Navigating Consent Mode, ITP, Ad Blockers & Attribution

Ilya Chu

,

Analytics Solution Architect

Olga Mirgorodskaya

,

Creative writer

December 2, 2024

Digital marketing analytics has become more challenging with the rise of privacy regulations, consent requirements, and tracking limitations that reshape how marketers measure and analyze data.

Tools like Google Consent Mode V2 and Apple's Intelligent Tracking Prevention (ITP) add layers of complexity, making it crucial for marketers to adapt to new restrictions and find compliant ways to gather insights. These changes affect everything from ad visibility to cross-device tracking and attribution.

This article dives into practical solutions for overcoming today’s digital analytics hurdles, focusing on key areas like consent management, ad blockers, and the shift in attribution models with GA4.

Note: This article was written in 2022 and was updated in December 2024 to keep up-to-date with the latest trends in GDPR and Consent.

Why Are These Known as the Four Horsemen of Digital Marketing Analytics?

In digital marketing, Consent Mode, Intelligent Tracking Prevention (ITP), Ad Blockers, and Attribution Challenges have earned the nickname "The Four Horsemen."

Like the Four Horsemen in myth and literature, these challenges signal major disruptions. Each brings a distinct set of obstacles that can alter how marketers collect, analyze, and act on data. Together, they impact key aspects of marketing performance, from tracking user behavior and managing consent to accurately attributing conversions.

As data privacy regulations tighten and user behaviors shift, these four forces demand adaptive strategies. Navigating each requires marketers to balance compliance with insight-gathering, finding ways to maintain campaign effectiveness while respecting user privacy and evolving tech constraints.

The Importance of Data Quality in Digital Marketing Analytics

As online advertising budgets continue to rise, so does the cost of customer acquisition.

Most marketers try to adapt by optimizing ad campaigns, landing pages, and creatives. However, few understand that an advertising campaign can have a low ROI or ROAS not because it is bad but because of poor data quality. To properly optimize your ad campaign, you need to make sure you can trust the data you’re basing your decisions on.

According to Forrester research, one of the main reasons for rising ad spending is poor data quality for marketing analytics. Here are some of the implications in numbers:

These numbers are not random. The marketing tool stack is growing. In our experience, a simple marketing report requires an average of 10+ data sources. If you do not react to current changes in time, this can lead to up to 60% of conversions in your reports having the wrong traffic source. How will this affect your marketing?

A preliminary assessment allows us to draw the following conclusions:

Intuitive marketing decisions will become more important, making those decisions less likely to be successful. Reports will show ~30% fewer conversions across all non-direct channels than before.
Marketing departments will lose almost all evidence of the impact of display campaigns on conversions. Every attribution model will look like a last click.
Marketing departments will have fewer data-driven arguments to prove their results and protect their marketing budgets. The total share of mismatched CRM conversions will be ~40%.

Here is what your report might look like without complete and accurate data:

The sadly predictable result is that this data will not meet any of your marketing team’s requirements to ensure timely insights and performance improvements.

Overcoming the Four Key Challenges in Digital Marketing Analytics

Digital marketing analytics faces four major challenges that can disrupt data accuracy and insight: Consent Mode, Intelligent Tracking Prevention (ITP), Ad Blockers, and GA4 Attribution. Each of these obstacles impacts how data is collected, attributed, and analyzed, making it harder for marketers to maintain reliable reports and optimize performance.

To effectively manage these challenges, it is crucial to implement a consent mode, which helps in managing user consent for data collection and ensuring compliance with privacy regulations.

What is Consent Mode?

Consent Mode is a tool that helps manage Google’s advertising and analytics services based on user consent. It ensures basic user interaction can be tracked without compromising privacy using prediction modeling. Consent Mode does not replace the need for a Consent Management Platform (CMP) or a cookie consent banner.

Consent Mode allows websites to adjust how Google tags behave based on the consent status of users. When a user interacts with your site and grants consent, Google tags can collect and use data as usual.

However, if the user does not grant consent, the tags will operate in a limited mode, sending only non-identifiable data. This approach helps balance the need for data-driven insights with the growing demand for user privacy.

Google Consent Mode V2: Challenges and Solutions

Google Consent Mode V2 is an advanced tool for managing user consent and data collection on websites. Basic Consent Mode ensures that tags remain inactive until users interact with a consent banner, thereby preventing data collection without explicit consent.

