In the world of digital marketing, attribution* methods are undergoing a major transformation. With the rise of the Internet, multi-touch attribution (MTA) has been considered the gold standard for measuring advertising effectiveness, but we are now entering an era where it is no longer effective enough due to strengthened privacy regulations and growing consumer awareness of protecting personal information. With the enforcement of the revised Personal Information Protection Act, as well as privacy regulations such as the GDPR (EU General Data Protection Regulation) and CCPA (California Consumer Privacy Act) both in Japan and overseas, and the abolition of third-party cookies, marketers are forced to seek new approaches.

*Attribution is a method of measuring and evaluating the contribution of each marketing initiative or touchpoint to a specific outcome (such as a purchase or application).

In this article, we explore the rapidly changing landscape of attribution in a privacy-driven era, its current state, challenges, and innovative solutions for the future. We understand the complexities posed by walled gardens and cookieless advertising, and offer insights into how emerging approaches and technologies can drive market success and how you should reimagine your attribution strategy.

*A walled garden is a system that encourages users to stay and be active on a platform as long as possible, allowing the platform provider to maintain its own rules and data.

The era of privacy

As digital marketing evolves, privacy regulations such as GDPR and CCPA are gaining momentum around the world. In Japan, the revised Personal Information Protection Act came into effect in April 2022, strengthening regulations on corporate data handling. This legal amendment requires Japanese companies to comply with privacy protection standards similar to those of GDPR and CCPA.

These regulations place strict limits on how companies can collect and use personal data. In addition, consumers themselves are becoming more sensitive about how their personal information is handled and are increasingly resistant to cookies and third-party tracking. This has led to a "cookieless era," forcing marketers to find new ways to do things.

According to a 2022 survey by the Ministry of Internal Affairs and Communications*, approximately 70% of Japanese consumers responded that they are concerned about the handling of their personal information online. In particular, there is a high tendency for concerns to be raised about "the leakage of personal information and internet usage history."

*Ministry of Internal Affairs and Communications "Survey on Telecommunications Usage Trends":https://www.soumu.go.jp/johotsusintokei/whitepaper/ja/r05/html/nd24b120.html

The decline of attribution

Traditionally, attribution has relied heavily on cookies, which are used to track a user's movements and behavior across websites, but in recent years browser security measures and regulations have limited their role.

Changes in the MTA environment

For years, MTAs have relied on third-party cookies to track users across the web and provide marketers with detailed information about consumer behavior, but two changes have had a major impact on this approach:

1. The growth of walled gardens in 2020 and beyond
2. Phasing out third-party cookies

These changes have necessitated a dramatic shift in attribution methodologies, moving the industry towards a more privacy-first approach.

Walled gardens are a double-edged sword

Walled gardens built by big tech companies like Google, Meta and Apple further complicate attribution. While they offer powerful advertising platforms, they also operate as closed ecosystems, limiting the access and sharing of data. This fragmentation creates challenges:

• Attribution methods vary across platforms
• Tracking and optimizing consumer behavior across platforms becomes difficult
• The cost and complexity of integrating consumer behavior increases

In particular, companies that advertise across multiple channels and offline media face challenges in getting a comprehensive view of consumer behavior and accurately measuring the impact of their campaigns.

The end of third-party cookies

Third-party cookies have long been the foundation of digital advertising, providing detailed insight into user behavior across the web, but growing privacy concerns are causing some changes:

• Tighter regulations on data collection and use
• Major browsers phasing out support for third-party cookies
• Implementing privacy safeguards, such as Apple's App Tracking Transparency (ATT)

On the other hand, Google's postponement of the abolition of third-party cookies until July 2024 had a major impact on the entire advertising industry and attracted a lot of attention. This announcement brought temporary relief to the digital advertising industry, but at the same time, it raised many questions. Google decided to postpone the abolition because its ecosystem was not yet ready, but this is merely an "extension of the preparation period," and the fact remains that the transition to a cookieless world is ultimately inevitable.

A revolutionary approach to attribution

However, the challenges posed by the demise of walled gardens and cookies do not mean the end of attribution. Rather, we are likely to see a shift in approach with privacy at its core. Below are some innovative approaches and solutions that are emerging in this space:

1. Leverage first-party data

There is renewed awareness of the importance of first-party data collected directly from customers. By focusing on data such as website browsing history, app usage, and purchase history, companies can gain a deeper understanding of how each touchpoint contributes to an outcome, such as a purchase or sign-up. Key initiatives include:

• Obtaining explicit consumer consent to data collection
• Promoting communication to strengthen trust with consumers
• Create comprehensive consumer profiles for deeper insights

2. Privacy Protection Technology

In response to increasing privacy regulations and consumer demand for data transparency, attribution and digital ad measurement companies are adopting privacy-first approaches, including by:

•  Utilizing differential privacy technology*
•  Data anonymization and aggregation
•  Reevaluating and strengthening contextual advertising

*Differential privacy technology is a data processing method that can obtain statistically meaningful analysis results while protecting personal data. This technology intentionally adds noise (random errors) to the data, making it difficult to identify individuals while maintaining overall trends and characteristics.

3. Data Clean Room

Data clean rooms emerge as secure virtual environments where first-party data can be shared in privacy-sensitive circumstances without disclosing personal information. Key initiatives include:

• Cooperation between advertisers, media and advertising platforms
• All PII (Personally Identifiable Information) is encrypted
• Only statistical information is used as the analysis result

The advantages of this method include the ability to achieve advanced attribution analysis and improve the ability to measure advertising effectiveness.

