As third-party cookies fade away, brands scramble to collect first-party data as a new Northstar to offer personalized services.
This post explores strategies for first-party and zero-party solutions for brands looking to embrace the trend. We see this as an unprecedented opportunity for brands to cultivate lasting relationships with their customers and enhance their competitive edge in a privacy-first landscape.
Game-like approach to collect first-party data
The most classic example is a form. Forms are a valuable technique for collecting highly structured data. The form's designer specifies the data's purpose and asks questions critical to the underlying service. Such data will only be used to adjust and balance the provided service.
On the other hand, forms have issues. They are not engaging for users, and they are also inflexible and resource-intensive. A new form must be designed and deployed for every latest information set. Lastly, the information collected by the form can expire or become invalid after some time.
In today's fast-evolving landscape, there's a pressing need for a dynamic approach to information gathering. This method must be adaptable to changing circumstances and engaging to capture the audience's interest. Adopting this innovative strategy enhances the quality and relevance of collected information, effectively meeting the diverse needs of stakeholders.
This is where game-like experiences shine. Take, for example, TikTok's UX design. The entire experience is designed around exploration and exploitation mechanisms. Users navigate and explore a vast content network. As they are served new content, their engagement (time spent on a post or interaction with the content) signals their interest in a cluster of topics. This information helps TikTok's algorithm understand which topics interest the user.
This approach applies to other businesses, particularly regarding user preferences. For instance, an interior design agency could develop a game-like experience to identify the styles, colors, and materials that users prefer. This differs significantly from current methods that merely ask for preferences, like Scandinavian or Mid-century. Most users enjoy a mix of styles, and their tastes evolve.
Benefits of collecting first-party data
The question of the benefits of 1P data is a context-specific issue. Established businesses with an existing customer base answer this question differently than do new startups.
However, the core value of first-party (1P) data is not increased monetization. We argue that 1P data is essential to remaining competitive and surviving in the current landscape.
Customers have become used to personalization. Some personalized services heavily use third-party cookies, an option that is fading away. So, suppose your business can offer a different level of Customer Experience (CX). In that case, you will soon see that your customers leave you for your competitors, who provide a more personalized service.
However, if you intend to present 1P personalization to boost short-term revenue, you might be surprised to learn that it will not achieve that goal. While your service can become more engaging and foster a lasting relationship with your customers, these benefits may not translate into an immediate increase in revenue.
This is unsurprising, considering that Double Jeopardy and the Law of Growth explain this nearly universal occurrence. Unfortunately, many marketing leaders overlook this crucial principle. I recommend reading the excellent book How Brands Grow by Byron Sharp.
Hence, brands must adjust their expectations about first-party personalization. Shifting the perspective from "this thing must make us more money" to "this thing is a matter of life and death."
But here is the truth. You will build a lasting relationship with your customers by providing a more fulfilling service. This will translate into long-term brand growth and increased long-term sales. By long-term, we mean beyond two years.
Trust is all you need
So why should customers share their data?
While the answer to this question may depend on the exact demographic and geographic differences, it is important to remember that trust is paramount to every attempt to provide personalized solutions based on first-party and zero-party data.
Brands should build customer trust to encourage sharing personal information. Thus, "TRUST" is the guiding principle for all personalization efforts.
Methods encouraging customers to provide data
Immediate and tangible value exchange
Customers must perceive the impact of first-party data immediately. The result must be tangible and noticeable as soon as they provide the data.
Imagine being asked to provide personal information for a sign-up or application. You submit your details, hoping for a personalized interface, enhanced features, or tailored offers. Yet, despite your efforts, nothing changes. This frustration leads you to question: why go through the hassle if there are no real benefits?
This relates to trust. By offering immediate value, users grasp the worth of their data, encouraging return visits and sharing with friends. Word of mouth is a vital growth mechanism for brands.
The regulatory landscape is constantly changing.
There are several data privacy laws and acts that govern data collection and storage, including GDPR, CCPA, and COPPA, which protect the privacy of children under 13 in the USA.
