The coming disappearance of third-party cookies will affect everyone in marketing, but those who depend on direct digital attribution will suffer the most.
Third-party cookies have been the ideal tool for the job. With them, marketing teams can track online activity at the user level, gather data for message targeting and retargeting, and follow the user all the way down the funnel to their conversion points.
Now, reports will struggle to attribute conversions across the different touchpoints in the customer journey. There will be more guesswork in frequency capping, retargeting, and when generating consistent user IDs across various platforms.
But to be fair, eliminating cookies is only the tip of the iceberg regarding the decline of direct attribution. There’s also the convergence of:
- Cross-device and cross-platforms
- Ad-blocking software
- Data silos
- Marketing complexity
- Rise of private walled gardens
Which are all working together to make things a bit tedious.
Is there any way out of this?
There are alternative approaches to measuring and understanding the impact of your marketing efforts.
Two tried and true approaches include first-party data strategies and privacy-compliant data partnerships. Embracing first-party data collection from direct customer interactions can provide valuable insights without relying on third-party cookies. Privacy-compliant data partnerships allow you to collaborate with trusted partners and data-sharing alliances. Both of these tactics can help bridge the gap caused by the decline of third-party cookies. You can leverage anonymized and aggregated data to gain insights into consumer behavior while respecting privacy regulations.
However, contextual targeting and AI-powered attribution models are the newest areas of development. Both can help your marketing team gain insights into what’s working.
Contextual targeting: Instead of tracking individual users, shift towards contextual targeting, focused on content relevance and user intent within specific contexts (e.g., an ad for a facial cleanser on a YouTube skincare tutorial). This approach allows for personalized ad placements without relying on user-specific data. You can leverage contextual targeting for video, display, and behavioral ads.
AI-powered attribution models: Machine learning models already play a role in deciphering complex user journeys and attributing conversions, and their commodification by the major cloud platforms makes them promising options for more precise guessing. Advanced AI models can analyze vast amounts of organized data to forecast the patterns and trends that inform marketing strategies.
Tools that can alleviate attribution challenges
Emerging technologies, tools, and platforms can help you adapt to the changing attribution landscape, gather meaningful insights, and optimize your campaigns effectively. Here are some of them:
Multi-touch attribution platforms
Platforms like Google Analytics 4 or HubSpot use sophisticated algorithms and machine learning to analyze user interactions across multiple touchpoints. They provide a more comprehensive understanding of the customer journey, attributing value to each touchpoint based on its contribution to the conversion.
CDPs (customer data platforms)
CDPs aggregate data from various sources to create unified customer profiles. These profiles provide a holistic view of individual users, enabling personalized marketing efforts and tracking user journeys across devices.
In-app analytics and SDKs
For mobile apps, in-app analytics and SDKs (software development kits) offer insights into user behavior within the app. These tools provide valuable information about engagement and conversions within a controlled environment.
Machine Learning Ecosystems
The major cloud platforms (Google, AWS, and Microsoft) each offer plug-and-play machine learning ecosystems for rent. Using their off-the-shelf tools, you can build data stores and attribution models in your own rented machine-learning environment, from as simple to as complex as you need.
FLoC (federated learning of cohorts)
Developed as part of Google‘s Privacy Sandbox initiative, FLoC groups users with similar browsing habits into cohorts for ad targeting. It maintains user privacy by keeping individual data on the user’s device.
Customer journey mapping tools
You can use Microsoft Visio, Smaply, Figma, or any other tool to visualize and understand the user journey across touchpoints. This will help you identify key moments and interactions that lead to conversions, even without precise attribution data.
You’ll need a sophisticated data program
All these tools are great, but you’ll eventually need to integrate everything into a single report to understand attribution and make the right decisions. And if you’re using platforms, CDPs, app analytics, ML ecosystems, and mapping tools all at once, it can get complicated.
To make data insights actionable, take advantage of your mapping work and begin building a flexible data architecture that captures the current and future organization of your data and who needs which dashboards. Understanding what’s involved in a comprehensive data project is essential.
Don’t make finding a data architect an insurmountable hurdle to your project, though. Start by hiring a dedicated data analyst who can uncover insights and create simple machine learning models using some of the tools we discussed above. Maintenance will be an ongoing challenge, so hire more people with different skills as you collect more sources and make more products.
We say this not to put a damper on your digital attribution goals but because the solution is not a quick fix. You will need a data strategy, a modern architecture, and a data-driven culture to de-risk your digital marketing goals.
While digital attribution challenges persist, they are actually pushing the marketing world to come up with smarter, more secure solutions. To stay ahead, you must embrace creativity, foster innovation, and commit to value and privacy. In the long term, this is good! It will help you build strong relationships with customers.
