AI-Powered Cold Email Personalization for Local Businesses: The Complete Data-Driven Automation Guide

Let's take 2 minutes to analyze this fundamental statement about cold email success.

When we break down the concept of AI-powered cold email personalization for local businesses, we need to start with the word "personalize."

What Makes Cold Email Personalization So Powerful?

Cold email personalization is what makes your message authentic rather than sending a generic message like "Oh, you are a company, you want to make more money, click here." We are going to make our message as specific as possible to our recipient's current situation.

To achieve this level of personalized cold emails, especially for local business outreach, we need data. That's where web scraping comes in. We can scrape the company name, phone number, number of reviews, and any other information that will enable us to apply relevant variables to our email marketing campaigns.

Of course, before diving into any cold email strategy, you should understand whether cold emailing is legal to ensure you're staying compliant with regulations.

Why Local Businesses Require a Different Approach

The term "local businesses" is particularly important because a local company has very little news, unlike a large company which is generally much more active on social networks. In this case, we have access to more interesting variables for our customization.

In this guide, we'll look at different ways of applying these variables. The very first thing we have to do is collect our data. And trust me, if you want to learn how to write effective cold emails, having good data is absolutely crucial.

Step 1: Google Maps Lead Generation and Data Collection

If we want to target small or medium-sized companies, the best platform to find this data is Google Maps. Let me show you how this web scraping Google Maps process works with a practical example.

I'm going to make my Google Maps search: "restaurants near Nashville, Tennessee, USA."

Now, I could try to do the scraping by myself, but it is a time-consuming method and it will be very likely I will get tons of errors that I will have to handle. I don't have time for that! What I'm going to do instead is use a template - a pre-made template.

If you're interested in the complete technical approach, check out our comprehensive guide on Google Maps scraping which covers all the technical details.

Using Pre-Made Templates

What I'm going to do instead is use a template - a pre-made template. Let's check if there is something relevant based on Google Maps.

I have to insert my keyword - the keyword is "restaurants near Nashville Tennessee USA." For number of pages, I think it refers to the number of companies, so let's say 100. I give a name to my task and click on "start standard mode," and I simply have to wait for a bit of time.

The more data fields and columns I get, the more I'll be able to create relevant variables. Let's see what we got: 187 data rows - that's better than expected! Why not?

I'll export my data in an Excel or CSV file. Let's double-check the data we've collected. We've got a lot of valuable information like:

• Company name
• Number of reviews
• Ratings
• Address
• URL of pictures
• Opening hours

Something like this, okay? So based on these columns, we're going to apply our variables.

Step 2: Creating Personalized Messages with Code

Based on these columns, we're going to apply our variables. Let me show you how.

I'm going to write a bit of code to create three different personalized messages. Obviously, you can do it in another way, but in my case, I'm going to use a library called pandas. I've also imported openpyxl if I need to manipulate Excel files. It's pretty much like if you do it directly in Excel, but in my opinion, this method is better because it's more intuitive.

All right, let's get started!

Setting Up the Data Frame

First of all, I import pandas as pd and create what we call a data frame: pd.read_Excel. I import my Excel file and I've got 187 rows.

Let's take the first message as an example:
"Hey [name], I just tried to call you on [phone number] but couldn't get through. Figured it might be better to email you."

The first question we ask ourselves is: do we have a variable for name and do we have a variable for phone number?

The name is the "title" in our case, and the phone number is the column entitled "phone." With pandas, we can perfectly select a specific column. All I have to do is write the name of my column. So if I want to select the title, I do df['title'].

Building the Personalized Message

Now let's assume I want to create my message in another column, so I'm going to create another column which doesn't exist yet - something like df['comment']. For the moment, let's say it's equal to zero.

As you can see, we've got our comment column now. This comment column is equal to:
"Hey " + df['title'] + "I just tried to call you on " + df['phone']

We've got "Hey" plus the title. Let's make a line break - if I want to make a line break, I do plus "\n". It looks good!

Let's save our data frame: df.to_excel() or to_csv() - it depends on the format you prefer. I'm going to call it "restaurant_nash.xlsx". We don't have any errors, and we've got something which is personalized each time. All right!

Testing Different Message Templates

Let's take our second example now: "Hey [name], saw that you had [number of reviews] five-star reviews for your [business type] in [location]. That's awesome!"

We ask ourselves the same question: do we have access to all of these parameters?

• Name: yes (title)
• Business type: yes (category)
• Location: yes (address)

But do we have access to the number of five-star reviews? We don't. We only have access to the total number of reviews, but we don't have any more details, which means that unfortunately we cannot apply this second message template yet. Bye-bye!

Creating Location-Specific Variables

Can we write the third personalized message instead? "Hey [name], I was looking for [business type] around [specific location]. Saw your five-star reviews!"

