Tools: How to Generate Business Leads Using Google Maps, IDS, and Python

Introduction ## Extension Overview ## Why is Google Maps a Powerful Lead Source? ## What is Instant Data Scraper? ## Step 1: Install the Chrome Extension. ## Step 2: Open Google Maps. ## Step 3: Search for a Specific Niche and Location. ## Step 4: Scroll to Load More Results. ## Step 5: Activate Instant Data Scraper. ## Step 6: Review the Extracted Data. ## Step 7: Export the Data. ## Defining the "Lead Score" ## CONVERT YOUR CSV FILE SCORING USING AUTOMATION SCRIPT USING PYTHON ## Step 0: Environment Setup & Library Import ## Step 1: Intelligent Data Loading ## Step 2: Executing the Scoring Algorithm ## Step 3: Exporting the "Hit List" ## The Outreach Phase "Result" ## The Final Result ## Reference This article was co-authored by @brenth_daryllmozo_ad9f09 Lead generation is one of the most important skills in freelancing, digital marketing, and entrepreneurship. Many beginners assume they need expensive software or paid databases to find potential clients, but that’s not always true. With the right approach, you can generate targeted business leads completely free using tools that are already available to everyone. In this guide, I’ll walk you through how to use Google Maps together with a Chrome extension called Instant Data Scraper to extract business information and turn it into actionable leads. This method is especially useful for freelancers offering services like web development, SEO, social media management, automation, or digital marketing. It’s also a great starting point for students and aspiring agency owners who want to practice outreach without investing in premium lead generation tools. The idea behind this method is simple. Businesses list their information publicly on Google Maps, including their name, address, phone number, website, reviews, and ratings. Instead of manually copying this information one by one, we use a browser extension called Instant Data Scraper to extract the visible data into a downloadable file. From there, we clean the data, identify opportunities, and use it for outreach. This entire workflow requires no coding knowledge and no paid subscriptions. You’re essentially turning Google Maps into a free business directory and using automation to speed up the process. Google Maps is more than just a navigation tool. It is one of the largest public databases of local businesses in the world. Companies voluntarily provide their contact information so customers can find them easily. Because of this, Google Maps contains real, active businesses that are already operating in specific cities and industries. When you search for a niche like “Dental Clinic in Manila” or “Coffee Shop in Cebu,” you immediately get access to dozens or even hundreds of potential leads. The information is updated regularly, which makes it more reliable than many outdated business directories. This makes it an ideal place to start when building local or niche-specific prospect lists. Instant Data Scraper is a free Chrome extension that automatically detects structured data on a webpage and allows you to export it as a CSV or Excel file. It works by analyzing page elements and identifying repeating patterns such as business listings. The tool is beginner-friendly because it does not require programming knowledge or technical setup. Once installed, it can extract visible information directly from your browser. This makes it perfect for scraping business listings from Google Maps search results without manually copying each entry. Open your Google Chrome browser and go to the Chrome Web Store. In the search bar, type Instant Data Scraper and select the correct extension from the results. Click “Add to Chrome” and confirm the installation. Once installed, you should see the extension icon in your browser toolbar. After installing the extension, go to Google Maps in your browser. Make sure you are using the web version, not the mobile app, since the extension works directly inside the browser. In the Google Maps search bar, type a business category along with a city or area. For example, you can search for “Dental Clinic in Manila” or “Coffee Shop in Cebu.” Be specific with your keywords to ensure your leads are targeted and relevant to your service. Once the search results