Generative AI: What Is It and How Can It Help With User Research?

Nov 29, 2023 | User Research | 0 comments

Understanding user behaviour and preferences is of the utmost importance in the ever-changing field of user experience design. At this stage, user research becomes relevant, providing valuable insights that can inform design decisions.

The problem with tried-and-true approaches to user research is that they can be tedious. Fortunately, this is where generative artificial intelligence comes into play as an emerging technological paradigm with the potential to revolutionise the way that we conduct user research.

Currently, generative AI is being touted for its potential to enhance user research rather than replace UX professionals. Generative research has the ability to handle repetitive tasks that UX researchers often spend substantial time on, such as, data collection, and transcription. This may free UX researchers to focus on activities such as data analysis, interpretation, and even strategy development.

In this article, we will investigate the idea of generative AI, its history, and the ways in which it has the potential to alter the way user research is carried out in user experience design.

What is user research?

Before delving into generative AI, let’s first understand the concept of user research. User research is a fundamental component of the UX design process, involving the gathering of insights and understanding the needs, preferences, and behaviours of users.

By conducting user research, designers can make informed decisions and create products that genuinely meet the needs of their target audience.

What is generative research?

Generative research, also known as exploratory research, is a qualitative research methodology aimed at uncovering users’ problems and discovering new opportunities for innovative solutions. This is not to be confused with “generative AI”

Going beyond simply understanding user preferences and behaviours, this discipline delves into the deeper aspects of users’ lives, motivations, and frustrations. It helps designers gain a comprehensive understanding of their users and enables them to create products that address real-world problems.

Read: Generative Research: All you need to know

What is generative AI?

Generative AI is an emerging branch of artificial intelligence that leverages algorithms to generate new content, such as text, images, or even entire user experiences. In practice, generative AI learns from existing artifacts to generate new artifacts at scale in a programmatic manner that reflects the characteristics of its training data.

It achieves this by leveraging machine learning (ML) techniques to analyse vast amounts of data and create new, relevant outputs based on patterns and insights derived from the data.

In fact, Generative AI has incrementally evolved significantly over the years, thanks to advancements in technologies like generative adversarial networks (GANs) and transformer models.

These innovations have made generating highly realistic and contextually relevant content possible, pushing the boundaries of what AI can achieve across image generation and natural language processing domains.

Benefits and uses of using Generative AI in UX research

Generative AI offers a wide range of benefits and applications in the field of UX research. Let’s explore some ideas that it can enhance the research process and improve the overall user experience:

Data augmentation

Data scarcity can be challenging in UX research— especially when targeting niche user segments. This can be a very time consuming and cost intensive research exercise. Generative AI can address this systemic issue by generating synthetic user data to supplement real-world data. In practice, it can create realistic user profiles and behaviours as a way to manage cost and timeline, however, synthetic user data will still need to be validated with real world user insights eventually.

User persona creation

User personas play a crucial role in UX design, helping designers empathise with their target audience.

Generative AI can automatically generate detailed user personas based on existing data from user research, saving time and effort. It can be leveraged to enhance persona profiles with AI-generated content, such as images or typical user queries.

User behaviour simulation and analysis

Generative AI can be exploited to simulate potential user behaviour based on existing data from user research, allowing designers to anticipate user needs and optimise designs proactively. By modelling potential user pathways, it helps designers to identify points of friction or interest and make informed design decisions.

Rapid prototyping

The prototyping stage of the design process is vital because it gives designers the opportunity to iterate on their concepts and hone their designs. Generative artificial intelligence can be deployed to speed up the prototyping phase by rapidly generating design mockups based on the preferences of the user— enabling designers to test multiple design variations without extensive manual effort, saving time and resources.

Scenario generation

Creating realistic user scenarios or stories is crucial for understanding user needs, challenges, and motivations. Generative AI can assist in generating these scenarios, enabling designers to conduct role-playing exercises or guide qualitative research. By immersing themselves in these scenarios, designers can gain deeper insights into user experiences.

