UX 3.0: modern UX research methods for fintech

In 2024, in the evolving landscape of fintech, e-commerce, and adjacent digital service sectors, understanding user needs and behaviors is critical for maintaining a competitive advantage. As these sectors adopt new technologies and adapt to shifting user expectations, we at Markswebb observe the emergence of several advanced UX research methods. These methods are particularly relevant within the UX 3.0 framework, specifically in areas such as developing ecosystem-based experiences, innovation-enabled experiences, AI-enabled experiences, and human-AI interaction-based experiences. Markswebb researchers are applying these new methods to stay prepared for the future of user experience. Let us share our insights from this journey - we'll show you examples from our own case studies where these new methods have been successfully applied.

Are you ready for the new demands of UX 3.0? We are. We stay ready by constantly updating our toolkit to reflect the latest user practices in fintech and across the broader landscape of digital services worldwide. Our resources include two dynamic databases: the Best Mobile Banking Apps Database and the UX Problems Guide. These databases are updated quarterly and feature hundreds of user journeys, carefully illustrated and analyzed by our researchers. We would be glad to share our findings with you - just get in touch!

UX evolution: stages

Let's first consider the conditions and drivers that have led to the need for new methods in user experience design. Understanding the evolution of UX practices reveals how technological advances, evolving user expectations, and new design approaches have shaped the field and set the stage for the current demands of UX 3.0.

User Experience evolution can be divided into three stages, each shaped by technological shifts, changing user needs, and new design approaches:

1. UX 1.0: The exploring stage (Late 1980s - 2007) Focused on usability, this stage aimed to create intuitive and functional digital interfaces driven by PC and internet technologies. It emphasized basic user needs, such as accessibility and efficiency, through usability engineering, user research, and interface testing, laying the groundwork for user-centered design.
2. UX 2.0: The growing stage (2007 - 2015) With the rise of mobile internet and smart devices, UX expanded beyond usability to cover diverse touchpoints across entire business processes and platforms. This stage promoted a holistic view of user interaction, emphasizing cohesive experiences throughout the product lifecycle, from development to post-launch.
3. UX 3.0: The maturing stage (2015 - Present) In the intelligence era, UX integrates AI, big data, and advanced technologies to design adaptive systems and human-AI interactions. It now addresses complex factors like ethics, user autonomy, and collaborative interfaces. UX 3.0 requires a systemic, interdisciplinary approach, adapting to emerging technologies and diverse user needs to foster innovative and ethical experiences.

UX 3.0 paradigm framework: key aspects

The UX 3.0 paradigm provides a framework for designing user experiences in the age of artificial intelligence, focusing on four key areas:

1. Ecosystem-based experienceShifts from isolated product interactions to a holistic approach, integrating diverse devices, platforms, and environments (e.g., smart homes, cities). The aim is seamless, cohesive user experiences across an interconnected ecosystem.
2. Innovation-enabled experiencePositions UX as a driver of product differentiation and innovation, using emerging technologies and creative design solutions. It leverages data-driven insights and new interaction methods, fostering a culture of continuous innovation.
3. AI-enabled experienceUses AI to enhance the design process and user experience, employing tools like machine learning for real-time personalization, interface design, and testing. The goal is to improve efficiency, transparency, and alignment with user needs.
4. Human-AI interaction-based experienceFocuses on the evolving interactions between humans and AI, designing interfaces that support intuitive, emotionally intelligent engagement. It emphasizes trust, transparency, and ethical considerations in human-AI collaboration.

These aspects guide UX practices to be integrated, innovative, adaptive, and ethically aligned with the realities of human-AI collaboration.

Modern UX research methods for fintech and digital services

Now, let's look at the methods themselves, and we will illustrate them with practical examples from our projects and case studies we have completed.

1. Ecosystem-based experience research

Modern UX research for fintech now extends beyond individual product usability to consider the entire user journey across multiple touchpoints and platforms. This approach requires a comprehensive understanding of how users interact within an interconnected ecosystem of devices, services, and channels.

Methods:

• Cross-platform user journey mapping: This method involves tracking and analyzing user interactions across various devices (e.g., desktop, mobile, wearables) and services (e.g., digital wallets, payment gateways, banking apps). By identifying friction points and opportunities for seamless transitions, UX professionals can optimize the entire ecosystem's experience.
• Technological ecosystem testing: Fintech services increasingly rely on integration with various platforms, such as cloud services, third-party APIs, and microservices architecture. Testing within these interconnected environments ensures consistent user experience, regardless of the entry point or device.

