Data-Driven Attribution - Best Marketing Attribution Model 2023

Data-driven attribution (DDA) is not a buzzword but an effective, proven attribution model that is already widely practiced. While marketers struggle to demonstrate the value of their marketing efforts, making mistakes and depleting marketing budgets, DDA keeps you on track and eliminates mistakes. 

So what is data-driven attribution, and how can you make it work for your business?

Catch up with our article about marketing attribution models, types, differences, and how to use them.

What is Data-driven Attribution (DDA)?

How do attribution models distribute credit for conversion?

Unlike the rule-based attribution models (single-touch and multi-touch) discussed previously, data-driven attribution does what the name actually refers to: it takes your data, analyzes it, and creates a custom model. It doesn’t use any of the pre-defined models and doesn't give credit to fixed touchpoints. 
 

In 2021, marketers were all thrilled by Google’s announcement about shifting to a data-driven attribution model. 

DDA uses machine learning to analyze and map engagement across all touchpoints, adding value to each interaction while respecting the user’s privacy. Also called an "algorithmic model," this is, so far, the best model to analyze purchase journeys without any subjectivity. Leveraging machine-learning technologies, data-driven attribution also gives credit to those interactions that a customer has before conversion. And the more you use it, the more intelligent it gets. In fact, ML can help you evaluate non-conversion paths that can be used to optimize marketing campaigns further. 

60% of marketers rely on data-driven attribution to understand the journey of high-value customers.

In DDA, credit is given to the touchpoint that was crucial to the customer’s journey, thus giving you a comprehensive map of journeys and knowledge of the productivity of ads, campaigns, and keywords. 

How Does Data-Driven Attribution Work?

At its core, data-driven attribution aims to assign value to each touchpoint along the customer's journey based on actual data, rather than relying on predefined rules or last-click attribution models. This approach considers multiple touchpoints that customers have before making a purchase, and each interaction plays a role in influencing their decision.

Data collection and integration

The foundation of data-driven attribution is robust data, i.e., user interactions across various channels, including website visits, clicks on ads, email opens, social media engagements, and more. This data is collected and used for analysis.

Machine learning algorithms

Machine learning algorithms lie at the heart of data-driven attribution, which collects data to identify patterns and relationships between different touchpoints and conversions. These algorithms consider factors such as the timing, sequence, and frequency of interactions to determine the contribution of each touchpoint to the final conversion.

Conversion paths and weighting

DDA models map out the various paths buyers take before converting. These paths can be quite complex, involving multiple channels and touchpoints. The process of assigning appropriate credit involves mathematical weighting to each touchpoint based on its influence on the conversion. The weights are also determined by the machine learning algorithms and can change dynamically based on real-time data analysis.

Customization and experimentation

While machine learning collects data and gives scores to touchpoints, humans are responsible for choosing the attribution model that best fits their goals and business model.

What are the Factors Determining the Best Marketing Attribution Model?

Advanced marketing attribution models like multi-touch or data-driven attribution are widely used to process customer data across channels. To achieve data granularity and get a more holistic view of data, marketing teams use advanced analytics platforms that help them focus on multiple factors. 

Target channel: if you are running a single marketing channel, a multi-touch or DDA model is not suitable (in this case, a last-touch attribution model will be enough). While those marketing campaigns running simultaneously on different channels will need something more than a multi-touch attribution model.

Sales cycle length: customer journeys become more complicated, and consequently, sales cycles become longer. To be clear, the average online journey (to convert a lead) can now range from 20 to 500 touchpoints. A DDA will work throughout the cycle length, with dozens of touchpoints. 

The number of touchpoints: The sophistication of an attribution model is directly proportional to the number of touchpoints: one touchpoint — single-touch attribution model; multiple touchpoints—DDA.

Primary objectives: In single-touch attribution models, there can be a single objective. A DDA will track the entire marketing funnel.

On September 8, 2022, Google changed its data-driven attribution model, improving its accuracy for low conversion volume or short data history. 

What does Shifting to Data-driven Attribution Mean for your Business?

Is it worth choosing a data-driven attribution model?

Data-driven attribution is not a magic wand; you still have tons of work to do with data. The key point of data-driven attribution in Google Ads is having better insights for marketers. You decide how to use this data.

Partial conversions: Here is an example of the DDA model. If conversion involved two ad interactions, the attribution model would give credit not smaller than 1. Unlike other marketing attribution models, DDA may allocate the credit as 0.25 and 0.75 or 0.5 and 0.5 instead of giving credit to one interaction as a whole. 

