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Conversion Tracking in a Privacy-First World

Digital marketers have relied on third-party cookies for years to track user behaviour, personalise ads, and measure campaign effectiveness. But not anymore! Popular search engines like Google and Bing plan to phase out cookies due to growing privacy concerns. Recent surveys show that 65% of respondents find excessive cookie usage a growing privacy concern. This shift necessitates adopting alternative attribution methods for conversion tracking that don’t rely on cookies. 

To help you with this, we will discuss cookie-less conversion tracking methods and their pros and cons. So, let’s begin!

Conversion tracking without Cookies

Challenges of Cookie-Less Conversion Tracking

The shift towards cookie-less conversion tracking presents multiple challenges for marketers and businesses that rely on detailed user data to shape their strategies. Some of these challenges include:

Difficulty in Tracking User Behaviour

Firstly, the cookie-less approach makes it difficult to track user behaviour across different websites and devices. You don’t know if the customer purchased via laptop or mobile phone and what the user’s first contact point with your offerings. They might have reached your campaign through a laptop and then used a mobile to make the purchase. This lack of awareness complicates your ability to gain a holistic view of the customer’s journey. 

Poor Marketing Strategies 

Marketers heavily rely on cookies to shape their marketing strategies. Cookie-less conversion tracking means they don’t have enough data to segment audiences, personalise content, and target ads more precisely. This makes it difficult for businesses to find out which aspects of the campaign are working and which aren’t, reducing the effectiveness of their marketing strategies. 

Alternative Attribution Methods that Don’t Rely on Cookies

As cookie-less conversion tracking is gaining traction, there are several alternative attribution methods you can use to keep an eye on user behaviour:

Browser Fingerprinting

Browser fingerprinting is a subset of device fingerprinting that efficiently and accurately identifies users on a website. Compared to traditional cookies, this method works by collecting data from a user’s browser and then creating a unique ID for that individual. This ID or fingerprint remains consistent across all touchpoints and cannot be blocked by ad blockers, making it easy to collect user information. 


  • Tracks user data across different platforms to help you better understand the full customer journey, even when users switch between their smartphones, tablets, and computers.
  • Helps identify fraudulent or suspicious activities based on unusual or inconsistent fingerprints preventing fraudulent transactions and account takeovers. 


  • Collecting information about user devices may not be considered ethical.
  • Device fingerprints may change over time due to hardware changes or device updates, leading to data inaccuracies. 


Server-Side Tracking 

Server-side tracking refers to when one cloud-based central system or server collects user data rather than the customer’s browser. In this, you load the tracking scripts from your domain name and send them to your server first, creating an intermediate layer. The server then processes the data, which you can forward to the analytics platform. This method eliminates your reliance on third-party cookies for data collection. 


  • Data is less susceptible to being blocked or deleted by the user’s browser settings and ad blocks.
  • Enhanced privacy for user data as it’s compliant with privacy regulations such as GDPR.
  • Consistent and reliable data collection across different browsers and devices.


  • It requires significant investment in server infrastructure and ongoing maintenance, which can be costly.
  • Potential server overload, especially if the volume of data being tracked is high. 


Probabilistic Tracking 

Probabilistic tracking is an alternative attribution or cookie-less conversion tracking method that relies on statistical analysis, models, and machine learning to track user behaviour. It doesn’t rely on deterministic identifiers like cookies or device IDs. Instead, this technique works by analysing user behaviour and comparing it with existing data to get an idea of which touchpoint played a role in conversion. 


  • Helps identify users across multiple devices by analysing patterns and behaviours that suggest the same user is active on other devices.
  • Offers a quick top-down assessment of which marketing campaigns are yielding results. 


  • Relies on statistical models and assumed data, which can be precise, leading to inaccurate targeting.


Best Practices to Implement Cookie-Less Conversion Tracking

The following are some best practices you should follow to implement cookie-less conversion tracking in your organisation:

  • Choose Your Methods: To implement conversion tracking without cookies, you should first find an alternative attribution method. Common options include Server-Side Tracking, Probabilistic Tracking, and Browser Fingerprinting. Evaluate the pros and cons of each and pick the most suitable one. 
  • Educate Your Teams About New Methods: Make sure your IT and marketing teams are well-informed about the new methods. Provide them with training sessions and resources to help them understand how these techniques work. This will help them quickly adapt to the changing marketing world. 

Frequently Asked Questions


How can marketers adapt to the loss of third-party cookies?

Marketers can quickly adapt to the loss of third-party cookies by investing in alternative data collection methods. These include side-server tracking, probabilistic tracking, and browser fingerprinting. 

What role does user consent play in cookie-less tracking?

User consent plays a crucial role in cookie-less tracking as businesses consider users their top priority. Therefore, they opt for tracking methods compliant with privacy regulations like GDPR. 

Are there any risks associated with probabilistic attribution modelling?

Yes, probabilistic attribution carries risks such as low accuracy and misidentification of users. This leads to incorrect conclusions about user behaviour and reduces the effectiveness of your campaigns. 

How can machine learning algorithms improve conversion attribution?

Machine learning can easily improve your conversion attribution by analysing complex user interactions across different platforms. It identifies patterns and finds out the impact of each channel on conversion, allowing you to shape your marketing campaigns accordingly. 


Cookie-less conversion tracking is needed in modern marketing, considering that top platforms are abandoning cookies. If you need help to take your marketing efforts to the next level, 3 Phase Marketing is the best partner. 

We offer amazing digital marketing services and make it easy for you to implement cookie-less tracking across all platforms. Discover our success stories to know how we have done it for other top brands and how we can replicate the same for you. Contact us today


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