Predictive analysis

Generative AI can predict future user needs or trends based on current and past data, enabling designers to adjust research strategies proactively. By leveraging AI-driven predictions, designers can anticipate user behaviour and tailor their designs to meet evolving user expectations.

Enhanced A/B testing

A/B testing is a common practice in UX research to compare different design variations. Generative AI can be utilised to quickly generate multiple design variations for split testing to provide more granular insights into user preferences. As a result, allows designers to gather data-driven insights and make data-informed design decisions.

Content generation

Content creation is a crucial aspect of UX design, and generative AI can automate this process. Generative AI can be exploited to automatically generate content, such as text or images, tailored to user personas. By leveraging AI-generated content, designers can enhance user engagement with dynamic and relevant materials.

Enhanced survey and questionnaire design

Surveys and questionnaires are valuable tools for gathering user feedback. Generative AI can be leveraged to ‘synthetically’ generate survey questions to give the UX researcher ideas on questions to ask as well as optimising the survey flows. Furthermore, Generative AI can be used to test run your survey questions, predicting if survey takers would understand your survey questions easily. Finally, Generative AI can also be helpful when it comes to crunching the numbers, analysis and providing report summary ideas.

Accessibility improvements

Designing accessible products is essential for ensuring inclusivity, and generative AI can help improve accessibility by generating alternative content formats, such as audio descriptions for images. This ensures a more inclusive user experience for all demographics, regardless of their abilities.

Innovative design solutions

Generative AI encourages designers to explore novel design solutions that might not be immediately obvious to human designers. By pushing the boundaries of traditional design thinking, generative AI opens up new possibilities and fosters innovative approaches to problem-solving.

Also read: How you can use Generative AI tools & applications for your research workflow

Conclusion

Generative AI has emerged as a transformative force within the realm of UX research, ushering in a new era of possibilities.

With its remarkable capacity to mimic user behaviours, fabricate content, and elevate the intricacies of design workflows, it bestows upon designers an array of infinite potentials.

It facilitates UX designers to amplify their research endeavours, refine their design blueprints, and curate bespoke user interactions.

Nonetheless, it’s crucial to bear in mind that generative AI should be regarded as a collaborator, augmenting and enriching human expertise rather than rendering it obsolete. The quintessential human elements of touch, creativity, and analytical thinking embedded within UX researchers remain irreplaceable, forming the bedrock of deciphering user requirements and sculpting unparalleled user journeys.

Therefore, the latent capabilities of generative AI warrant earnest consideration.

Embrace this technological tide, and let it bestow you with the authority to forge exceptional user experiences that captivate and engross your intended audience.

In today’s technology-driven landscape, both human ingenuity and generative AI will be invaluable to deliver better user experience to customers.

Generative Research: All You Need To Know

Aug 22, 2023 | User Research | 0 comments

Human-centred research should always be an integral component of any UI/UX design process than isolated from it. Furthermore, human-centred research approaches should always be flexible, allowing appropriateness to be the key determining factor in the selection of methods for information collection and the testing of ideas.

Generative research is an approach that focuses on developing a deeper understanding of users to uncover opportunities for solutions and innovation.

Sometimes referred to as exploratory research, discovery research or problem-space research, generative research helps organisations when they don’t even know what problem to solve for customers.

This article seeks to delve into all things generative research, its benefits as a discipline as well as the execution methods of its underlying principles.

What is generative research?

Generative research is essentially a qualitative research methodology expressly designed to help reveal ordinary users’ problems in order to find viable solutions.

This research methodology often entails interviews with customers, exploratory research, and observation of customers to comprehensively learn about who they are, what they do, why they do what they do in a specific way, and how they do it.

It can even go further to unpack what frustrates them, what really makes them happy, and in what contexts they take a specific action, etc.

Fundamentally, generative research seeks to deeply “generate” a deep understanding of who one’s customers are (as human beings, not as users), and what they experience in their daily lives.