By allowing flexibility in how each tag behaves based on user consent, it balances privacy with data-driven insights essential for marketers.

Properly configuring consent settings within Google Tag Manager is essential for maintaining measurement accuracy and compliance with consent requirements.

Challenges of Google Consent Mode V2

With GDPR and similar regulations, Google Consent Mode V2 allows websites to track user activity while respecting users’ choices regarding data sharing. However, when users reject cookies, critical traffic source data for conversions can be lost.

Setting a default consent state is crucial for ensuring compliance and accurate measurement, especially in regions where consent banners are required.

As many as 30-40% of users on sites with Consent Mode V2 opt out of cookies, leading to unlinked conversions and reduced attribution accuracy. This data loss can result in underreported ROI and gaps in understanding campaign performance.

Solution: Implement Conversion Modeling

Conversion modeling can mitigate these challenges by estimating traffic sources for unconsented conversions, providing a more complete data picture. Key steps include:

Configure Consent Mode V2 to ensure compliance by collecting only non-identifiable data for unconsented users. Fields like email, user ID, and IP should be left blank.
Use Machine Learning for Attribution: Apply machine learning models trained on consented data, using non-personal parameters such as region, browser, and device type to estimate traffic sources for unconsented conversions.
Incorporate Modeled Conversions in Reports: Add modeled conversions to your marketing reports to enhance ROI calculations, creating a more accurate representation of campaign impact by filling in the gaps left by unconsented data.

By leveraging conversion modeling with Consent Mode V2, marketers can expect improved attribution for approximately 70% of conversions, while gaining estimated traffic insights for the remaining 30%.

Setting Up Consent Mode V2

To set up Consent Mode, ensure consent updates or consent status are tracked on the page where they occur before any page transition. The Google tag takes actions (e.g., writing cookies, sending events) in response to the command to ensure future events will include the full measurement data.

Here’s a step-by-step guide to implementing Consent Mode:

Step 1: Integrate a CMP – Choose and set up a Consent Management Platform (CMP) like Cookiebot to collect user consent and ensure compliance with regulations (e.g., GDPR, CCPA).

Step 2: Set up Google Tag Manager – Enable consent overview in GTM’s ‘Container Settings’, create a new tag, and configure it with your CMP’s ID to reflect consent status.

Step 3: Configure the Cookiebot CMP Tag – Enter the CMP ID, set region-specific consent preferences (if applicable), and use the “Consent initialization – All pages” trigger to fire the Cookiebot tag.

Step 4: Debug and Publish – Test your setup in GTM’s preview mode to ensure the consent banner appears and user selections for consent are respected; publish the tag once confirmed. The Consent Mode V2 has 2 new consent types: ‘ad_personalization’ and ‘ad_user_data’.

Step 5: Verify Built-in Consent Checks – Confirm that tags with built-in consent checks (e.g., Google Tags, Ads) function properly, and set up custom event triggers for tags that need additional consent verification (e.g., Meta Pixel).

Implementing Consent Mode V2 is crucial for maintaining compliance and ensuring accurate measurement data. By integrating a CMP, configuring GTM, and thoroughly testing your setup, you can seamlessly manage consent preferences while respecting user privacy.

This approach empowers you to balance compliance with actionable insights, enhancing your data-driven decision-making.

Intelligent Tracking Prevention (ITP) and Its Impact on Analytics

Apple’s Intelligent Tracking Prevention limits cookies' lifespans, disrupting how user activity is tracked across websites, especially in the Safari browser. This presents challenges in linking user journeys, particularly in long sales cycles.

What is ITP?

Intelligent Tracking Prevention (ITP) is a feature of WebKit, an open-source web-browser engine, that powers Apple’s Safari web browser. It was introduced in Safari 12 and iOS 11 to protect users’ online privacy by changing the way Safari handles first-party cookies.

ITP incorporates a machine-learning model to assess which privately controlled domains have the ability to track users across different websites. If the model identifies a first-party cookie as a tracker, it will be blocked unless the user uses the Storage Access API to allow the use of the cookie.

ITP aims to limit the cross-site tracking capabilities of cookies, thereby enhancing user privacy. It uses a sophisticated machine-learning model to identify and restrict cookies that can track users across multiple sites. This means that even first-party cookies, which are typically used for legitimate purposes like remembering user preferences, can be affected if they are deemed to have tracking capabilities.