However, implementing data clean rooms requires complex development and maintenance costs, and is difficult to operate in real time (shared data needs to be updated regularly), so for now it tends to be limited to large enterprises or specific use cases.

4. Advanced Marketing Mix Modeling (MMM)

MMM is a method for analyzing the impact of various marketing measures on results such as sales. This method uses aggregated data rather than individual-level data. This makes it possible to analyze marketing effectiveness without violating individual privacy. The main initiatives are as follows:

• Use marketing strategy data (volume of advertising/cost, etc.) and results data (sales volume/revenue, etc.)
• Modeling the relationship between policies and outcomes using statistical methods
• Comprehensive evaluation including not only digital advertising but also offline advertising
• Analyze the ripple effects and synergies between channels

This solution not only analyzes the effectiveness of measures, but also predicts results from various budget allocation scenarios and can simulate optimal budgets. Another advantage is that it can analyze not only measures but also seasonality and external factors.

"MAGELLAN" by XICAAs an alternative to MTA, modern MMM solutions such as these can help improve your return on marketing investment (ROMI).

Related articles:What is MMM (Marketing Mix Modeling)? Explaining its features, procedures, examples, etc. 

5. Incrementality Testing

Incrementality testing quantifies the incremental effect of marketing activities by comparing a test group with a control group. The main efforts are as follows:

• Analysis independent of user-level data
• Compare the results of test and control groups
• Measure incremental lift/incrementality

Incremental testing is a method that allows you to measure the effectiveness of creative and segments, complementing other attribution methods and allowing you to measure the additional effectiveness of your marketing initiatives.

Technological evolution drives new thinking and roles for marketers

Moreover, the integration of machine learning and AI into these approaches and solutions is one of the most exciting developments in attribution. The integration of these technologies allows for:

• Rapid and efficient analysis of large amounts of data
• Extracting complex patterns from fragmented data
• Improved personalization features
• Advanced multi-touch and algorithmic contribution value assignment
• Building and using predictive models for budget optimization

Furthermore, the changes in the environment surrounding attribution in digital marketing are not just technological advances, but are also leading to a change in marketers' thinking. Rather than focusing only on touchpoints that led to results such as purchases or applications, marketers are now being asked to grasp the entire customer behavior and focus on optimization from a long-term perspective.

This mindset shift coincides with the evolution of marketing's role in driving growth for their organizations. Leveraging first-party data and advanced attribution techniques, marketers will be better positioned to:

• Discover new customer needs and preferences
• Promoting innovative product development
• Providing specific, actionable feedback to development teams
• Increase customer satisfaction

Technical implementation hurdles and solutions

The introduction of innovative attribution methods and privacy-preserving technologies has the potential to improve marketing performance, but not all companies are able to quickly adopt these technologies. There are several hurdles to overcome, especially for small and medium-sized businesses and those with less developed data infrastructure.

Cost Issues

Introducing new technologies and tools requires initial investment. For example, introducing a customer data platform (CDP) or data clean room to collect and manage first-party data requires licensing fees and system development costs. In addition, hiring and training personnel with specialized skills to implement data protection technologies and machine learning models is also a cost-intensive factor.

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• Phased implementation:
  It is important to introduce technologies in stages, rather than all at once. Start with the collection and analysis of first-party data, which is likely to have an immediate effect, and then gradually introduce advanced MMM and AI technologies, which will help spread out the risk.
• Leveraging partnerships:
  When the technical hurdles are high, it is effective to utilize external data analysis and attribution specialist companies to collect and accumulate know-how while keeping costs down. This allows you to quickly use technologies that cannot be covered by your own resources alone.

Lack of expertise

The introduction of advanced attribution methods and data privacy technologies requires specialized knowledge and skills. For example, data analysis using machine learning and AI requires data scientists and engineers who are familiar with building algorithms and processing data. However, it is not easy to secure such talent in-house.

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• Use of external consulting:
  If you have limited internal resources, it can be effective to use external consultants or specialized agencies to help with technology implementation, as expert advice can speed up implementation and reduce the risk of incorrect implementation.
• Employee training:
  In the long term, it is important to develop data analytics and attribution skills internally. Upskill your employees through online courses and workshops, and improve digital literacy across the organisation.

Here are some other key takeaways for a successful attribution strategy going forward:

• Building cross-functional collaboration:
  Collaborate with your marketing department, IT department, and legal department to foster a privacy-first culture across your organization when it comes to collecting, analyzing, and using data.
• Continuous learning and adapting:
  The digital marketing landscape is constantly changing, so stay up to date on the latest trends and technologies to continually improve your capabilities across your organization.
• A customer-centric approach:
  Attribution is a means, not a goal. Always keep improving customer experience as your end goal and use that data to guide your efforts.

Summary

The world of digital marketing is facing a new challenge: balancing privacy protection and data utilization. On the other hand, it is also an opportunity for innovation and growth. By implementing effective strategies, effective marketing activities and accurate measurement of effectiveness will be possible even in an era where privacy is emphasized. Furthermore, balancing privacy and performance will be a source of competitive advantage in future digital marketing. Now is the time to review your company's attribution strategy and start preparing for the future. It is important not to be afraid of change, but rather to see it as an opportunity and build a new form of marketing based on trust with customers.

XICA is a professional data science company that provides optimal solutions and professional support centered on MMM to adapt to the privacy-conscious era. If you want to strengthen your data-based decision-making and accelerate your marketing success, please try it out.of the Directions & ParkingOur consulting team will work with you to develop the best strategy tailored to your needs.

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