The regulatory environment is continually changing, and with the rise of GenAI, we can expect an increase in relevant laws in the near future. Therefore, it is crucial for brands to ensure full compliance with data privacy legislation. However, it is even more vital to stay ahead of regulations, demonstrating the brand's dedication to safeguarding user data, which is fundamental to building trust and transparency.
Keep opt in and out easy
While collecting first-party data is key to providing a personalized offer, we should remain mindful of the choices and give our customers options to opt in or opt out at their will.
Individuals can opt out of this experience and lose their personalized service if desired. They should have a simple way to request the deletion of their information. This approach can help brands build trust and demonstrate their commitment to protecting user data.
Finally, brands are obligated to delete user data if they choose to. Make this option easy, and implement rigorous processes for user data removal. To see additional nuances of this rule in the AI age, see the following section
Don't use first-party data in GenAI training
The "right to be forgotten" refers to an individual's ability to have their personal data erased or removed from certain systems, ensuring privacy and control over personal information. In the context of GenAI, this concept presents unique challenges:
- Data Removal Complexity: Once data is used to train a GenAI model, it's deeply embedded within the model's parameters. Unlike traditional databases, removing specific data points isn't straightforward.
- Model Retraining: To truly honor the "right to be forgotten," it might require retraining the model from scratch without the individual's data, which can be resource-intensive.
- Ethical and Legal Considerations: As regulations evolve, ensuring compliance with data protection laws such as GDPR is crucial. This means developing new methodologies to address this right effectively in AI systems.
- Technological Solutions: Researchers are exploring techniques like differential privacy and federated learning to mitigate these challenges and enhance data protection.Overall, balancing the right to be forgotten with the operational realities of GenAI remains a critical area of development and debate.
In today's digital landscape, brands that utilize advanced technologies such as Generative Artificial Intelligence (GenAI) and chatbots are faced with a critical ethical dilemma regarding the use of customer data. It is essential that these brands approach this issue with caution and avoid using customer data for training these AI models until a dependable and effective method for removing sensitive data from the models post-training is established.
Avoiding first-party data in GenAI training underscores brands' commitment to privacy and user trust. This approach aligns with ethical practices and prepares organizations for future regulations. Prioritizing transparency and consent fosters responsible development of data-driven technologies that safeguard user information.
Internal review committee
First-party data is a double-edged sword. When utilized correctly, it can bring significant benefits; however, if misused—even with good intentions—it can lead to public distrust in the brand.
In today's privacy-first era, professionals who are specifically trained in safeguarding customer rights and effectively managing first-party data play an essential role in various industries. As the digital landscape continues to evolve, these roles have become increasingly significant.
Consumers today are more informed and aware of their rights concerning data privacy, resulting in heightened expectations for transparency and accountability from brands regarding how their data is collected, used, and protected. Businesses must not only comply with regulations but also foster a culture of trust and reassurance among their customers. Therefore, having skilled professionals who understand the intricate dynamics of data privacy helps companies navigate the complexities of consumer expectations.
All processes or services that utilize first-party data must undergo thorough review and careful planning. A committed team that champions customer rights is crucial for coordinating these efforts.
This team should consist of data privacy experts, legal advisors, and data analysts who work together to:
- Assess Risks: Evaluate potential risks associated with data use and ensure compliance with data protection regulations such as GDPR and CCPA.
- Develop Guidelines: Create clear guidelines and best practices for data usage that align with legal requirements and ethical standards.
- Ensure Transparency: Maintain open communication with customers about how their data is used, fostering trust and transparency.
- Monitor and Audit: Regularly monitor data practices and conduct audits to ensure policy adherence and identify improvement areas.
- Provide Training: Offer ongoing training for employees to understand the importance of data privacy and their role in protecting it.
An internal review team for managing first-party data is a strategic advantage that builds consumer trust, enhances brand reputation, and ensures ethical data-driven initiatives. By prioritizing customer rights and transparency, organizations can maximize the potential of first-party data while maintaining public trust.