ITP Challenges for Marketers

ITP presents several challenges for marketers aiming to track user behavior accurately and maintain attribution data. Key challenges include:

Reduced Cookie Lifespan: With cookies restricted to a 24-hour lifespan, tracking user interactions over extended periods becomes difficult, impacting ad attribution and remarketing efforts.
Cross-Device and Cross-Browser Tracking Challenges: ITP makes it harder to track users across multiple devices and browsers, leading to fragmented data that affects cross-channel marketing attribution.
Privacy Compliance Costs: Staying compliant with privacy regulations requires significant resources. Marketers need to invest in tools and strategies to adapt to the ITP environment, which can increase costs.

Solutions for Intelligent Tracking Prevention Challenges

To address the challenges posed by ITP, marketers can adopt the following solutions:

Use First-Party Data and Server-Side Tracking: Collect first-party data and shift data collection to server-side tracking to enhance cross-device attribution.
Implement Consent Management Platforms (CMPs): Using a CMP enables websites to collect and manage user consent for tracking, ensuring compliance with GDPR, CCPA, and other privacy regulations.
Leverage Consent in Affiliate Links: Implement consent parameters within affiliate links, such as the "CNST" parameter, which tracks user consent signals to meet CCPA compliance.

These strategies allow marketers to adapt to ITP's limitations and comply with evolving privacy standards, helping to retain a more accurate picture of user behavior and attribution data.

Ad Blockers: A Growing Concern for Digital Marketers

Ad blockers are tools that prevent ads from being displayed on websites, affecting the visibility and revenue of marketers who rely on online advertising. Plugins like Adblock Plus and privacy-focused browsers have popularized ad blocking, impacting both small and large websites that depend on ad revenue to operate.

Integrating Google Ads with consent management tools ensures that advertising effectiveness and tracking accuracy are maintained while respecting user consent.

Ad blocking has grown rapidly over the years. According to Adobe and PageFair, desktop ad blocker usage increased from 21 million users in 2010 to over 181 million by early 2023, with mobile ad blockers gaining traction as Apple added ad-blocking support on iOS.

This trend, combined with privacy concerns, significantly impacts advertising revenues and digital marketing strategies.

Challenges Ad Blockers Present to Digital Marketing

Ad blockers present significant challenges for digital marketers, particularly those who rely heavily on display and PPC ads.

Reduced Ad Visibility: With ad blockers, ads are hidden from users, meaning even the best-targeted ads won’t reach those who use these tools, with built-in ad-blocking support.
Impact on PPC and Display Campaigns: Ad blockers can prevent PPC ads and display ads on platforms like Google Ads and Microsoft Ads (formerly known as Bing Ads) from appearing, leading to lower impressions and missed conversions
Loss of Mobile Reach: Mobile users with ad blockers installed won’t see ads, regardless of ad format or campaign type. For marketers targeting mobile audiences, this reduces the effectiveness of campaigns.
Lower Campaign Performance Metrics: With fewer impressions and conversions, marketers may see skewed performance metrics, affecting ROI calculations and campaign optimization decisions.

Solutions to Overcome Ad Blocker Challenges

To adapt to the growing use of ad blockers, marketers can implement strategies that reach audiences in ways that bypass traditional ad limitations. Here are some effective solutions:

Leverage Native Advertising: Native ads are designed to blend seamlessly with content, appearing as part of the user’s experience rather than as standalone ads.
Focus on Content Marketing and SEO: Invest in high-quality content that provides value and engages audiences organically. Well-optimized content can improve search rankings and attract users without relying on ads that may be blocked.
Implement Server-Side Tracking: Moving tracking and data collection to the server side can help bypass ad blockers, as server-side events aren’t blocked as easily as client-side data.
Emphasize Email and CRM Marketing: Building a solid email list and engaging with customers directly through CRM-based campaigns ensures you’re reaching users in a way that’s unaffected by ad blockers.
Retarget with Consent-Based Remarketing: Use retargeting that’s built around user consent and preference. This approach can help improve engagement with users who have shown interest while respecting privacy choices.

By diversifying strategies and moving beyond traditional ad placements, marketers can reach a broader audience, ensure accurate tracking, and improve campaign effectiveness.

Google Analytics 4 (GA4) Attribution Challenges and Solutions

GA4 attribution is the method Google Analytics 4 uses to assign credit for conversions across different user touchpoints. Unlike Universal Analytics, which focuses on last-click attribution, GA4 provides more flexible, user-centric attribution models, including data-driven, first-click, and last-click options.

