2024: A Turning Point in Digital Identity—The Rise of Non-Biometric Solutions and the Role of AI in Advertising and Marketing 

A Pivotal Year for Digital Identity

The year 2024 marks a pivotal moment for the digital identity solution industry, characterized by unprecedented growth and innovation. As technologies evolve and traditional data collection methods become increasingly outdated, digital identity solutions have become essential for guiding marketing and advertising campaigns. According to the CrowdStrike 2024 Global Threat Report, 75% of initial access attacks now occur without the use of malware, highlighting the need for new data-gathering methods that do not rely on third-party cookies. This is where Identity solutions come in. As advertising cookies and other traditional methods phase out, marketing and advertising firms are increasingly turning to digital identity solutions to stay ahead. These solutions help businesses meet the rising demand for deeper consumer insights and stay current without the risk of data breaches. By leveraging advanced identity solutions, firms can gain valuable insights into consumer preferences and behaviors, enabling them to create more effective and personalized advertising strategies for the future.

What are Identity Solutions? 

Identity solutions are advanced technologies designed to track and identify users across various digital platforms and channels, including web browsers, mobile devices, and streaming services. Unlike traditional third-party cookies, which are often collected by external entities, identity solutions are obtained directly from the publisher. This direct approach allows users greater control, as they can opt in or out of these solutions when visiting a website and simultaneously gain more transparency about who is tracking their data.

There are generally three types of identity solutions:

• Seller-Defined Audiences: Publishers use their own data to group audiences over time and then offer these segments to advertisers and marketers for their campaigns.

• Universal IDs: These are unique identifiers that help companies track individuals across the web. Universal IDs are gaining popularity because they allow users to opt in or out while providing consistency across different publishers, enabling advertisers to use them widely.

• Cohort-Based Solutions: These solutions group individuals into segments based on shared interests. This allows companies to target advertisements and content to aggregated data groups rather than individual users.

Each type has its advantages and disadvantages, but Universal IDs (Type 1) are currently receiving the most investment. Companies like Yahoo are embracing this trend with taglines like “built on real relationships,” positioning these solutions as ideal for establishing private and loyal connections between users and companies.

Although identity solutions have been available for decades, recent legislation has heightened their importance and adoption as privacy and anti-piracy laws are commonplace. Identity solutions are indispensable for advertisers and marketers, particularly as AI and machine learning become integral to modern marketing strategies. These solutions are now crucial for gathering cutting-edge consumer data, enabling more precise targeting and personalized experiences.

Industry Overview

Rise of Non-Biometric Solutions, Market Growth and Drivers

The non-biometric segment of the identity solution industry—particularly advancements in machine learning (ML) and artificial intelligence (AI)—has experienced remarkable progress over the past year. This has also been exacerbated by the integration of biometrics into smartphones and the transformative impact of the pandemic on work patterns have significantly boosted the global digital identity solutions market. By 2022, the market had surged to USD 27.51 billion. Today, it stands at USD 40.1 billion, with projections estimating it will reach USD 117.42 billion by 2030. This rapid growth is largely fueled by the increasing consumer demand for data protection and the surge in global privacy regulations. In a world where identity solutions may no longer exist, AI and machine learning will become the standard tools for aggregating and analyzing data and consumer trends.

Digital ID Wallets

As the world has advanced technologically in recent years, both governments and businesses are seeking methods to allow citizens to manage personal affairs more conveniently and efficiently. Digital ID wallet solutions have emerged as a key innovation in this effort. These secure mobile applications store digitized and encrypted user documents, including government IDs, driver’s licenses, vehicle registration certificates, healthcare information, and more. The primary advantage of these solutions lies in their ability to offer users safe, quick, and convenient access to essential documents.

Dominance and Decline of Biometric Solutions

However, the biometric segment's market share has declined from 69% in 2022, reflecting the gradual rise of non-biometric solutions. These non-biometric approaches are gaining traction due to their enhanced privacy features and the ability to offer a more personalized experience. They foster a closer, more individualized relationship between consumers and companies, allowing users greater autonomy in their choices.

The Role of AI and Machine Learning

Advantages of AI and Machine Learning in Digital Identity Solutions

While AI has become a familiar concept to many, a significant portion of the population remains inherently distrustful, with 82% of Americans expressing concern about how AI might shape the future. However, when applied to digital identity solutions, AI has the potential to both safeguard user data and advance personalized, accurate data models without relying on third-party cookies.

AI’s role in the adoption of digital identity solutions is indeed shaping a promising future for online data collection. While AI excels at preventing malware and enhancing user security—critical for building brand trust—its most exciting applications lie in creating comprehensive data identities and monitoring consumer behavior.

AI's strength in data integration and analysis allows it to rapidly and accurately combine information from multiple sources, building detailed user profiles. This holistic approach empowers businesses to make informed decisions about user access, privileges, and services. Additionally, AI enables businesses to remain agile, adapting their advertising and marketing strategies to address emerging trends and technological challenges.

Machine learning algorithms, in particular, are adept at understanding and learning from new data. By analyzing vast amounts of information, these algorithms can establish baselines of normal user activity, allowing them to quickly detect deviations and identify consumer trends. They also enhance user experience by offering secure and convenient ways to manage and understand data, supporting personalization and audience segmentation for more targeted ad content. This capability enables the dynamic personalization of data and ads over time, reducing reliance on invasive third-party cookies.

Challenges and Considerations

Bias and Accuracy in AI Algorithms

Despite its promising benefits, the implementation of AI in digital identity solutions is not without challenges. One significant issue is the risk of bias in AI algorithms. If the data used to train these algorithms is not diverse or representative, it can lead to unfair or inaccurate outcomes, resulting in inappropriate final judgments by the AI. Such occurrences can undermine the very purpose of digital identity solutions, making it imperative for companies to be meticulous and stringent when training AI systems.

Technical and Financial Hurdles

Integrating AI into existing digital identity infrastructure presents both technical and financial challenges. This process requires significant human and financial resources, which can be a substantial burden for many organizations. Ensuring a seamless integration involves not only expertise but also a considerable investment, posing a daunting challenge for many companies.

These challenges must be carefully considered and strategically planned for when deploying AI in identity solutions, especially in a world without third-party cookies. The goal is to realize the potential benefits of AI without compromising fairness, accuracy, or financial viability. Achieving this balance is crucial for advancing marketing and advertising toward a future with more optimized and personalized ad experiences that adapt to user trends with ease.

As the digital identity solution industry evolves, integrating AI and machine learning into non-biometric solutions will be pivotal in enhancing user profiles and advertising campaigns. This integration will create solutions more attuned to user preferences, trends, and privacy rights. Strategic planning and careful implementation will be essential for overcoming these obstacles, positioning AI and machine learning as key drivers in the future of digital identity solutions and advertising campaigns.