The future of data-driven attribution models and how TAGGRS can help with it


Tracking data and conversions is becoming increasingly difficult. It is a New reality that companies and marketers worldwide are grappling with:

  • Not all conversions are measured.
  • Phasing out third-party cookies
  • Orders do not match Analytics data.
  • Less data in the campaigns that the algorithm can learn from.

This mostly has to do with new regulations of third-party cookies and tracking prevention mechasnismes such as Safari’s. As a result, companies are moving to increasingly anvaceous tracking techniques.

One problem with this is that increasingly sophisticated data collection means that small companies can no longer keep up with large companies. In this blog, we’ll take you through the evolution of data-driven attribution models and how TAGGRS can help.

Key Points 🔑

  1. Future-proof attribution models: With the phase-out of third-party cookies and stricter privacy regulations, conversion tracking is becoming more complex.
  2. Attribution model challenges due to privacy regulations: Contemporary attribution models face several challenges due to stricter privacy regulations and the phasing out of third-party cookies. These developments limit the availability of detailed user data, making it more difficult for companies to accurately measure and attribute the effectiveness of their marketing campaigns to the right channels and touchpoints.
  3. Future-proof marketing strategies: By implementing Server Side Tracking, companies can anticipate future technological and regulatory changes while maintaining and improving their ability to make data-driven decisions.

How does an ad platform determine that an exposure to an ad resulted in a conversion?

The process involves requiring a specific piece of information to establish a link between seeing an ad and a conversion. Therefore, the advertiser must forward a distinctive data, such as an email address, phone number, IP address or other characteristic that identifies the buyer or the person who converted to platforms such as Google or Facebook. These platforms can then make the connection, since chances are that the person who saw the ad and took action was logged into their systems

What is Data-Driven Attribution?

The customer journey from first interaction to conversion can vary significantly from company to company and user to user.

At one end of the spectrum are users who perform a search, see your business for the first time, click on your ad and make an immediate purchase. This user has only one contact moment before purchase.

At the other end of the spectrum are users who interact with your business for months, interacting with display ads, organic and paid listings from Google, emails and other touch points before finally making their purchase. These users go through many touch points before they proceed to purchase.

From an attribution perspective, the first situation is quite simple. There is only one contact moment that gets the credits. However, when you introduce multiple touch points, it becomes much more difficult to say with any reasonable certainty which touch points actually influenced the final purchase and to what extent.


Data-driven attribution within Google Ads

Data-driven attribution within Google Ads is an advanced functionality that allows you to assign conversions based on users’ interaction with your ads. This method analyzes the entire dataset of your ad account to identify which keywords, ads and campaigns have the greatest impact on your results. It takes a look at all interactions and engagement within your paid ad history to gain deeper insight into the effectiveness of your ad campaigns in driving conversions and sales.


Your data-driven model is unique and tailored to your specific situation, enabling you to:

  • To discover which keywords, ads, ad groups and campaigns generate the most conversions.
  • Optimize your bids based on this performance data.
  • Choosing the right attribution model for your business.

With data-driven attribution in your paid reporting, you gain insight into which ads contribute the most to achieving your business goals. Moreover, if you are using an automated bidding strategy to get more conversions, your bidding will leverage this crucial information to help you get more conversions. This approach allows you to determine with greater precision how your ad spend contributes to the bottom line, making your marketing strategy more efficient and effective.

Data-Driven Attribution is Google’s proposed solution to this uncertainty

Data-Driven Attribution assigns value to ads by modeling which ads increased the likelihood of a user converting. It compares the trajectories of users who converted with those who did not, in order to assess the added value of different touch points.

For example, it can compare a group of users who clicked on a shopping ad with those who clicked on both a shopping ad and a complementary remarketing ad to understand the additional benefit of that ad. This allows the system to better assess the added value of different points of contact.

Statistics on the adoption and use of data-driven attribution (DDA):

And it’s not just Google that thinks data-driven attribution (DDA) is the solution.

  • 60% of leading marketers believe DDA is essential to understanding high-value customer journeys.
  • 50% of marketers say DDA has helped them improve their marketing ROI.
  • 70% of marketers say DDA is more accurate than traditional attribution models.
  • The average marketer spends 15 hours a month administering their marketing attribution data.
  • The average marketer spends $10,000 a year on marketing attribution software.
  • These statistics suggest that DDA is a growing trend in the marketing industry and is a valuable tool for marketers looking to improve their ROI.

Examples of how marketers use DDA:

  • To understand the impact of different marketing channels on conversions.
  • To identify the most effective marketing campaigns.
  • To optimize their marketing budgets.
  • To personalize their marketing messages for individual customers.

Conversion Modeling.

Following the Consent Mode V2 presented earlier, Google is coming up with Conversion Modeling, which is focused on Google Ads. This tool has the ability to predict the likelihood of conversion from an ad in Google, fueled primarily by anonymized data.

After correctly setting up Consent Mode V2, much data is no longer collected, Google supplements this with predicted data

The tool compares this data with data from visitors who did give permission, giving a complete picture of behavior on your website. This way, the data in reporting is more accurate and certain follow-up actions can be better substantiated. Google Ads itself implements Conversion Modeling in accounts, which requires marketers – unlike Google Consent Mode – not to modify anything in the website source code.