We have the name, we have the business type, and let's assume that a specific location is just part of the full address. We don't have exactly that specific location criteria, but we can create it because it's based on another column that already exists.

All we have to do is apply a regex or split our text based on the comma in the address field. Let's create another column called "specific_location":

df['specific_location'] = df['address'].str.split(',').str.get(0)

This splits the address on commas and gets the first part (the most specific location).

Did it work? It did work! Now let's change our comment data field:
"Hey " + df['title'] + ", I was looking for " + df['category'] + " around " + df['specific_location'] + ". Saw your reviews!"

Let's save it, and everything sounds good to me.

The Problem with DIY Approaches

As we have seen, this method actually works, but let's be honest - it's not an ideal one. It looks more like a DIY approach. It's something I've done based on the knowledge I already have, but the point is we don't have to acquire this technical knowledge, and maybe we don't have the time to apply it.

And it raises a question: is there an easier solution? Something that will allow us to get the same result, or maybe a better result, without all these technical difficulties and spending hours on it?

Yes, there is a solution, and it is called Scrap.io.

Introducing Scrap.io: The Ultimate Solution

Scrap.io is the ultimate weapon for web scraping on Google Maps. Given that all personalization starts with data, it is the ideal tool. With over 200 million establishments indexed and the capability to process 5,000 queries per minute, it's the only solution that allows you to extract all Google Maps listings at a country scale in just two clicks, without code.

This time, you won't need to manipulate data because there are a lot of columns - there are about 70 of them! For example, in terms of location, we don't just have the address; we have the address divided into different parts, and that's just one example.

We have access to lots of other data like:

• Email addresses
• Metadata
• Social networks
• And much more

So we have access to lots of variables - we can personalize our messages in plenty of different ways. What sets Scrap.io apart is its real-time data extraction directly from Google Maps, ensuring you always have up-to-date information for your cold email personalization efforts.

Perhaps it's best if I show you how to use the tool. In the meantime, feel free to click on the link in the description for free access to your first 100 leads. I will see you on Scrap.io!

Hands-On Demonstration with Scrap.io

I'll be able to collect all my leads. All I need to specify is an activity (meaning a profession) and different location criteria.

Let's take our previous example: I'm looking for restaurants. Obviously, I can pick another category if I want to - there are 4,000 different categories listed on Google Maps!

If I want to choose Nashville, I have to change the country as well - United States. There we go: Nashville. When I click on search, I'll be able to determine how many leads I'll be able to retrieve.

Let's see if we can get more or less than 187 leads, which is the number we got using the template. Let's take a look - actually, there are a lot more!

Please note that you can also scrape data based on an entire country, an entire state, or specific regions, depending on your subscription plan.

Advanced Filtering Options

I have an overview of what my results look like. Now I can filter my data. We have several criteria, for example:

• I only want restaurants with at least one email address
• With social media links
• With a specific price range
• With a range for the number of reviews
• With pixels on websites
• And so on

I make my choices, click on "filter," and I'll get more accurate results.

Exporting Your Data

If everything sounds good to me, I click on "Export" and "Advanced options." I can check which columns to export. As you can see, we've got around 70 columns in total, but in my case, I've only chosen to get some columns because I only need a couple of columns to create our personalized messages.

I want to get the first 30 data rows in my case, and I click on "export."

Now I'm in the "My Exports" tab, and I simply have to wait. I can update my tasks if they are at least one month old, and I can download what I've got in a CSV or Excel file. I'm going to get both the CSV and Excel files - we will see why in a minute, so let's do it.

Let's double-check the exported data. We've got the location data fields, which is good, and we have access to the number of reviews per score, which is important because based on this column, we'll be able to write personalized messages.

Remember, we need to determine exactly what the number of five-star reviews is - based on this column, it now becomes possible! The address, email, and that's it for now.

For a deeper dive into extracting email addresses specifically, check out our detailed guide on how to find email addresses from Google Maps.

Using AI for Enhanced Cold Email Personalization

Now, how can I write my personalized messages without writing the code we did previously? Maybe I can achieve a similar result, and even a quicker result, by using AI for cold email personalization.

If I write the correct prompt in ChatGPT, will I be able to get the result I want? This represents the future of automated cold email personalization.

Here's the challenge: the first thing I want to do is extract only the five-star review count from data that looks like "5: 284, 4: 123, 3: 45..." I could write a regex to identify that specific text, but again, I think I can do it in an easier way, and this is what we are about to see right now.

Working with ChatGPT

Here we are on ChatGPT. I've selected ChatGPT-4 and picked the Premium plan because, to be honest, I had a lot of problems with the free plan. When I switched to premium, these problems magically disappeared!

So what should I write here? Well, first I have to import my CSV file, and I have to write my prompt:

"From this CSV file, create a 'number_five_stars' column from the 'reviews_per_score' column which contains the number contained after 'five' and before the colon sign. Save the file in CSV format."