This enables marketers to see how various channels contribute to conversions along the user journey.

Common GA4 Attribution Challenges

Google Analytics 4 (GA4) introduces a user-centric, event-based tracking model that brings unique attribution challenges as marketers transition from Universal Analytics. Here are some of the primary issues:

Model Inconsistencies: GA4 allows the use of different attribution models (e.g., first-click in User Acquisition vs. data-driven in Key Events), which can lead to confusion when analyzing conversions across reports.
Cross-Channel Attribution Confusion: With options like data-driven attribution (DDA) and last-click models, GA4 requires marketers to balance accuracy with data sufficiency. Choosing the best model for each channel and understanding its limitations are key to effective cross-channel attribution.
Delayed Data Processing: GA4 processes data in near-real-time, but full data processing can take up to 24 hours, which may cause short-term reporting gaps and make it challenging to respond promptly to performance shifts.

Solutions to GA4 Attribution Challenges

To address these attribution complexities and make the most of GA4’s advanced features, marketers can adopt the following strategies:

Use the Attribution Model Comparison Tool: This tool allows you to compare the impact of different attribution models on conversion data. By assessing model differences, you can choose the most suitable model for your specific goals.
Align Attribution Models with Business Goals: Consistently using attribution models that align with your campaign objectives helps reduce reporting inconsistencies and improves data accuracy.
Implement User ID Tracking and Google Signals: User ID tracking and Google Signals enable cross-device tracking, providing a more unified view of user behavior.

By taking these steps, marketers can better navigate GA4’s attribution challenges, ensuring accurate reporting and a more comprehensive understanding of each channel’s role in conversions.

Best Practices for Managing Analytics in a Privacy-First World

Managing analytics effectively requires balancing data insights with user privacy in a privacy-first world. Adopting best practices can help marketers comply with regulations while gathering valuable data.

Utilize First-Party Data Collection

With the decline of third-party cookies, collecting first-party data is essential for effective, privacy-compliant analytics. First-party data lets marketers track user interactions directly from their site, leading to more accurate attribution and personalization.

Use customer interactions, site behaviors, and transactional data to view user journeys while respecting privacy preferences comprehensively.

Optimize Consent Management

Consent Management Platforms (CMPs) and automated compliance tools help ensure adherence to privacy laws like GDPR and CCPA. CMPs let users manage their cookie preferences like first, second, or third-party cookies, enabling businesses to respect user choices while collecting essential data.

Regularly update CMP settings and use compliance monitoring to adapt to evolving regulations, maintaining legal compliance and user trust.

Diversify Marketing Attribution Channels

Expanding attribution beyond cookies is essential in a privacy-first landscape. By diversifying across organic and paid channels - including email marketing, social media, and native ads, marketers can mitigate data loss from ad blockers or short-lived cookies.

This multichannel approach helps capture broader user interactions, improving data reliability and campaign performance insights.

Thrive in a Cookieless World with OWOX BI Streaming

Right now, in these evolving privacy regulations and complex analytics challenges, OWOX BI Streaming empowers marketers to collect and process data in near real-time. This tool ensures your analytics remain accurate, even with consent restrictions, ad blockers, and attribution hurdles.

OWOX BI Streaming integrates seamlessly with your existing systems, capturing complete and high-quality data for informed decision-making.

Whether navigating GA4’s attribution complexities or maintaining compliance with privacy laws, this solution provides reliable insights to optimize campaigns and measure performance effectively. Elevate your data-driven strategies with OWOX BI Streaming and stay ahead in the analytics game.

Key Takeaways

Due to limitations in data accuracy and timeliness, marketing teams can waste up to 21% of their budget and 32% of their team’s time on ineffective strategies. Tracking restrictions and privacy-first regulations further impact data completeness, making attribution and ROI analysis more challenging.
Modern tracking limitations, like consent requirements and ITP, lead to data gaps where up to 60% of online conversions may be attributed to incorrect sources, affecting the reliability of marketing performance insights and hindering budget defense.
These challenges make it harder for marketing departments to justify spending, derive accurate insights, and run effective experiments, ultimately impacting growth potential.
This article outlines practical solutions to improve data accuracy and attribution, from leveraging first-party data and Consent Mode V2 to diversifying marketing channels and using GA4’s Attribution Model Comparison Tool. These solutions determine the traffic source chain for over 97% of conversions, and all 4 can be configured with OWOX BI.