For example, its methodical approach to interviewing goes beyond tactical inquiries (as typically seen with usability testing) to focus on digging into a person’s identity beyond their interaction with a product.

Why is generative research important?

The strategic approach that underpins generative research and focuses on direct collaboration with users enables UI/UX developers to uncover the mindset of users regarding a particular problem to later translate it into a feasible and executable solution.

This approach does not perceive users as passive consumers but as active stakeholders in the design process of a product. It reveals to product developers what users are thinking of at a given moment when using a product, removing biases they typically have.

This is because developers sometimes get stuck in a box when creating unique products and can become quite short-sighted.

In practice, generative research helps break those biases to devise solutions to a user’s exact problems.

In fact, a Nielsen Norman Group survey revealed that 83% of organisations that engaged in generative research on their last projects reported higher success rates in contrast to others that did not.

Types of generative research

Generative research seeks to get prospective users to tell stories about their lives that go beyond a product or service, revealing rich information about their overarching needs and motivations.

The different ways generative research achieves this outcome in contrast to other research methodologies can be summarised below:

Generative vs formative research

Generative research processes focus on identifying new hypotheses for a business as well as building an understanding of their domain.

In contrast, formative research methodologies focus on testing new hypotheses before and during the delivery of a product.

Consider this scenario. A startup company wants to launch a new fitness app but isn’t sure about the features and functionalities users would find most beneficial. They conduct interviews and surveys with potential users to gather insights about their fitness habits, preferences, and pain points. Based on the feedback, they identify a need for a feature that offers personalised workout plans based on individual health goals and constraints. This is generative research.

Now, after developing a prototype of the fitness app with the personalised workout plan feature, the startup wants to ensure it meets user expectations and is easy to use. They conduct usability testing sessions where users interact with the prototype. During these sessions, they discover that users are confused about setting up their initial health goals in the app. Based on this feedback, they refine the user interface and instructions to make the setup process more intuitive. This is formative research.

In essence, while generative research helped the startup identify a potential feature (personalised workout plans), formative research helped them refine and perfect it based on real user feedback.

Generative vs evaluative research

The objectives of generative and evaluative research differ but are both essential. Generative research defines the problem one would like to design a solution for. On the other hand, evaluative research helps developers to evaluate an existing design.

Furthermore, while generative research helps one uncover opportunities to advance customer-centric solutions, evaluative research is more product-centric.

Nonetheless, though disparate in their goals, generative and evaluative research methodologies go hand in hand. Generative research helps product developers to find the problems they need to solve, while evaluative research ensures they are solving these problems well.

How to determine if generative research is needed?

As already mentioned, generative research methodologies aren’t product-focused. This means that you will not test a hypothesis or gather feedback on a product prototype. Rather, you’ll focus on understanding a customer’s identity to uncover their unmet needs you can fulfill.

It is important to initiate a generative study at the very beginning of a product-development process. This helps to drive product direction, generate new ideas and exploit any innovative market opportunities.

Furthermore, its imperative to iteratively assimilate generative research into every stage of the product development lifecycle.

Generative research methods

Generative research methodologies help organisations move from a product-centric ethos to a user-centric one.

Thereby, it enables them to uncover rich insights about customer behaviours, opinions, and motivations to design better products that solve real-world problems for their users.

The different generative research methods one can exploit are:

1. Interviews: conducting in-depth interviews to gather rich qualitative data

During user interviews, multiple participants are engaged on a one-on-one basis, either remotely or in person. Subsequently, they are asked approximately 5-10 open-ended questions that strategically encourage them to share relevant opinions, stories and experiences about a product, topic or problem.

These interviews typically provide a wealth of qualitative information about one’s users that organisations can harness to generate product ideas and solutions.

Generative research questions

Generative research questions are designed to generally explore many possible vectors that focus on one key problem. This qualitative activity is designed to generate new ideas, understandings, and directions.