appear on the left-hand panel, scroll down slowly. Google Maps loads more listings as you scroll, so continue scrolling until no new businesses appear. This ensures you capture as many leads as possible. Click the Instant Data Scraper icon in your Chrome toolbar. The tool will automatically analyze the page and attempt to detect structured data from the business listings. Wait a few seconds while it identifies the available information. A preview window will appear showing the detected fields, such as business names, ratings, addresses, or other visible details. Carefully review the preview to make sure the data looks accurate and properly structured before exporting. Once you are satisfied with the extracted information, click the “Export CSV” button. The file will download to your computer, allowing you to open it in Excel or Google Sheets for cleaning and organization. Categorize: High-Value Web Dev Lead (+50): Absence of a website indicates a critical need for a digital storefront. This is the primary trigger for outreach. Proven Active Business (+25): A review count > 30 confirms the business is operational and has a consistent customer flow, making them more likely to have a marketing budget. Reputation Management Opportunity (+25): A rating of 4.1 or below highlights a "reputation gap." This provides an entry point for offering automated review-generation systems or customer feedback loops. Manual filtering in Excel is slow and prone to human error. The Python script simplifies the process through three steps: Before processing, the script prepares the workspace by importing Pandas and NumPy. These are the industry-standard tools for data science. The script reads the CSV file exported from the scraper. It doesn't just "open" it; it prepares it for analysis by identifying column names (like name,ASWgTc,website, rating, or review_count). This is the "Brain" of your automation. The script runs a function called calculate_score on every single row simultaneously. The Weights: +50 pts if the Website is missing (High-value web dev lead). +25 pts if Reviews are > 30 (Proven active business). +25 pts if Rating is 4.1 (Reputation gap). The final step saves the cleaned, ranked, and filtered data into a brand-new CSV file: leads.csv. This simple method ensures you save time scrolling and more time talking to the high-value clients who are practically being curioused and wanting to explore or to engage with services you want to offer. The Strategy: Instead of saying "Hire me to build a website," you say "I noticed your door is locked." By highlighting a missing link or a bad rating, you are providing a mini-consultation for free. By following this workflow, you’ve transformed a boring static map into a high-potential database of opportunities as a freelancer. Before: You were "looking for work" and hoping someone would notice you. After: You are an expert identifying specific business lapses and positioning yourself as the bridge to fix them. This systematic approach turns cold outreach into a warm, problem-solving conversation. You aren't just a freelancer anymore; you're a strategic partner. https://chromewebstore.google.com/detail/instant-data-scraper/ofaokhiedipichpaobibbnahnkdoiiah?pli=1 Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse COMMAND_BLOCK: import pandas as pd import numpy as np def run_lead_scorer(input_file, output_file): try: # 1. Load the dataset df = pd.read_csv(input_file) # 2. Define the scoring logic def calculate_score(row): score = 0 # CRITERIA 1: Website Presence (50 pts) # Most scrapers name this column 'website' or 'Website' web_col = 'website' if 'website' in df.columns else 'Website' if pd.isna(row.get(web_col)) or str(row.get(web_col)).strip() == "": score += 50 # CRITERIA 2: Review Count / Activity (25 pts) # Targeting active businesses (30+ reviews) rev_col = 'review_count' if 'review_count' in df.columns else 'Review Count' reviews = row.get(rev_col, 0) try: if float(reviews) >= 30: score += 25 except: pass # Handle cases where review count isn't a number # CRITERIA 3: Rating / Reputation Gap (25 pts) # Targeting businesses with room for improvement (< 4.1 stars) rate_col = 'rating' if 'rating' in df.columns else 'Rating' rating = row.get(rate_col, 0) try: if 0 < float(rating) <= 4.1: score += 25 except: pass return