More than the eye can see

Before data-driven attribution took hold, Google Ads based the reporting of conversions solely on the last click. This meant that when a user clicked on a Google ad and then completed a conversion action (such as a lead or purchase) on the website, Google attributed the conversion to that specific last click.

As of September 2023, data-driven attribution became the standard for all newly defined conversion actions within Google Ads, moving a significant number of existing advertisers to this model. This means that instead of attributing conversion only to the last click, Google is now using machine learning to assign conversions to both clicks and impressions that occur throughout the customer’s journey to conversion.

The introduction of this model marks an important shift in evaluating the impact of ads on conversions. It allows advertisers to gain a comprehensive picture of the contribution of each individual interaction – from initial introduction to the final conversion act – to achieving success. This approach allows marketers to fine-tune their campaign strategies more precisely, no longer limited to the last point of contact but with an appreciation for the integral value of the entire customer journey. These insights are crucial for refining campaigns and allocating marketing budgets more efficiently, aimed at improving the overall performance and return on investment of ad spend.

When is it no longer accurate enough?

Digital marketers have seen how all major ad platforms have gradually introduced data-driven attribution into their conversion measurement over the past few years. The driver for this was the preservation of performance tracking, which is very important for platforms to demonstrate their value when traditional tracking methods are lost due to Apples ATT framework and the phase-out of third-party cookies.

But there is a fundamental problem with data-driven attribution….

When cookie-based conversion tracking began to face limitations such as the phasing out of third-party cookies, companies implemented server side tracking with technical and financial resources to maintain their ability to accurately measure performance. Companies without internal technical resources or the finances to pay for analytics and data specialists are left without the ability to make technical tracking changes.

The Privacy Sandbox Timeline for the Web.

Advertising platforms historically provided fairly accurate conversion data

Both Google and Meta knew that a large number of SMEs would not have the resources to set up advanced conversion tracking. That’s why they started using modeled data to estimate lost, or untracked, conversions. A measure of modeling was historically used to estimate cross-device conversions and conversion loss due to blocked scripts and tracking/adblockers. This was quite accurate because almost 90% of the conversions were tracked accurately, and the models only had to bridge the gap for the missing 10%.

Attribution modeling now relies on smaller data sets

The following piece goes pretty deep into technical statistics, but it’s important for marketers to understand. The work of a Guinness brewer named William S. Gosset inspired the concept of statistical significance when he discovered the t-distribution curves.

In simple terms, when statistics are applied to large data sets, the results are more reliable and the expected (modeled) results are close to the actual numbers. In the graph below, this is represented by the blue Z-distribution curve, which indicates results that do not deviate significantly. However, when statistical models are built on a very small data set, they are less reliable, and the actual numbers may differ greatly from the modeled data – as seen in the t-distribution curve with a small sample size.

concept of statistical significance

Real-world examples of this phenomenon are familiar to us all, where advertisers see a sale reported, but it did not take place. This may be because of algorithms that model conversions and predict, based on data and the circumstances in which a click occurred, that a conversion should have occurred.

When actual tracked conversion volumes decrease, due to the aforementioned changes in privacy management and third-party cookies, more conversion reporting will be based on modeling non-tracked data. The reliability of that information will be drastically reduced.

How to improve the accuracy of performance data

To optimize campaign performance, you need to reduce platforms’ reliance on data-driven attribution models and provide them with more reliable data. The best way to do this is to combine Server Side Event Tracking with advanced audience matching such as Enhanced Conversions. This is a problem for small businesses because this is a fairly sophisticated method of data collection used primarily by large companies.


Is there then a solution for these small parties

The data landscape is becoming increasingly complex. So how do you make sure that as a smaller party you can still set up a server side solution. TAGGRS is focused on realizing a future where the most complete tracking solutions are accessible to all. An integral part of this is Server Side Tracking, which sets a new standard in data analytics and user privacy.

With Server Side Tracking:

  • Do you have the solution to the disappearance of third-party cookies
  • More data for your campaigns which makes for less predictive conversions.
  • GDPR proof online data collection

Tip: Also check out our Server Side Tracking FAQ with the 50 most frequently asked questions about Server Side tracking.

Why is TAGGRS accessible?

With our easy GTM Server Side Tracking Software you will be helped step by step through every step you need to take for a proper Server Side Tracking setup, we offer, among other things:

  • Configuration blogs
  • Videos.
  • Templates
  • Premium support
  • Support platform
  • Instant chat support

Make sure all conversions are measured

In the rapidly changing world of data-driven attribution models, a focus on data quality remains essential. With advanced Server Side Tracking solutions, companies can ensure data accuracy despite the limitations of traditional tracking methods. This approach transforms contemporary challenges into future successes by leaning on precision and deep data analysis, with TAGGRS serving as an example of how technology can assist. Convinced? schedule a free demo.

About the author

Ate Keurentjes

Ate Keurentjes

Server Side Tracking Specialist at TAGGRS

Ate Keurentjes is a Server Side Tracking specialist at TAGGRS. He has experience with various Google Tag Manager concepts. Keurentjes has been editing and writing about the latest developments and trends in data collection / Server side tracking since 2023.

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