Automating the mundane: 4 AI tools to improve UX research

The focus of product and UX leaders on user research is stronger now than ever. And we all recognize the critical role UX research plays in crafting exceptional user experiences that truly meet customer needs.

However, UX designers often encounter major hurdles throughout the research process. Repetitive tasks and time-consuming analysis can further bog things down.

Amidst these challenges, powerful AI tools have been developed to optimize UX research for greater ease, scalability, and accuracy. These AI tools offer a solution by automating some of the biggest bottlenecks in UX research.

In this article, we discuss these common bottlenecks, examine the role of AI for UX research and explore the best AI tools to better UX research.

Challenges in UX research

Let’s explore the most common challenges UXers face when conducting research:

Choosing the right research method for your needs

Selecting the most appropriate research method can be a huge stumbling block for UX designers. The allure of trendy new methodologies can often overshadow the tried-and-true methods that best suit the research question at hand.

A common mistake when conducting UX research is relying solely on surveys for quantitative insights when user interviews or usability testing might provide richer, more contextual data.

The key lies in understanding the strengths and limitations of different methods.

AI can help you identify the right research method based on your research project’s requirements and limitations. It can also compare various research methods and let you know which best suits your needs.

For example, surveys excel at gathering quantitative data from a large sample size, while user interviews delve deeper into user needs and motivations. Usability testing, on the other hand, provides invaluable insights into user behavior within a specific product context.

Drafting the perfect survey and interview questions with minimal biases

How you phrase questions or interact with participants in UX interviews can influence their responses. Mitigating this bias requires careful design of research questions and a neutral facilitation approach.

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While this can ordinarily be quite tricky, AI can help you develop neutral survey and interview questions that limit biases.

Transcribing lengthy interviews and coding qualitative data

One of the most time-consuming tasks in UX research is transcribing lengthy interviews and coding qualitative data.

However, with the advent of AI-powered tools like Otter.ai, this process can be automated. These tools can transcribe audio recordings and assist with coding themes and sentiment analysis, freeing up valuable time for designers to focus on interpreting the data and extracting actionable insights.

Analyzing large datasets can be tricky and nearly superhuman

Making sense of vast amounts of qualitative and quantitative data can be overwhelming, especially for smaller UX teams. Identifying key themes and insights requires strong data analysis skills, typically far removed from a UX designer’s skill set.

With AI, UXers can automatically generate insights and visualizations from complex datasets. These can be used to quickly identify trends and communicate research findings effectively, even to nontechnical stakeholders.

Top 4 tools in AI for UX research

Let’s examine the best AI tools for UX research and how you can use them to improve the UX research process and overcome the research challenges we’ve highlighted.

1. ChatGPT and Gemini for research brainstorming

Generative tools like ChatGPT and Gemini offer a unique value proposition for UXers in the early stages of their research planning.

These two AI tools can significantly improve your research planning and free up time to focus more on interpretation and design decision-making. They can also help you deliver UX that resonates more profoundly with your target audience.

Here are some ways you can use these two AI for UX research:

• Brainstorm and refine research questions — If you’re stuck formulating a clear research question, you simply have to provide some background information about your project and target audience, and AI tools like ChatGPT or Gemini can suggest relevant research questions
• Analyze your research questions and find ways to refine them for clarity and direction — Using AI for UX research ensures that your research questions target the most relevant user behaviors to gather data that directly addresses your research goals
• Figure out the best methodology — You may use ChatGPT or Gemini to determine what research method works best to help you meet your research goals once you have your research questions
• Highlight any gaps in your research study — You may have missed or overlooked gaps in your research plan, and using AI for UX research can help you bridge these gaps

Logrocket Video Gemini AI tool in UX Research

Here is how I use Gemini for brainstorming in UX Research.

2. Otter.ai for interview transcription

UX research often involves capturing user insights through interviews, surveys, and user testing sessions. Manual transcription of audio recordings is time-consuming and prone to errors.