Case study example:

How we lead digital transformation across all banking services

This case study focuses on enhancing the digital experience across various banking channels, such as mobile apps, web platforms, and physical touchpoints. By utilizing cross-platform user journey mapping, Markswebb identified friction points in the ecosystem and implemented solutions to optimize transitions between devices and services, ensuring a consistent user experience throughout the entire digital banking ecosystem.

2. Innovation-enabled experience research

In fintech, where innovation is a driving force for differentiation, UX research focuses on uncovering latent user needs and aligning them with emerging technologies to deliver unique, high-value experiences.

Methods:

• Predictive analytics and data mining: Utilizing big data analytics, researchers can identify patterns in user behavior, transaction data, and feedback to anticipate future needs and preferences. This method supports the development of innovative features, such as predictive financial planning tools or personalized investment advice platforms.
• Experience prototyping and concept testing: Before full-scale implementation, innovative concepts are prototyped and tested with target users. This process allows for rapid iteration based on real user feedback, ensuring that new features align with user needs and preferences while maintaining usability standards.

Case study example:

How we innovate chatbot experiences

In this project, Markswebb used experience prototyping and concept testing to develop and refine new chatbot features for a bank. The team created prototypes of conversational AI interfaces and conducted iterative testing with real users to align the new features with user needs and preferences while ensuring usability. This research helped innovate the chatbot's experience, making it more engaging and user-friendly.

3. AI-enabled experience research

AI technologies have transformed UX research by providing tools for deeper insights into user behavior and preferences. In fintech, AI-enabled research methods help create personalized, real-time experiences that cater to individual user profiles.

Methods:

• Intelligent user behavior analysis: Leveraging machine learning algorithms, researchers analyze vast amounts of data from user interactions to identify micro-behaviors that might indicate friction points or areas for improvement. For instance, detecting patterns in how users navigate financial dashboards can help optimize layout and content delivery.
• Adaptive UX testing: AI-driven tools facilitate adaptive testing, where different user segments are exposed to varied design elements based on real-time data. This method enables continuous optimization of interfaces, ensuring that the user experience evolves in response to changing behaviors and preferences.
• Conversational AI testing: With the rise of chatbots and virtual assistants in fintech, UX research focuses on improving human-AI interactions. Testing involves real-time simulations to assess the effectiveness of AI responses, emotional intelligence, and the ability to handle complex queries or financial transactions.

Case study example:

How we turned a good chatbot into a great one

Markswebb leveraged conversational AI testing to improve a financial chatbot's effectiveness. The team analyzed user interactions with the chatbot, focusing on the AI's ability to provide accurate responses, demonstrate emotional intelligence, and handle complex queries or transactions. The insights gained from these tests were used to optimize the chatbot's conversational flow, leading to increased user satisfaction and engagement.

4. Human-AI interaction-based experience research

As digital services increasingly incorporate AI, understanding the nuances of human-AI interaction is crucial. The goal is to enhance trust, transparency, and satisfaction in human-AI collaboration, particularly in contexts where users make critical financial decisions.

Methods:

• Explainability and trust evaluation: This method involves assessing how well AI-driven fintech services communicate their decision-making processes to users. Through usability tests and user feedback sessions, researchers evaluate the clarity of AI-generated explanations and their impact on user trust.
• Human-AI collaboration mapping: In environments where humans and AI work together (e.g., automated trading platforms or robo-advisors), mapping the interaction flows between human users and AI systems helps identify points of friction, miscommunication, or inefficiency. This method supports the development of interfaces that foster effective collaboration and decision-making.

Case study example:

How we humanised chatbot in digital wallet

This case study involved human-AI collaboration mapping to understand how users interacted with an AI-driven digital wallet assistant. The goal was to enhance the chatbot's ability to provide transparent and trustworthy financial guidance. Markswebb conducted usability tests and gathered user feedback to assess the clarity of AI-generated explanations and their impact on user trust. This research helped refine the digital wallet's interface, promoting better human-AI collaboration.

Well, let’s create something remarkable?

As we move forward in the age of UX 3.0, we invite you to partner with us on this journey of discovery and innovation. At Markswebb, we are not just observing the shifts in user experience - we are actively shaping them with our cutting-edge research and practical insights. Whether you are looking to enhance your digital services, create seamless user journeys, or leverage AI for a more personalized experience, we have the expertise and tools to help you succeed. Let’s explore the future of user experience together!