ZoomInfo Web Scraping: How to Extract Company and People Data Safely

ZoomInfo scraping can be done using a wide range of intelligence tools, including pre-built ones or by creating your own scripts from scratch. In this guide, we will discuss ZoomInfo scraping, including how to do it, why you may need to do it, common issues, and much more. If you’re in need of a pre-built or custom-built web scraping solution, this is the guide you have been searching for. So, without any further ado, let’s dive right in!

Key Takeaways

• ZoomInfo provides access to rich company and people data useful for sales, marketing, and research. 
• Platforms providing detailed data about companies make research easier for businesses, researchers, and scraping service providers
• ZoomInfo scraping must follow legal and ethical rules, including ZoomInfo’s terms and privacy laws.
• A proper Python setup with tools like Selenium, httpx, and BeautifulSoup is required for effective scraping.
• ZoomInfo web pages are dynamic and often use embedded JSON, which makes scraping data easier.
• Scraping involves loading the page, locating elements or JSON, extracting information, cleaning it, and saving data.
• To avoid blocks, ZoomInfo scraping services use rotating proxies, add delays, mimic real browser behavior, and scale responsibly.

Why Scrape Data from ZoomInfo

The main reason to scrap data from this platform is to get access to large amounts of information about companies and their employees. Data that you can collect from this platform includes detailed company profiles, firmographic information, employee lists, job titles, direct contact details (from contact databases), revenue estimates, technology stacks, and intent signals.

Businesses can later use this data to shape their strategy for sales, marketing, recruitment, and a lot more. For B2B companies, having this data helps sales teams run more effective B2B campaigns and other sales processes. Overall, businesses extract data from ZoomInfo to build targeted leads lists, enrich CRM systems, study market trends, improve outreach, business intelligence, and several other niche use cases. 

Legal and Ethical Considerations

To avoid potential legal penalties, here are the key things to keep in mind when Web scraping ZoomInfo:  

• Terms of Service: ZoomInfo’s data is protected by strict terms of service. When such platforms provide their terms of use, ensure to read and follow them before implementing any scraping tasks. 
• Automated web scraping: It is important to note that automated ZoomInfo scraping may violate some of the terms and lead to IP bans or legal actions. So, it must be done with caution. 
• Regulations: Depending on the region you are targeting, you need to consider privacy laws like GDPR and CCPA that regulate personal data privacy. That’s why businesses need to handle contact data responsibly and avoid sensitive information in their hands from being exposed. 
• Copyright laws: Ethical web scraping requires respecting platform rules and data ownership. So, reselling data gotten from platforms like ZoomInfo may not be allowed. 
• Web Scraping alternatives: You may consider using official APIs or licensed access if you want to avoid the downsides that may come with data scraping. 

Project Setup

Before getting started with ZoomInfo scraping, you need to have a few things set up to avoid any hiccups along the way. First of all, you need to have a clean Python workspace (virtualenv) to keep dependencies isolated. 

Some of the key Python libraries you will need include Selenium (for JS-heavy pages), httpx (async HTTP client), and BeautifulSoup (for parsing). 

Setting Up the Environment

These are some of the procedures you can follow when setting up your environment:

• Python installation: Install Python 3.10 or later and create a virtual environment to keep dependencies separate.
• Libraries installation: Install core libraries using pip install playwright httpx beautifulsoup4: Playwright for browser automation, httpx for HTTP requests, and BeautifulSoup for parsing.  You should then install Playwright browsers using python -m playwright install. 

Prerequisites for Web Scraping ZoomInfo

The key details you need to access ZoomInfo and scrape data include:

• Data access: You need a ZoomInfo account with the proper permissions for the data you want to access before running your scripts. 
• Browser automation tool: You need a browser automation tool (Playwright or Selenium) to load dynamic content.
• Proxies: You may also need proxies such as rotating residential or datacenter proxies, to handle rate limits and reduce the chance of IP blocks that could interrupt your ZoomInfo scraping sessions. 
• Internet connection: You need to have a fast and stable internet connection and a session management method for storing cookies and handling authentication.
• Parsing tools: You also need parsing tools such as Parsel or BeautifulSoup to extract structured data from the page.
• Storage: Finally, you will need secure storage for any credentials or API keys, such as environment variables or a .env file.