2. Observations: leveraging ethnographic and contextual observations

Ethnographies is a technique where one speaks with and critically observes participants’ social behaviours, interactions and perceptions as they execute tasks in their natural habitat, like at home or in the office.

Ethnographic studies help build empathy with users, enabling product designers to better understand the context of users’ daily lives and why they behave as they do.

This information can be leveraged to uncover potential product limitations and problems and devise solutions that address those exact customer needs.

3. Workshops and Co-creation: involving participants in idea generation and collaboration

Workshops enable researchers to group people together to generate executable solutions to rectify user challenges.

Typically, during workshops, participants are provided with some background information on the challenges at hand, and then given ample time to brainstorm and suggest potential solutions.

Workshops can also be employed to get feedback on potential solutions that product developers are already considering.

4. Diary Studies: capturing longitudinal data through personal journals or diaries

A diary study allows one to collect qualitative data about user habits, behaviours and experiences over a defined period of time. While interviews and surveys capture user perceptions during an exact moment in time, diary studies are rather longitudinal to attain a more in-depth view of users.

During diary studies, researchers ask participants to submit their thoughts and feelings over a specific time period. This is usually done via video logging, enabling them to analyse observable behaviour that words alone cannot capture.

At the end of the diary study, the findings are analysed to uncover trends in customers’ long-term behaviour, attitudes, habits and motivations.

How to plan a generative research study?

When seeking to engage in generative research, here are the common steps that one can follow:

1. Define the problem at hand

Before commencing, it’s imperative to clearly define the problem to determine what generative research approach best fits your intended outcome.

If your problem dictates discovery, exploration, and understanding, then generative research is probably the right approach.

2. Understand the objectives

After defining the problem, you will need to understand the objectives of the users. To do this, create a research plan to keep you on track and serve as a guide to help you focus your tasks. Your plan should clearly state the objectives of the research exercise, detail how you’ll select participants, and even determine incentives for the participants.

3. Participant selection

Afterwards, you’ll start recruiting participants for your generative research study to get the data you need to address your research questions.

4. Choose the research methods

After participant selection, you’ll proceed to choose the appropriate generative research methodology that aligns with your objectives, timelines, and overall goals.

5. Create a discussion guide

After selecting a research methodology, you’ll need to create a discussion guide for direction when engaging participants in order to meet your objectives or moderate your discussions whilst keeping them on track.

6. Conduct a generative research

In the last phase, you’ll proceed to commence your moderated generative research using the methods mentioned earlier. These include interviews, observations, workshops or diary studies.

You can employ transcription tools, Google Meet and Zoom and also create a user research repository to store all your insights.

Data Analysis in Generative Research

After performing the generative research exercise, you’ll need to synthesise and analyse the data at hand. You can execute this manually; however, it will probably be time-consuming and cumbersome.

As such, you can use tools like UserTesting to automatically discover relevant patterns and trends related to participants’ behaviours and motivations to create user-centric experiences.

You can also create affinity diagrams to discover the most common customer pain points using tools like Condens.io.

Real-World Applications of Generative Research

Generative research is currently being exploited in different industries like technology and healthcare to gain a deeper understanding of users’ problem space.

For example, it is being used in the technology sphere to engineer new digital products and services and uncover new areas for innovation.

In healthcare, generative research is being employed to comprehensively understand patients’ needs and experiences for healthcare companies to design products that meet the unique needs of their patients.

Conclusion: Future of generative research

All things considered; generative research helps organisations define users’ problems at the very beginning of the product cycle.

Based on the insights garnered, product designers can synthesise them and understand patterns to resolve user challenges.

As such, generative research will continue to be relevant to aid researchers in finding opportunities for constant innovation when iteratively creating solutions to common pain points.

With the advancement of AI, virtual reality and augmented reality, we foresee that generative research will be branching out into immersive environments. This may foster cross-functional collaboration to help to break down user-designer silos and create a more innovative and collaborative culture.

Reach out to us at Netizen Experience for conducting generative research for your next project!