Understanding the impact of AI on user research

Let’s go back to late 2022. ChatGPT had just made headlines and sparked the fear that “AI will take our jobs.”

Fast forward to today, the conversation has shifted to a completely different perspective. 77% of UX researchers use AI tools to be better at their jobs.

AI has many promising benefits for user researchers—from quicker time to insight to more accurate data analysis.

In this article, we’ll break down three ways AI is shaping user research.

Breaking down the promise of AI for UX researchers

One of the biggest benefits of AI tools is speeding up existing research workflows. In many cases, research teams can go from designing a study to having analyzed responses within a few hours. Let’s explore how AI tools are impacting user research.

1. Move towards continuous research and user-centric decision-making

Traditionally, conducting user research meant designing surveys, recruiting participants, analyzing data manually, and curating reports. It’s tedious and time-consuming.

The result? UXR teams selectively create research studies for a few high-value projects and rely on guesswork for other decisions.

AI tools like Wondering fast-track the research process to enable continuous research.

UX researchers can help product teams validate assumptions and make more user-centric decisions continuously throughout the product development cycle, helping them acquire new users, drive more conversions, or retain existing customers.

With Wondering, you can design, deploy and gather responses for a research study within a few hours. You can:

Write a prompt to generate a full-fledged AI-powered research study
Customize the study by editing or adding blocks, or changing the language
Publish and share it with a link, embed it on your website, or send it to our global panel of over 150K participants

Once the responses are in, Wondering instantly analyzes the data to convey the key themes and present meaningful insights.

That’s how the team at Butternut Box achieved a 42x faster time-to-insight when they integrated Wondering to replace their lengthy user research setup, helping them move towards a continuous research process.

With Wondering, the time required for Butternut Box to conduct and analyze moderated research studies (in this case moderated by AI instead of human moderators) was slashed from 14 days to just 8 hours.

Read the case study

AI-first user research tools like Wondering capture and analyze data in real time. That means your study results are instantly transcribed and categorized into relevant themes as soon as people submit their responses.

As responses roll in, Wondering’s AI will:

Transcribe audio responses into your preferred language
Thematically analyze data to identify core themes with supported quotes
Prepare a dashboard with a granular analysis of individual responses from the survey/interview

Similarly, Katkin’s digital product team use AI-moderated research studies on Wondering to find insights in a few days rather than weeks. They went from spending two weeks on research projects to completing these projects in just three days with Wondering.

The team used Wondering’s AI-moderated interviews to get nuanced responses from their users. Plus, they also interviewed participants from Wondering’s global panel of 120,000 testers to discover and validate problem statements.

Read the case study

2. Understand the “Why?” behind user behaviors

It’s clear that large language models can be used to process data a lot faster than manual methods.

The product team at StreamElements presents a great example of using AI to gain diverse insights from user data and make informed decisions.

The team usually saw an uptick in customer complaints after launching new features. That’s why they leveraged Wondering to gauge their users' needs and design features that align with customer expectations.

Wondering enabled them to:

Launch in-product surveys to target users within their platform
Conduct AI-moderated interviews to learn about user pain points and expectations
Use AI-powered analysis to uncover opportunities for how to improve their product conversion rate

As a result, StreamElements saw a 20% uptick in product conversions and a 50% decrease in support tickets.

Read the case study

There are other ways in which AI can help you understand your users more deeply.

Sentiment analysis

AI tools use natural language processing (NLP) to analyze the emotions in any user response. They can categorize responses into positive, negative, or neutral sentiments.

Or conduct a more nuanced analysis with a wider range of emotions to understand customer feedback. Discover subtle emotions like frustration, confusion, or satisfaction in user responses.

Make more informed decisions and prioritize your product roadmap based on the sentiment analysis of your research study.

User behavior

Track behavior across touchpoints, such as app usage, website interactions, etc. AI tools will process this data to predict user preferences and identify friction points. You can also discover the most used features or why users drop off.

For example, you can roll out AI-moderated interviews to hear directly from your users about their most preferred features. These interviews will ask contextual follow-up questions to discover:

Users’ key pain points
Primary expectations
Improvement suggestions

Usability tests

AI tools automatically collect data about how users interact with your product. They can track mouse movements, scrolls, clicks, and other activities. This analysis helps find usability issues or inefficiencies in the user experience.

For example, let’s say you want to understand why your cart abandonment rate is high.

You can use AI tools to gain real-time visibility into the user experience and detect possible reasons for abandonment, such as long loading times, confusing interface, clunky button design, or something else.

Then, create a design study with Wondering to explore these reasons further and learn your customers’ main concerns.

3. Adopt a multilingual approach to user research

Multilingual research is another key area where AI shows promise for UX researchers.

Typically, you have to collaborate with local translators to conduct research studies anywhere outside your domestic market. This process requires a heavy investment of time, effort, and money.

AI tools can make multilingual research a breeze. Here are three ways in which multilingual AI research can guide your product strategy:

Analyze product-market fit in different markets: Conducting multilingual research studies means you can validate assumptions about users in a particular region. The result? You’ll gain a deeper understanding of niche customer preferences, existing feature gaps, and potential opportunities in this market.
Identify region-specific pain points: Multilingual UX surveys can help you familiarize yourself with local experiences and discover challenges that users in a region might face. You can also compare and contrast your product strategy for domestic and foreign users. Based on these insights, you can build a more adaptable product roadmap for different target markets.
Enhance local engagement: Multilingual research studies can uncover the subtle differences in how users in different parts of the world perceive your product features. This enables you to tailor each region's product experience and messaging and maximize user delight.

With AI tools like Wondering, creating a multilingual research study is possible with a few clicks.

You can design a survey or interview in your preferred language. Then, enable the option to let participants choose which language they want to respond in. Wondering will translate all the questions into their chosen language in seconds.

The best part? You’ll see all the responses in your preferred language when you analyze these results. You can also check out individual responses in the original language.

How can I embrace AI in my UX research safely and responsibly?

With all its benefits, it can be tempting to integrate AI tools into your UX research process at every step of your research process. Here are a few points to remember to look out for when integrating AI into your research setup:

Data privacy and security: Opt for GDPR-compliant and SOC 2 Type II certified platforms. This will ensure service providers implement and maintain internal privacy and security controls while processing your data.

Counter research biases and hallucinations: Opt for platforms that make it clear how they actively stop biases and hallucinations from creeping into your research. At Wondering, we’ve designed our AI to follow best practices when generating studies, asking participants questions and completing analysis. This includes, but isn't limited to, best practices like:some text
- Asking questions in clear language.
- Only asking one question at a time.
- Avoiding leading questions.
- Avoiding biased questions.

We’re committed to building a transparent and secure AI-led research platform. We use OpenAI as our AI provider, and we’ve established rigorous internal safeguards to prevent your data from being used to train AI models.

More importantly, we conduct extensive in-house testing during development to identify potential output liabilities. Read more about our approach to developing an AI-first research platform.

Curious to test out using AI in your own research process? Sign up for a free trial to Wondering to explore the promise of AI for user research for yourself!

Author

Axel Thomson

Founder & CEO

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