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.
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.
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.
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 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.
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.
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.
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.
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:
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.
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:
Tip: DDA is a baseline model, and you can create your custom attribution model using the DDA model.
“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:
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.
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.
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,
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.
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:
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.
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:
Find and mark search queries that meet these conditions as "positive ROI" keywords that will generate revenue for your campaign.
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.
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.
For omnichannel marketing, it is crucial to have a global perspective on processes to visualize your marketing direction and zoom into details whenever needed.
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.
Time to switch to a data-driven attribution model.
A data-driven attribution model credits conversions based on how potential customers engage with your advertisements. DDA uses machine learning to analyze and map engagement across all touchpoints, adding value to each interaction.
A data-driven attribution model gives you a comprehensive map of journeys and knowledge of the productivity of ads, campaigns, and keywords. Nevertheless, there is no good or bad attribution model; the best one is the one that works best for your marketing efforts, and to find one, you need to experiment with all of them.
Everything depends on your experience, advertising goals, and conversion tracking setup. You can compare the results by experimenting with different attribution models. By switching to DDA, you will be able to discover valuable keywords, determine negative ones, track the best-performing ads, and optimize campaign budgets.
Data-driven attribution is a model that can be used by various digital platforms, not just Google. Google has a tool called the "Data-Driven Attribution Model," which is built-in into Google Analytics, and it’s a widely used tool in the industry. However, other attribution modeling tools are available on the market, such as Visual IQ, Adobe Analytics, Singular, etc.
Google Analytics uses the last-click attribution model by default. If you do not meet the requirements for data-driven attribution, you will be automatically switched to the last-click model.
October 27, 2023
8 min. read