Unleashing the Power of Data: Scraping Your Adobe Analytics Website to Find Your CID Code and Delimiter

January 16, 2026 · 5 min read
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Key takeaways

  • Teams inheriting an Adobe Analytics setup should first confirm how campaign tracking was implemented before assuming which CID code, parameter, or delimiter is being used.
  • Inspecting your own website’s source code or developer tools can help reveal campaign-tracking labels, query parameters, delimiters, and implementation clues.
  • CID and delimiter discovery should only be done on websites you own or have permission to inspect, because scraping unrelated domains creates ethical and trust issues.
  • Python tools such as Beautiful Soup can help extract suspected CID labels from page source, but the results must be validated against the actual Adobe Analytics setup.
  • This process is most useful as an Adobe campaign-tracking audit step, not as a replacement for documenting the approved parameter structure your team should use going forward.

As I meet with more and more customers, many of you are taking over another marketer and have no idea how your adobe analytics instance was created (or where to even go). At https://adobe.utmsmartmanager.com we’ve historically provided this service to our enterprise plan customers but we thought we would provide you with some guidance in case you ever want to try to find your adobe analytics ID code and delimiter yourself.

Adobe Analytics.com is a powerful tool that provides businesses with valuable insights into their website’s performance. To fully leverage its capabilities, it is crucial to find your Adobe Customer ID (CID) code and delimiter.

In this article, we will explore the process of scraping your Adobe Analytics website to extract these essential elements. By doing so, you can unlock the full potential of Adobe Analytics and gain deeper understanding and control over your data. Please note that you should only do this for your own website or with the permission of the domain owner. Any other use of scraping process is considered unethical by us.

  1. Understanding the CID Code and Delimiter: Before diving into the scraping process, let’s clarify the significance of the CID code and delimiter. The CID code is a unique identifier assigned to each Adobe Analytics user. It is vital for accurate data tracking and reporting. The delimiter, on the other hand, is the character used to separate different parameters within your Adobe Analytics URLs. By finding both, you can ensure seamless data extraction and analysis.
  2. Selecting a Scraping Tool: To scrape your Adobe Analytics website effectively, you need a reliable scraping tool. There are various options available, including Python-based libraries such as Beautiful Soup and Selenium. Choose a tool that aligns with your technical expertise and project requirements.
  3. Inspecting the Website’s Source Code: To find the CID code and delimiter, you need to inspect your website’s source code. Right-click on any webpage and select the “Inspect” or “Inspect Element” option (depending on your browser). This will open the browser’s Developer Tools, allowing you to explore the underlying HTML structure.
  4. Locating the CID Code: Within the Developer Tools, use the search function (usually denoted by a magnifying glass icon) to search for “CID” or “Customer ID.” The search results will highlight relevant sections of the source code. Look for a string of alphanumeric characters following the CID or Customer ID label. This string represents your unique CID code.
  5. Identifying the Delimiter: To find the delimiter, search for the Adobe Analytics URLs in the source code. These URLs typically contain multiple parameters separated by a delimiter. Look for a character that consistently appears between parameters. Common delimiters include “&,” “?,” or “#.” Identify the delimiter used in your Adobe Analytics URLs.
  6. Extracting the CID Code and Delimiter: With the CID code and delimiter identified, you can use your chosen scraping tool to extract them programmatically. Depending on the tool, you may need to write custom scripts or utilize predefined functions to scrape the necessary information. Extract the CID code and delimiter from the relevant HTML elements and save them for future use.
  7. Validating the Extracted Information: Once you have extracted the CID code and delimiter, it is crucial to validate their accuracy. Manually compare the extracted CID code with the one displayed in your Adobe Analytics account to ensure they match. Similarly, validate the delimiter by examining your Adobe Analytics URLs and confirming that it separates parameters correctly.

Scraping your Adobe Analytics website to find your CID code and delimiter opens the doors to comprehensive data analysis and actionable insights. By using a suitable scraping tool and inspecting the website’s source code, you can extract these crucial elements programmatically. Remember to validate the extracted information to ensure accuracy. Armed with your CID code and delimiter, you can unleash the power of Adobe Analytics, track data effectively, and make informed decisions to drive your business forward.

Here’s an example of how you can scrape your website’s source code using Python and the Beautiful Soup library to extract the Adobe Analytics CID code:

pythonCopy code

import requests
from bs4 import BeautifulSoup

# Specify the URL of your website
url = "https://www.example.com"
# Send a GET request to the website and retrieve the HTML content
response = requests.get(url)
html_content = response.text
# Create a BeautifulSoup object to parse the HTML content
soup = BeautifulSoup(html_content, "html.parser")
# Find the CID code within the source code
cid_element = soup.find(text="CID")  # Adjust the label if needed
cid_code = cid_element.find_next_sibling(text=True).strip()
# Print the CID code
print("Adobe Analytics CID Code:", cid_code)

In this example, you’ll need to have the Beautiful Soup library installed. You can install it using pip install beautifulsoup4. Replace the url variable with the URL of your website.

The script sends a GET request to the website, retrieves the HTML content, and creates a BeautifulSoup object for parsing. It then uses the find method to locate the element containing the CID label (you may need to adjust the label based on your website’s specific HTML structure). The find_next_sibling method retrieves the next sibling of the CID element, which should contain the CID code. Finally, the script prints the extracted CID code.

Please note that this code assumes the CID code is present in the source code of your website and is labeled as “CID.” You may need to modify the code accordingly if your website structure or label differs.