For example, if I have "5: 284, 4: 123" in the reviews_per_score column, I should end up with "284" in the number_five_stars column.

I'm not an expert in writing prompts, but at least I structured my text a little bit. Here's my approach:

1. Task: What should I do? (Create another column - that's an action verb)
2. Context: From this CSV file, using specific columns
3. Example: Very important - showing what I have and what I want to get
4. Format: Save the file in CSV format

Let's try it out. To be honest, I don't know if it's going to work - I didn't try it before. Let's check it out together.

It looks like magic to me! We can download the updated file and we have access to a preview. Let's check it out - it looks really good! We can download the file.

I'll get a CSV file which is not really visible for human eyes, so what I'm going to do is transform my CSV into an Excel file. A few moments later: the number of five stars looks correct!

Creating the Final Personalized Messages

Now that we've got an updated file, we're able to write that personalized message. But again, this time we're not going to use the pandas library - we're not going to write any lines of code. We're going to use ChatGPT!

Let's create a second prompt to do our job:

"From this CSV file (the updated CSV file), create a 'comment' column from the name, number_five_stars, main_category, and street_one columns. The value of the comment column must include the following formula: 'Hey [name], saw that you had [number_five_stars] five-star reviews for your [main_category] in [street_one]. That's awesome!' Save the file in CSV format."

Again, we've got:

• The task
• The context
• The example (very important)
• The format

Let's copy and paste our text, upload the CSV file, and cross our fingers!

That was even faster this time! Let's check the preview - the preview looks good. I download the CSV file and, as last time, I'm going to convert my CSV into an Excel file.

It is personalized - that's perfect!

Conclusion

The key takeaway here is that AI-powered cold email personalization doesn't have to be complicated or time-consuming. With the right tools like Scrap.io and AI assistants like ChatGPT, you can create highly personalized cold email campaigns that resonate with local businesses, leading to better response rates and more successful outreach campaigns.

For those interested in exploring this approach further, Scrap.io offers access to your first 100 leads free of charge, making it an excellent starting point for testing these cold email personalization techniques.

For more insights on building effective cold email strategies, check out our comprehensive guide on cold email outreach strategies and discover the best cold email tools that actually work in 2025.

Frequently Asked Questions

How can I use AI to personalize cold emails for local businesses effectively?

The most effective approach combines web scraping Google Maps data with AI-powered message creation. Start by collecting specific business information like company name, location, reviews, and contact details using tools like Scrap.io, then use ChatGPT to process this data and automatically generate personalized messages that reference their specific situation and location.

What are the best AI cold email personalization techniques?

The best techniques include using ChatGPT to process CSV data files, creating dynamic message templates that pull from multiple data points (name, business type, location, reviews), and automating the entire process from data collection to message generation. Advanced techniques involve using AI to extract specific metrics like five-star review counts from complex data structures.

How do I automate cold email personalization without losing authenticity?

Use a combination of data-driven personalization and AI-generated content. Collect real, specific data about each prospect using Google Maps scraping, then use AI to create natural-sounding messages that incorporate this information. The key is ensuring the personalization elements are accurate and relevant - that's what makes it feel authentic.

What tools are best for AI-powered cold email personalization?

Scrap.io is excellent for data collection from Google Maps, providing over 70 data points per business. For message creation, ChatGPT Premium can automate the personalization process by processing CSV files and generating customized content. For comprehensive outreach strategies, check our guide on cold email tools that actually work.

How can I automate the entire process from scraping to sending personalized emails?

Start with Scrap.io to extract business data from Google Maps, export to CSV, then use ChatGPT to create personalized message columns. The process can be streamlined: data collection → AI processing → message generation → email sending. This automation approach saves hours while maintaining high personalization quality.

How do I use Google Maps for automated lead generation?

Search for businesses in your target location and category using Scrap.io, then apply filters to get exactly the type of prospects you want (businesses with emails, specific review counts, etc.). The platform extracts comprehensive data including contact information, reviews, location details, and business characteristics automatically. Learn more about the technical aspects in our Google Maps scraping guide.

How can AI improve my cold email response rates?

AI can analyze and process large amounts of prospect data to create highly specific personalization that would be impossible to do manually. By referencing exact details like review counts, business locations, and company characteristics, AI-generated emails feel much more personal and relevant, leading to significantly higher response rates.

How do I avoid spam filters with AI-generated personalized emails?

AI-generated personalized emails have higher deliverability because spam filters prefer variety and authenticity. Avoid using the same templates repeatedly, ensure each message includes genuine personalization elements, maintain proper formatting, and target relevant prospects with valuable content. The key is that AI helps create unique messages for each recipient rather than mass-sending identical content.