Google only: The biggest mystification revealed…

Before moving forward, let’s answer the most critical question one may definitely have when starting with DDA. Is the DDA model a Google tool? Search the web, and you will get a definite answer with clear documentation about the new data-driven attribution model launched by Google. It’s hard to overcome Google’s monopoly and find the answer. No, the data-driven attribution model is not Google only, and it goes beyond the marketing efforts of Google's buying platform. So what’s the trick? Simple as that, Google created a tool for a data-driven attribution model and called it the “Data-Driven Attribution Model,” confusing those who are just starting with DDA. 

Third-party platforms using DDA models implement different algorithms to analyze data and attribute credit, but the whole logic is identical. In terms of integration with Google, many attribution modeling tools can integrate with Google Analytics, which allows businesses to use data from Google Analytics for their attribution model. This allows businesses to take advantage of Google's extensive data collection and analysis capabilities while still using a different attribution modeling tool. Additionally, many platforms can integrate with other data sources, such as CRM systems, ad platforms, and e-commerce platforms, to give a more holistic view of the customer journey.
 

Here are three platforms that have DDA and can perform independently:

• Visual IQ: This is a cross-channel marketing attribution platform that uses machine learning algorithms to analyze data from multiple channels and touchpoints in the customer journey. It allows businesses to attribute value to different channels and optimize their marketing strategy.
• Adobe Analytics: This is a web analytics tool that allows businesses to collect, analyze, and report on web data. It can be integrated with other Adobe products, such as Adobe Experience Cloud. It also has a feature called "Attribution IQ," which allows businesses to perform data-driven attribution analysis.
• Singular: It's a mobile marketing attribution platform that provides data-driven attribution for mobile apps. It uses machine learning algorithms to analyze data from different touchpoints in the customer journey, such as ad clicks, in-app events, and post-install events. It also allows integration with different platforms like Google Ads, Facebook, and Twitter to get a holistic view of the customer journey across different channels.
• DoubleClick: This is a suite of digital advertising tools offered by Google that includes a data-driven attribution feature called "DoubleClick Attribution." It allows businesses to analyze data from multiple touchpoints in the customer journey and attribute value to different marketing channels.
• Kochava: This mobile attribution and analytics platform allows businesses to track and attribute conversions across multiple channels and devices. It uses a data-driven approach to attribution and allows integration with various platforms and ad networks.

Other platforms, like Outbrain or Taboola with its “Taboola Attribution” model, or, Revcontent, don’t have a built-in data-driven attribution model. Outbrain does offer tracking and measurement features that allow businesses to track conversions and attribute value to different campaigns. Outbrain’s platform allows integration with third-party analytics, like Google Analytics, Adobe, etc., to track conversions and attribute value to Outbrain campaigns.

Discover your high-value customers with data-driven attribution. Trust your marketing efforts to a skilled marketing team already implementing the DDA model.

Facebook also uses a data-driven attribution model that works by analyzing data from users' interactions with ads to determine the most likely sequence of ads that led to a conversion. This is done by tracking users' clicks, impressions, and conversions across different ads and campaigns and then using machine learning algorithms to analyze that data and identify patterns. Then, through Facebook’s API, it is possible to access the data programmatically and integrate it with other analytics tools, such as Google Analytics or Adobe Analytics.

The takeaway is that other platforms besides Google also offer data-driven attribution, and the choice of model should be based exclusively on business needs and specifics. 

Data-Driven Attribution in Google Ads

Why do we again refer to Google's DDA tool? As the most popular one with available documentation, you can start with Google, gain experience, and experiment with other tools. 

Here is how to start with Google Ads data-driven attribution:

1. Ensure adequate data volume: Keep reading to learn about the requirements. 
2. Enable conversion tracking: Before you can use data-driven attribution, you need to have conversion tracking set up in your Google Ads account by applying a conversion tracking code.
3. Access your Google Ads account for data-driven attribution: Log in to your Google Ads account with your Google account credentials.
4. Navigate to attribution settings: In the Google Ads dashboard, choose the data-driven attribution model. Once selected, Google will choose whether the website meets the data volume requirements. If it does, you can proceed with selecting data-driven attribution.

Data Requirements for Google Data-driven Attribution

• Access to GA 360. The major requirement is to be eligible to use the DDA model with a Google Analytics premium account. 
• Access to a high volume of high-quality data. To make DDA work for your marketing efforts, you must maintain a high volume of high-quality data from different sources.
• Goals and KPIs alignment. Align goals and KPIs across all the marketing channels you are using; otherwise, the DDA model won’t work.
• Minimum conversion threshold. Generally, conversion actions need at least 300 conversions and 3,000 ad interactions within 30 days. If your data drops below 2000 ad interactions or 200 conversions, you won’t be able to use this model any longer. If the low numbers continue for 30 days, the attribution will automatically switch to the last-click model. To enable the MCF (multi-channel funnel) DDA model, you need to meet the minimum conversion threshold of 400+ conversions per conversion type with a path length of 2 or more interactions, with 10,000 conversion paths fixed in the last 28 days. 
• Maintain the minimum conversion threshold. If you have met the minimum conversion threshold, it doesn’t mean it will automatically make your data eligible for ongoing DDA analysis. You should always maintain the requirements. 