Here’s where you can leverage AI for UX research. Otter.ai can automatically join your meetings to record, take notes, and utilize AI to streamline transcription. Here are some ways Otter.ai does this for you:

• Automatically transcribe interviews and user testing sessions — Otter.ai frees researchers to focus on observing user behavior and taking contextual notes during interviews with its real-time, editable, and searchable transcriptions
• Create summaries of the discussion that can be easily shared with relevant stakeholders — By creating summaries out of interviews — live or pre-recorded — Otter.ai significantly reduces your post-interview workload, so you can dedicate more time to analyzing user data. You can also highlight and annotate the transcriptions and share notes with involved stakeholders
• Enable researchers to directly probe the transcription via AI chat — Otter.ai allows researchers to interact with the transcription through an AI chat interface, significantly improving data accuracy compared to manual note-taking methods and ensuring the integrity of captured user feedback

3. Typeform for building smarter forms

Typeform will help you create surveys and forms that can be utilized for better insights. It has AI capabilities that can enhance the form-building and data-collection experience. Here’s an overview of Typeform’s most helpful AI features:

• Build and design your form — Simply describe your goals for the survey, and the Typeform’s AI will suggest relevant question types and answer options. This jumpstarts the form-building process, helps you cure blank page syndrome, and ensures you collect the right data
• Get contextual options for your multiple-choice questions — Typeform’s AI can automatically generate multiple-choice and dropdown answer options based on the keywords you enter for your question. This can help save time spent scrambling for answer choices and ensure your questions provide users with clear and relevant response choices
• Easily personalize the survey design and aesthetic — Typeform’s AI can scan your website and suggest design elements that match your brand. It can also suggest tailored branching logic based on your form’s content, personalizing the survey based on user responses. This ensures users see only relevant questions, creating a more engaging experience
• Get “smart insights” — You may also use Typeform’s AI for UX research in the post-survey phase. Typeform automatically converts survey responses into charts and graphs. This saves you time on data visualization and manipulation and helps researchers instantly identify patterns and key points. Additionally, it highlights user sentiment in responses, enabling UXers to gauge satisfaction and identify areas for improvement quickly

Logrocket Video Typeform AI in UX Research

Uploaded by Iwalola Sobowale on 2024-06-21.

4. Kraftful for user feedback analysis

Kraftful has AI capabilities specifically designed to enhance user feedback analysis for product development. All you have to do is select and integrate the data source, and Kraftful does all the work for you. With Kraftful, you can pull data from your app reviews, support tickets, call transcripts, and website reviews.

Here’s a breakdown of how Kraftful utilizes AI-powered text analysis:

• Sentiment analysis of user feedback — Kraftful can automate repetitive tasks like categorization and sentiment analysis. The AI can identify positive, negative, and neutral sentiments within user comments, helping product teams faster understand overall user satisfaction and areas of concern
• Topic modeling that goes beyond simple keyword searches — The AI of Kraftful utilizes topic modeling to uncover hidden patterns and recurring themes within user feedback and interview data. This helps product teams identify unexpected trends and user pain points that might’ve been missed in traditional analysis, empowering product teams to make data-driven decisions
• Automated categorization of user feedback—Kraftful also offers AI for UX research to categorize user feedback into predefined categories like bugs, feature requests, or usability issues. This helps save time and effort for product teams when analyzing feedback

Logrocket Video Kraftful AI Tool in UX Research

Uploaded by Iwalola Sobowale on 2024-06-21.

Bonus: Insight7 for a custom vocabulary

Here’s a bonus tool that uses AI to better your UX research — Insight7. Insight7 offers industry-specific accuracy and customization for your UX research, just like Kraftful, but with a cherry on top.

For users in legal, financial services, and healthcare, where precise transcript accuracy is critical, Insight7 offers a ‘custom vocabulary’ feature. With this, you can upload domain-specific terminology to ensure the transcripts capture the nuances of your industry jargon.

Additionally, Insight7 provides pre-built templates to enhance framing insights for various research areas like market research, marketing communications, and customer research.

Things to note when using AI for UX research

Using AI for UX research can offer massive benefits, but you should be aware of the limitations and considerations involved:

• While AI is a powerful tool, it cannot be taken at its face value. It can’t replace the critical thinking and understanding of research methodologies that a skilled researcher brings to the table. Always review AI suggestions and calculations critically and ensure they align with your research goals
• AI algorithms are trained on data sets, which can sometimes be biased. Be mindful of potential biases in the data used by AI tools
• The quality of data provided to the AI tool directly impacts the quality of AI analysis. Product teams need to ensure they gather clear, concise, and unbiased user feedback to get the most out of the AI’s capabilities

End note

If you can leverage these AI tools strategically while maintaining your research expertise, you’ll be able to significantly accelerate your research process. You’ll be equipped to gain deeper user insights faster and make data-driven decisions that lead to user-centered design and successful product experiences.

However, it’s important to remember that AI is a powerful assistant, not a replacement for your expertise.

The ability to understand user needs, interpret research findings creatively, and translate insights into actionable design solutions remains the domain of skilled UX researchers and designers.