score # 3. Apply Scoring df['Lead_Score'] = df.apply(calculate_score, axis=1) # 4. Filter and Sort (High-Value only) # We only keep leads that scored 75 or 100 high_priority_df = df[df['Lead_Score'] >= 75].sort_values(by='Lead_Score', ascending=False) # 5. Export results high_priority_df.to_csv(output_file, index=False) print(f"✅ Success!") print(f"Total leads analyzed: {len(df)}") print(f"High-value prospects found: {len(high_priority_df)}") print(f"File saved as: {output_file}") except FileNotFoundError: print("❌ Error: 'leads.csv' not found. Please check your filename.") except Exception as e: print(f"❌ An error occurred: {e}") # Run the script if __name__ == "__main__": run_lead_scorer('leads.csv', 'high_priority_prospects.csv') Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: import pandas as pd import numpy as np def run_lead_scorer(input_file, output_file): try: # 1. Load the dataset df = pd.read_csv(input_file) # 2. Define the scoring logic def calculate_score(row): score = 0 # CRITERIA 1: Website Presence (50 pts) # Most scrapers name this column 'website' or 'Website' web_col = 'website' if 'website' in df.columns else 'Website' if pd.isna(row.get(web_col)) or str(row.get(web_col)).strip() == "": score += 50 # CRITERIA 2: Review Count / Activity (25 pts) # Targeting active businesses (30+ reviews) rev_col = 'review_count' if 'review_count' in df.columns else 'Review Count' reviews = row.get(rev_col, 0) try: if float(reviews) >= 30: score += 25 except: pass # Handle cases where review count isn't a number # CRITERIA 3: Rating / Reputation Gap (25 pts) # Targeting businesses with room for improvement (< 4.1 stars) rate_col = 'rating' if 'rating' in df.columns else 'Rating' rating = row.get(rate_col, 0) try: if 0 < float(rating) <= 4.1: score += 25 except: pass return score # 3. Apply Scoring df['Lead_Score'] = df.apply(calculate_score, axis=1) # 4. Filter and Sort (High-Value only) # We only keep leads that scored 75 or 100 high_priority_df = df[df['Lead_Score'] >= 75].sort_values(by='Lead_Score', ascending=False) # 5. Export results high_priority_df.to_csv(output_file, index=False) print(f"✅ Success!") print(f"Total leads analyzed: {len(df)}") print(f"High-value prospects found: {len(high_priority_df)}") print(f"File saved as: {output_file}") except FileNotFoundError: print("❌ Error: 'leads.csv' not found. Please check your filename.") except Exception as e: print(f"❌ An error occurred: {e}") # Run the script if __name__ == "__main__": run_lead_scorer('leads.csv', 'high_priority_prospects.csv') COMMAND_BLOCK: import pandas as pd import numpy as np def run_lead_scorer(input_file, output_file): try: # 1. Load the dataset df = pd.read_csv(input_file) # 2. Define the scoring logic def calculate_score(row): score = 0 # CRITERIA 1: Website Presence (50 pts) # Most scrapers name this column 'website' or 'Website' web_col = 'website' if 'website' in df.columns else 'Website' if pd.isna(row.get(web_col)) or str(row.get(web_col)).strip() == "": score += 50 # CRITERIA 2: Review Count / Activity (25 pts) # Targeting active businesses (30+ reviews) rev_col = 'review_count' if 'review_count' in df.columns else 'Review Count' reviews = row.get(rev_col, 0) try: if float(reviews) >= 30: score += 25 except: pass # Handle cases where review count isn't a number # CRITERIA 3: Rating / Reputation Gap (25 pts) # Targeting businesses with room for improvement (< 4.1 stars) rate_col = 'rating' if 'rating' in df.columns else 'Rating' rating = row.get(rate_col, 0) try: if 0 < float(rating) <= 4.1: score += 25 except: pass return score # 3. Apply Scoring df['Lead_Score'] = df.apply(calculate_score, axis=1) # 4. Filter and Sort (High-Value only) # We only keep leads that scored 75 or 100 high_priority_df = df[df['Lead_Score'] >= 75].sort_values(by='Lead_Score', ascending=False) # 5. Export results high_priority_df.to_csv(output_file, index=False) print(f"✅ Success!") print(f"Total leads analyzed: {len(df)}") print(f"High-value prospects found: {len(high_priority_df)}") print(f"File saved as: {output_file}") except FileNotFoundError: print("❌ Error: 'leads.csv' not found. Please check your filename.") except Exception as e: print(f"❌ An error occurred: {e}") # Run the script if __name__ == "__main__": run_lead_scorer('leads.csv', 'high_priority_prospects.csv')