How To Use AI to Increase Efficiency in Your User Research

Paul Boag, an expert in user experience (UX) design and conversion rate optimization (CRO), shares how AI has revolutionized his work. He explains that AI has “made my job a lot easier,” offering tools that simplify user research and provide new ways to gather and analyze data. In this blog, we’ll explore how AI is transforming user research, the advantages it offers, and why user research is crucial for UX and CRO. We’ll also break down the step-by-step processes Paul uses to integrate AI into his workflow.

Why User Research Matters in UX and CRO

User research is key to creating effective UX designs and improving conversion rates. By understanding what users need, how they behave, and what challenges they face, businesses can make their websites and apps more user-friendly and effective.

In UX, user research helps designers build interfaces that are intuitive and enjoyable. Boag points out that “user research can be difficult and time-consuming… because it’s challenging to understand and interpret what you’re learning as a user researcher.” Despite these challenges, the insights gained are invaluable for making designs that truly meet user needs.

For CRO, user research identifies the barriers that prevent users from completing desired actions, like signing up or making a purchase. Boag uses a simple yet powerful approach: “If you decided not to sign up today, it would be helpful to know why.” This question uncovers objections and concerns that businesses can address directly, improving their chances of converting users.

Without thorough user research, UX and CRO efforts can miss the mark, leading to designs and strategies that don’t resonate with users. By making user research a priority, businesses ensure that their decisions are based on real user insights, not assumptions.

Common Challenges in User Research

Although user research is essential, it often comes with significant challenges:

1. Data Overload: Platforms like Google Analytics and Hotjar provide large amounts of data, but making sense of it all can be overwhelming. Boag notes, “Whether it’s Google Analytics, whether it’s Hotjar, wherever you’ve got a large amount of data, working through and getting understandings and insights… can be really difficult.” This can make it hard to focus on what’s most important.
2. Analyzing Open-Ended Surveys: Surveys with open-ended questions give valuable qualitative insights but can be intimidating to analyze manually. Boag says, “If you haven’t run a survey and have an open-ended question and you’re faced with hundreds, if not thousands of answers… that is intimidating.” Sorting through this data to find common themes is time-consuming and complex.
3. Handling Interview Transcripts: User interviews are rich in insights but managing the transcripts can be a challenge. Boag describes the difficulty of finding specific comments or recurring themes: “You’re then faced with transcripts of these conversations. Where did someone say that? And I’m sure I remember this, but I can’t remember where or how to find it.” Manually reviewing transcripts is a tedious process that can delay research outcomes.

AI provides solutions to these challenges by automating data analysis and simplifying the process of extracting insights from user feedback.

The Advantages of Using AI for User Research

AI enhances user research by speeding up processes, improving accuracy, and enabling deeper analysis. Here are some key benefits of using AI in user research:

1. Automated Data Analysis: AI can quickly process large datasets, whether from surveys or analytics tools, to identify patterns and trends that might be missed by human analysis. Boag mentions, “AI is an amazing tool that helps you… understand data.” This automation saves time and allows researchers to focus on drawing meaningful conclusions.
2. Enhanced Qualitative Analysis: AI’s natural language processing capabilities are especially useful for analyzing open-ended survey responses and interview transcripts. Boag uses ChatGPT to analyze survey responses and rank common themes, which makes it easier to see what’s most important. He notes, “It’ll take a few minutes to look through all of those answers… with Ai, it’s not a few minutes, but a few seconds.” This speed allows researchers to get valuable insights quickly.
3. Scalability: AI makes it possible to scale research efforts without increasing the workload. Boag emphasizes that AI tools enable researchers to “do so much more user research than you’ve ever done before,” because the tools handle much of the heavy lifting. This scalability is crucial for businesses looking to expand their research capabilities or handle large datasets.
4. Improved Accuracy and Consistency: AI analyzes data with a high level of accuracy and consistency, reducing the risk of human error. This objective approach ensures that the insights derived are reliable and unbiased.
5. Faster Iterations and Better UX: AI helps businesses respond to user feedback faster, allowing them to make improvements more quickly. Boag points out that AI allows researchers to “look at information in ways that I’ve never been able to do before as a user researcher.” This ability to rapidly interpret data leads to better user experiences and more effective design updates.

Step-by-Step Process for Using AI in Survey Analysis

Boag outlines a straightforward approach to using AI for analyzing open-ended survey responses, addressing one of the biggest hurdles in user research:

1. Download and Upload Survey Data: Boag begins by downloading the survey results as a CSV file and uploading it to ChatGPT. He explains, “You literally just drag and drop it [the CSV file] to ChatGPT.”
2. Prompt ChatGPT for Analysis: Once the file is uploaded, Boag uses specific prompts to guide ChatGPT. For example, he asks, “Attached is a survey asking the question, ‘If you decided not to sign up today, it would be useful to know why. Please, can you identify common themes?’” ChatGPT then sifts through the responses, identifies patterns, and ranks them by frequency.
3. Review and Act on Insights: The analysis quickly reveals actionable insights. For instance, ChatGPT might highlight “cost being too high” as a top reason why users don’t convert. By ranking the most common themes, Boag can prioritize which issues to address first, making it easier to focus on what will have the biggest impact on conversions.

This process, which once could take days, now takes only minutes, thanks to AI’s powerful data processing capabilities.

Using AI Tools for Interview Analysis

User interviews provide deep insights but are often underused due to the effort required to analyze them. Boag introduces Fathom, an AI tool that automates the transcription and analysis of interviews, making it a valuable addition to any researcher’s toolkit.

How to Use Fathom for Interview Analysis:

• Automated Notes and Highlights: Fathom automatically generates meeting notes and highlights key themes from the interview. Boag explains, “It’s created these meeting notes for me… covering all of the different areas of improvements or things that the user wants.”
• Easy Navigation and Search: With Fathom, users can jump directly to specific parts of the conversation, saving time otherwise spent manually searching through transcripts. Boag says, “I can jump through to any of these [questions] and see what response I got to those questions,” showing how the tool’s features make data more accessible.
• Ask Specific Questions: Users can also ask Fathom specific questions about the interview content, like “What were the main pain points experienced by this user?” The AI pulls insights directly from the transcript, giving researchers the ability to focus on strategic analysis rather than manual data extraction.

These capabilities make Fathom a powerful tool for anyone looking to get the most out of their user interviews, turning raw data into actionable insights quickly and efficiently.

The Future of User Research with AI

AI is not just a tool for speeding up user research; it is changing the entire approach to how researchers work. By automating routine tasks, scaling efforts, and providing deeper insights, AI allows researchers to focus on strategy and creative problem-solving.

Boag’s experience shows that embracing AI in user research leads to more effective and efficient outcomes. As he concludes, “Go and try these tools, because you’ll find that you can do so much more user research than you’ve ever done before, because it’s so much quicker and you’ll get better results too.” For UX and CRO professionals, using AI is no longer optional—it’s becoming essential for staying competitive and delivering the best user experiences.

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