Measure Trust as Key Metrics
What are the key metrics for measuring the user experience in the first-party offering? This is an ongoing question, and depending on the context and industry, we might find different answers.
Classical metrics like engagement, customer spend, and retention measure outcomes but don't reflect the actual experience. Measuring outcomes only shows personalized impact through final metrics, which can be influenced by unrelated factors.
To understand first-party personalization's impact, brands must measure how these strategies enhance user experience. Trust is the vital currency in this relationship. Brands should prioritize measuring customer trust as a key metric, offering valuable insights into customer relationships.
For example, the willingness of users to share their data, or the percentage of users who answer certain optional questions. This will highlight how your customers perceive the value of the personalization, and therefore are willing to give you more data.
Transparency and honesty foster lasting relationships with your customers, encouraging their trust and willingness to share data. This information allows you to provide improved and personalized services, enhancing engagement and boosting monetization. As a result, your brand will experience long-term growth and gain a significant competitive edge over others.
Test and experiment on small sample, before large-scale deployment
Before rolling out a large-scale service offering based on 1P data, we recommend testing new ideas and services on a small customer sample, observing behavior, and collecting data about service satisfaction.
This is why developing a measurement strategy is key. It helps you evaluate the value of each new service, and it enables you to run A/B tests and holdout experiments to measure the causal impact of new services.
In the meantime, a dedicated committee comprised of legal, compliance, and data analytics experts can work collaboratively to ensure that the ethical standards and compliance requirements of these campaigns are thoroughly met.
This committee's comprehensive approach will not only guarantee adherence to applicable laws and regulations but will also foster a culture of accountability and transparency within the organization. By bringing together diverse expertise, the committee can provide invaluable insights to leadership when considering new offerings and initiatives. These insights will help in assessing potential risks, understanding customer perceptions, and aligning new products with the organization's values, ultimately driving informed decision-making that supports sustainable growth and maintains public trust.
Explainability is key for successful AI in first-party domain
Another key consideration when developing personalized solutions is which types of AI and machine learning (ML) algorithms we use to build the core components.
So, one approach is to develop an artificial neural network (ANN). When provided with a lot of data, the advantage of an ANN is its ability to learn non-linear patterns in the data. Your data science team must adjust the model's hyperparameters and train the system. You can then have an intelligent algorithm that can be used for prediction and personalization.
However, there are two main drawbacks of ANNs:
- Once training is complete, removing a data point is impossible. Refer to the section above on "AI and GenAI training data."
- ANNs lack explainability. You'll never understand why a particular input results in a specific output, making it a black box.
At ELIYA, we believe that these models need to be explainable. Only through transparent and elucidated models can brand leaders experiment with various aspects of the service, forecast outcomes, and grasp how each component affects customer behavior. This creates a crucial feedback loop that facilitates the optimization of the customer journey.
We believe using ANNs for data compression is best. Instead of the output, we utilize the ANN's hidden layers to obtain the embedding layer, similar to a barcode that encodes behavioral components in a condensed format.
Next, we input this embedding vector into an interpretable ML algorithm for predictions. This system's benefit lies in its semi-explainable nature, allowing us to harness the strengths of deep learning and neural networks, including non-linearity.
Final Remark
We are living in an exciting time. Brands that adopt a strong first-party strategy are the winners of the future. They can enhance their services and offerings without violating user trust and privacy. These brands can build sustained and long-term relationships with their customers and enjoy great growth for a long time.
First and zero-party data are double-edged swords. They can enhance customer experience but also damage brand reputation if mismanaged. Brands should be diligent with first-party data applications. An internal committee must oversee these efforts to ensure compliance, ethics, and customer privacy.
At ELIYA, we are excited about the future of first-party data. We invite visionary brands to partner with us in navigating this timely and critical matter. Our consulting service is designed to empower you with the confidence and tools needed to maximize the value of your data.
Utilizing first-party and zero-party data gives your brand a competitive edge and fosters innovative strategies that differentiate you in the market. Transform your data into a powerful asset; let us guide you to success!