Understanding ZoomInfo Data Structure

Before you start ZoomInfo scraping, it is important to understand the data structure of ZoomInfo, as it can help save time and reduce the resources needed. First of all, ZoomInfo pages are built with dynamic, JavaScript-driven content. Most company and people profiles load their information through embedded JSON blocks or structured HTML sections. 

Understanding how this data is organized helps businesses know exactly what to target when web scraping and the right scraping tools to use. For instance, company pages usually include a main overview section, contact information panels, firmographics, and technology sections. On the other hand, people’s profiles usually include personal details, job roles, and company associations. Knowing the location of each section allows you to extract the data you need more accurately.

What Data Can You Extract from ZoomInfo

Here is a comprehensive list of the the kinds of company data your data scraping tools extracts from this platform

• Company name
• Industry and sector
• Employee count
• Revenue estimates
• Headquarters address
• Phone numbers and emails (main business contacts)
• Website URL
• Company description and overview
• Technologies used by the company
• Key decision makers and their job titles
• Social media links
• Intent or buying signal data (when available)

Identifying Data Locations in HTML

Here are the steps you need to follow to locate target elements and access the data you need to scrape: 

• Open the profile page in your browser such as Google Chrome and launch Developer Tools (right-click → Inspect).
• Use the Elements panel to view the structure of the page and locate the sections that contain the data you want.
• Hover over elements to highlight where they appear on the page. This makes it easier to confirm you’ve found the right block.
• Use CSS selectors (e.g., .company-info__name) or XPath (e.g., //h1[@class=’title’]) to target specific elements during web scraping.
• Remember to check for embedded JSON inside <script> tags, as many ZoomInfo pages load structured data there. Scraping these script blocks is often more reliable than web scraping raw HTML.
• Copy a selector by right-clicking an element in DevTools and choosing Copy > Copy selector or Copy XPath.
• Test your selector in your automation tool (Playwright in this case) to make sure it returns the correct element before adding it to your script.

Writing the ZoomInfo Scraper

Here is a high-level workflow for scraping data from ZoomInfo: 

• Confirm legal access first: Before you begin, it is important to ensure that you have the right to collect data from this platform.
• Follow a clear step-by-step workflow: The key steps for web scraping are as follows; Render the page > Find where the data is > Extract it > Clean/validate it > Store it > Monitor for errors and fix them. Using this structure ensures that our web scraping tool is always organized and also makes it easy to troubleshoot if issues arise. 
• Use one browser automation tool for JavaScript-heavy pages: ZoomInfo loads most details with JavaScript. You can use automation tools like Playwright to load the full page just like a real browser. Pair these tools with a parsing library like BeautifulSoup or httpx for network requests for more efficiency.
• Build reliability into the scraping tool: Add logging to track what happens, retries for failed loads, and rate limiting so you don’t overload the server.

Web Scraping Company Information

Here is the procedure to follow when extracting company information from ZoomInfo: 

• Load the company profile page fully: Use a browser automation tool (Playwright
•  for our case) to open the page and wait for the http request to be complete. Here, your objective is to ensure that every section of the company profile is fully loaded.
• Check for embedded JSON data: Most of the modern pages store structured information inside <script> tags. This data often includes company details in a clean JSON format. If the data exists in JSON, this is the easiest and most stable data source to parse and save. 
• Use HTML elements when JSON isn’t available: In scenarios where you can’t find structured JSON, inspect the page to locate consistent HTML blocks. Playwright gives you the full rendered HTML. You can then use BeautifulSoup to extract specific fields by targeting CSS selectors or XPath. Elements like the company name, website link, industry, and address are usually inside predictable containers.
• Focus on the main company fields: When web scraping, you will typically extract details including the company name, website URL, physical address, industry or category, employee count, and revenue or size estimates. 
• Clean and Save the data: After extraction, clean the data to ensure it follows a consistent style and format before saving it.  
• Save your results with useful metadata: Store the data in CSV or JSONL format, and always record the page URL and the time of extraction. Including this metadata makes it easier to track and debug later.

