What is Generative AI?
Generative AI uses existing data to create new ideas or content, including text summaries, videos, and images. The most newsworthy generative AI example is ChatGPT, which has taken the industry and world by storm. People use this new technology to write songs, help with homework, and even create workout plans. While it has widespread applications across industries, it’s particularly useful for UX research.
As an industry, we are just beginning to integrate AI into our research practices. Still, the tools already embracing this technology are undeniably revolutionizing our work. By leveraging AI-powered analysis, we can now accomplish what used to take hours or even days in mere minutes. This newfound efficiency allows us to shift our focus toward maximizing the impact and execution of the valuable insights we gather.
Applying Generative AI to UX Research
Ethos, a leading qualitative research platform, has introduced its groundbreaking generative AI tool called Instant Insights, which is set to revolutionize the efficiency of data analysis. With this innovative solution, businesses can now extract actionable insights from their data in a matter of minutes, a night and day improvement over the traditional time-consuming manual process. By harnessing the power of AI, Ethos is empowering organizations to expedite their decision-making and drive meaningful outcomes at unprecedented speeds.
Qualitative UX research can provide a wealth of valuable data, but analyzing extensive volumes of open-ended responses is time-consuming. However, thanks to the advent of generative AI, researchers can now effortlessly synthesize data and derive precise, meaningful conclusions with just a few clicks. This groundbreaking functionality streamlines the research process and eliminates one of the most significant obstacles UX researchers face. Generative AI unveils previously untapped potential within qualitative research, transforming how insights are obtained and utilized.
When analyzing UX data with Instant Insights, the quality of your conclusions is directly tied to the prompts you employ to explore the data. Providing context and precise instructions to the AI tool is crucial for generating valuable results. Fortunately, Instant Insights simplifies this process by offering a range of preset prompts to choose from. Its flexibility allows researchers to ask follow-up questions and even input their own custom prompts. This iterative feedback loop helps refine the system’s responses and ensures the delivery of more concise and targeted insights.
AI can play a pivotal role across the various stages of UX research. For instance, it proves immensely beneficial in supporting the creation of task guides and formulating objectives. This efficiency frees up valuable time and resources, so UX teams can focus on higher-order analysis and interpretation. Embracing AI-powered tools expands the horizons of UX research, enhancing efficiency and enabling researchers to extract deeper insights from their studies.
Utilizing Instant Insights – A Case Study
A fast-growing pet insurance company recently leveraged EthOS for a comprehensive path-to-purchase study. The study aimed to understand the key decision factors and obstacles their customers encountered while seeking pet insurance coverage. Engaging participants from diverse locations across the UK, they were able to gather detailed descriptions of their customers’ purchase journeys, resulting in a treasure trove of valuable data.
Given the pressing timeline of this project, manually coding and tagging the purchase journeys of 25 customers was out of the question, especially since the research team planned on being able to sleep during the three days they had to turn around a report. So they turned to Instant Insights to analyze their raw data.
While skepticism around the accuracy of AI-driven analysis may linger, the pet insurance company found that with well-crafted prompts, the outputs produced by Instant Insights proved remarkably accurate. To validate this, they conducted their own manual analysis in parallel and discovered that the AI effectively identified the most relevant themes within the data. Even when dealing with the intricacies of pet insurance, which can vary significantly for individual shoppers, the AI tool accurately summarized the target audience’s preferences and obstacles.
The reliability of this tool proved invaluable, saving the UXR team countless hours of tagging and coding, a typically arduous process in qualitative research. Armed with the insights generated, they promptly delivered results to their internal marketing team, enabling them to implement the findings into their customer acquisition strategies faster than what was previously possible.
Proven Benefits of AI-Based Analysis
Time-Saving
The most significant benefit of using AI in UX research is saving time on analysis. Being able to review multiple video submissions from 25 participants in minutes versus days is a game changer. With timeline pressure always present in research projects, being able to deliver insights to your client or internal team within a couple of days of wrapping up a study is a considerable improvement. Since AI dramatically reduces the time and effort needed for analysis, you can focus more on strategic thinking and implementation.
Reduced Bias
When manually tagging and coding qualitative data, we naturally introduce some level of bias into our analysis, favoring certain types of data that resonate with us. While eliminating bias is an elusive goal, AI is remarkably close to achieving it. By leveraging AI, we can significantly mitigate the impact of personal bias and attain more objective insights. While it’s essential to recognize that bias can still arise at various stages of the research process, incorporating AI into qualitative analysis offers a valuable tool for minimizing bias and unlocking deeper insights. The AI acts as an unbiased partner, working hand in hand with researchers to extract meaningful patterns and guide data-informed decision-making.
Easy to Use
With an interface similar to texting with your friends, Instant Insights feels familiar. Contrary to the common misconception of AI being complex and confusing, this tool offers simplicity. The AI takes all the participant data from a selected task, analyzes it, and returns the most relevant themes within seconds. Its chat-like format allows for ongoing interaction, enabling researchers to pose further questions and delve deeper while remaining intuitive and user-friendly.
Conclusion
AI is not poised to replace the roles of UX researchers any time soon. Our unique creative skills and ability to apply context are unmatched by AI. Nonetheless, UX researchers who embrace the power of AI will gain a significant edge over their peers. AI will continue to catalyze our evolution as UX researchers, empowering us to deliver heightened value within our organizations and the customers we aim to serve.