Tip: DDA is a baseline model, and you can create your custom attribution model using the DDA model.

Data-Driven Multi-Touch Attribution Models

“Traditional” multi-touch attribution model has many times proven its efficiency against other attribution models, but with a data-driven approach, the game takes an absolutely new turn. Let’s compare them:

Traditional multi-touch attribution

• Rule-based: Traditional attribution models rely on predefined rules to assign credit to specific touchpoints. 
• Simplicity: These models are relatively simple to implement and understand. 
• Lack of granularity: Traditional models often don't consider the nuanced interactions and contributions of various touchpoints, potentially leading to inaccurate assessments of marketing effectiveness.
• Limited flexibility: Traditional models are static and don't account for the evolving nature of customer journeys.
• Quick implementation: These models are relatively quick to set up and don't require advanced data analytics or machine learning expertise.

Data-driven multi-touch attribution

• Algorithmic approach: Data-driven multi-touch attribution models are advanced analytical methods based on machine learning and statistical algorithms to analyze data and assign credit. They don't rely on predefined rules but learn from the data itself.
• Complexity: These models can handle the complexity of modern consumer journeys.
• Granularity: Data-driven models provide a more granular and accurate understanding of how each touchpoint contributes to conversions, assigning credit based on the actual impact of each interaction.

To sum up, let’s say that data-driven multi-touch attribution is a more sophisticated and accurate way to understand the impact of marketing touchpoints, especially in today's complex multi-channel digital landscape.

What does it Mean to Use the Right Marketing Attribution Model?

A data-driven attribution model has already proven its efficiency for millions of marketers who have discovered the potential of attribution models in favor of their marketing efforts. 

Understanding how customers convert through PPC ads

The success of PPC advertising depends on understanding the customer's journey and purchasing behavior. PPC ads are meant to attract users in several ways; for example,

• Searching for a product term
• Searching for an informational word
• Searching a problem
• Searching a brand

Often, customers can start with one search term and move on to the other options. Generally, customers convert when they search for brand terms. Without DDA, marketers invest in brand terms, ignoring the role of the other key steps. 

Identifying negative keywords and filtering out irrelevant clicks

DDA helps target your ads more effectively by using negative keywords to exclude uninterested search terms and focus on keywords that matter to your customers. This will help increase your ROI by showing your ad to users who are more likely to be interested. Data analysis will help you find user search queries that have the following characteristics:

• 150 or more clicks
• CPA (Cost Per Acquisition) above your target CPA

Any search query that falls under these conditions is considered a “negative keyword,” attracting users who are not interested in your product and wasting your ad budget. 

Discovering valuable keywords

Tracking valuable keywords is one of the goals an ad campaign aims for. With a data-driven attribution model, it is possible to see how each search term has the following characteristics:

• 2 and more conversions
• CPA (Cost Per Acquisition) below your target CPA

Find and mark search queries that meet these conditions as "positive ROI" keywords that will generate revenue for your campaign.

Determining best-performing ads

With a limited budget, running two or more ads is quite challenging, so the critical part is to keep optimizing ads and reduce budget waste. A data-driven attribution model determines the best-performing ads that deserve the expense and those that should be paused and canceled. 

Refining and optimizing your budget strategies

With DDA, you will move from the ad level to the campaign level, comparing the performance of campaigns and removing low-performing ones. Eventually, you will manage to optimize your budget.

Getting one global perspective

For omnichannel marketing, it is crucial to have a global perspective on processes to visualize your marketing direction and zoom into details whenever needed. 

Designing your Next-level Marketing Attribution Model: Should you Switch to DDA?

Testing modern and more effective data-driven attribution is a good choice, but should you stick to it? Everything depends on your experience, advertising goals, and conversion tracking setup. 

Keep in mind that there are no good or bad models. Experiment with different models to understand user behavior and find the most effective attribution model for your marketing efforts.

The future of marketing with machine learning promises to fill the gaps that marketers have experienced for years. With privacy controls getting tighter, traditional last-click attribution will fall short in terms of effectiveness. It means it’s time to set up a GA4 account with a data-driven attribution model by default (if you run a free GA3 account, DDA modeling will not be available).
The complexity of the attribution model is largely determined by the number of marketing efforts. To ensure accuracy, your current attribution model should be regularly updated. If your marketing team finds it difficult to handle large datasets, you can use Google's requirements as a guide to help you transition to another attribution model quickly.