Web Scraping People or Directory Data

• Load the directory page using browser automation: Since people and directory pages largely rely on Javascript, you will also need to use Playwriting to load the employee pages. When the page fully loads, you can scroll or interact with it the same way a human user would.
• Handling pagination or lazy-loading results: Most pages use pagination and lazy loading to load new data only when you move to the next page or scroll down. With Playwright, you can automate these actions to reveal more results. If the website uses a background API request to load new entries, you can inspect this through the browser’s Network tab. Once identified, you can fetch that data more efficiently using an HTTP library like httpx or requests, which avoids loading every page manually.
• Extract each person’s details with selectors and parsers: After the page loads the visible data, the next step is to pull out specific information you need for the different individuals. You do this by locating the HTML elements that contain names, job titles, emails (if shown), phone numbers, and company details. Playwright lets you target these elements using CSS selectors or XPath. You may also consider passing the page’s HTML to a parsing library like BeautifulSoup to extract and clean the data in a structured way.
• Reuse sessions and cookies to avoid repeated logins: Directory pages often require you to stay logged in. However, logging in repeatedly can slow down your scraping tool or trigger security checks. Playwright can automatically store the cookies needed to stay authenticated. So, if you later switch to faster HTTP requests for certain endpoints, you can reuse the same session cookies to avoid being logged out or blocked.
• Control scraping tool speed to avoid detection IP bans:  Automated web scraping can look suspicious if it sends too many requests too quickly. Use Playwright to implement timed delays. You can use HTTP libraries like httpx to space out your direct network requests when fetching additional data.

Example Code Snippet

from playwright.sync_api import sync_playwright
from bs4 import BeautifulSoup

def scrape_all_text():
    with sync_playwright() as pw:
        browser = pw.chromium.launch(headless=True)
        page = browser.new_page()

        page.goto("https://zoominfo.com/company_name")
        page.wait_for_load_state("networkidle")

        html = page.content()
        browser.close()

        soup = BeautifulSoup(html, "html.parser")
        all_text = soup.get_text(separator=" ", strip=True)

        return all_text

result = scrape_all_text()
print(result)

This code This code extracts all visible text from the entire webpage (https://zoominfo.com/company_name)

Handling Anti-Bot Protection

When web scraping platforms like ZoomInfo that have sensitive and high value data, you may sometimes encounter strict anti-bot systems. Let’s explore how you can deal with such restrictions:

Using Rotating Residential Proxies

By using rotating residential proxies, your requests are spread across different IP addresses, making it seem like your connections are coming from more than one user. This reduces the chances of IP bans since each IP can be configured to stay within ZoomInfo’s rate limit. Also, residential proxies use IPs from real home devices, making it harder for ZoomInfo to detect and block them since they don’t appear as proxies. 

Applying Stealth Browsers and CAPTCHA Solvers

Using stealth automation tools allows your browser to behave more like a real user by hiding indicators that reveal automated activity. These tools adjust features such as browser fingerprints, user-agent strings, and other subtle details that anti-bot systems look for. In scenarios where CAPTCHAs may appear to confirm that the visitor is human, using CAPTCHA-solving services can help your web scraping tool move past these challenges seamlessly.

Scaling and Optimizing Data Collection

For large web scraping projects, you will likely have to scrape data from hundreds or even thousands of pages. One of the ways to achieve this scalability is through running multiple browser instances or using asynchronous strategies for processing large datasets faster. To optimize, you can reuse browser sessions, handle pagination automatically, and rely on structured data endpoints when available.

Web Scraping Search Results with Pagination

Search pages and directories usually show results on more than one web page. To collect all the data for a given search query, your scraping tool must be configured to detect the “Next” button or pagination links and move through them one by one. 

Each time you load a new results page, repeat the extraction process and store the records. This step-by-step movement ensures you capture the full dataset rather than only the first page of the search results..

Recursive Crawling through Related Companies

Intelligence platforms like ZoomInfo usually provide internal links to partner companies, competitors, or related organizations. Your web scraping tools can follow these internal links to expand your dataset logically and gather additional profiles without manually having to search for each one of them.

This technique is called recursive crawling. Once it collects data from one profile, your web scraping tool can identify relevant links on the page and visit them as new targets. This creates a chain of connected pages that produces a much richer dataset while maintaining an organized structure.

Web Scraping ZoomInfo Without Getting Blocked

We already shared earlier that ZoomInfo and other similar platforms can block your connection if they detect unusual behavior when web scraping ZoomInfo. To avoid getting your connection blocked when scraping, we recommend using proxies with rotating IP addresses. 

