead covers 50M+ establishments in 37 countries. Each listing includes enriched emails from the establishment's website — exactly the data you need to apply this classification method.

The database is updated weekly. You filter by city, category, Google rating, number of reviews, and export to CSV instantly. No waiting, no real-time scraping — everything is already indexed.

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FAQ — Frequently Asked Questions About Email Classification

How to Choose the Right Email When a Company Has Several?

Follow the priority order: Decision Maker → Management → Sales → Partnership → Info/General Contact. A personalized email without a generic keyword (e.g., pierre.martin@) is almost always more effective than a service address. If you only have generic addresses, prefer contact@ over support@ for a commercial approach.

Can ChatGPT Handle Large CSV Files?

ChatGPT handles files well up to 500-1000 rows depending on the version used. Beyond that, split into batches of 50-100 rows. You can also use the OpenAI API with a Python script to automate processing on larger volumes.

Which Emails Should Be Avoided in a Prospecting Campaign?

Systematically exclude: noreply@, unsubscribe@, newsletter@, desinscription@. These addresses are not monitored by humans — your messages will never be read. They can also trigger anti-spam filters and harm your sender reputation.

Does This Method Work for All Sectors?

Yes. Keyword classification adapts to all sectors — restaurants, agencies, firms, local businesses. The categories remain the same. What changes is the priority order according to your goal: to sell a B2B service, prioritize Sales and Management. For a partnership, prioritize Partnership and Management.

How to Verify That Exported Emails Are Valid?

ChatGPT classification does not validate emails — it classifies them. To check deliverability, use an email verification tool like NeverBounce, ZeroBounce, or Bouncer before sending your campaign. This step avoids hard bounces that degrade your sender reputation.

Can This Classification Be Automated Without ChatGPT?

Yes, with Python and a library like pandas. You create a keyword dictionary by category, apply a matching function to each address, and export the result. It’s faster on large volumes and does not depend on ChatGPT's token limits. The prompt presented in this article gives you the exact logic to reproduce in code.

Conclusion

Knowing how to find the best email in a Google Maps export is a skill that can significantly change your prospecting results. The method is simple: export with the right filters, classify with ChatGPT, clean the file, import into your sending tool.

The classification prompt presented here covers 12 categories and applies to any CSV file containing email columns. Copy it, adapt the priority order to your context, and test on a batch of 30 companies before scaling up.

Your email campaigns deserve better than to land in a noreply inbox.

free credits — 200 credits included