When using rotating IP addresses, ZoomInfo will view these connections as though they are coming from different devices, allowing your tool to send many requests without triggering bot detectors. 

Other techniques you can use to avoid getting blocked include adding short random delays between page loads and using realistic browser headers that match normal traffic. You should also maintain consistent browser fingerprints and avoid any behavior that looks automated during scraping.

Complete scraping tool Code Example

from time import sleep
import random
import json
from pathlib import Path

from playwright.sync_api import sync_playwright
from bs4 import BeautifulSoup

START_URL = "https://zoominfo.com/company_name"  # replace with the exact URL you intend to scrap
NEXT_SELECTOR = "a.next"             # CSS selector for the site's "next" link
OUT_FILE = Path("playwright_results.jsonl")

def polite_sleep(min_s=0.8, max_s=1.8):
    sleep(random.uniform(min_s, max_s))

def extract_all_text(html: str) -> str:
    soup = BeautifulSoup(html, "html.parser")
    return soup.get_text(separator=" ", strip=True)

def save_record(record: dict):
    OUT_FILE.parent.mkdir(parents=True, exist_ok=True)
    with OUT_FILE.open("a", encoding="utf-8") as f:
        f.write(json.dumps(record, ensure_ascii=False) + "\n")

def run_simple_crawl(start_url: str, max_pages: int = 5, headless: bool = True):
    with sync_playwright() as pw:
        browser = pw.chromium.launch(headless=headless)
        page = browser.new_page(user_agent="EducationalScraper/1.0")
        url = start_url
        pages = 0

        try:
            while url and pages < max_pages:
                page.goto(url)
                # wait until network is idle so JS can finish
                page.wait_for_load_state("networkidle")
                polite_sleep()

                html = page.content()
                text = extract_all_text(html)
                save_record({"url": url, "text_preview": text[:1000], "_scraped_at": __import__("time").time()})

                pages += 1
                print(f"Saved page {pages}: {url}")

                # try to find and click a Next link (if present)
                next_el = page.query_selector(NEXT_SELECTOR)
                if next_el:
                    try:
                        next_el.click()
                        polite_sleep()
                        url = page.url
                        continue
                    except Exception:
                        break
                else:
                    break

        finally:
            browser.close()

if __name__ == "__main__":
    run_simple_crawl(START_URL)

The above Python script uses Playwright to render pages, BeautifulSoup to extract all visible text, follows a single “Next” link up to a page limit, and saves each page’s text preview to a JSONL file.

Troubleshooting and Common Errors

While web scraping ZoomInfo, you may encounter some errors. Let’s discuss the common ones and how to overcome them. 

• Timeouts: Pages with heavy JavaScript or slow networks may take longer to load. To minimize the possibility of timeout errors, increase your wait time and ensure your browser automation tool waits for all elements to appear.
• Missing Data: Sometimes data loads only after scrolling or clicking a tab. Make sure your scraping tool interacts with the page exactly as a human would and checks for structured JSON if available.
• Blocked Pages or CAPTCHAs: If you see unexpected blocks, slow down your requests, rotate IP addresses, and make sure your headers match normal browser traffic. CAPTCHA triggers often indicate your scraping tool is moving too fast or repeating actions unnaturally. Make changes to your ZoomInfo scraping tool configuration to minimize CAPTCHA triggers.
• Inconsistent Results: Dynamic content can change depending on location, login state, or session. Try reusing the same browser session and ensure cookies are stored properly.
• Navigation Failures: Pagination buttons or internal links may move or change. Remember to update your selectors regularly and inspect the page with DevTools when something stops working.

Final Thoughts

This article has explained in detail how you can scrape data from ZoomInfo, whether it is company information, employee details, or data about key figures in any industry. All the scraping tools shared throughout this guide, including Playwright, httpx, and BeautifulSoup, are open source and free to use. 
However, remember to scrape data ethically and follow ZoomInfo’s terms of service to avoid legal issues. If you run into problems such as rate limits or IP blocks during scraping, using residential proxies with rotating IPs can help you bypass these restrictions. ProxyWing provides web scraping proxies that give you access to millions of IPs across more than 190 countries.