Ween.ai
In our previous article, we explained why User research is essential to understanding the needs, behaviors, and pain points of customers. It helps product teams make evidence-based decisions that drive business growth. However, analyzing qualitative data can be time-consuming and resource-intensive.
The process of manually tagging and consolidating user data to find patterns is not only laborious but also susceptible to human biases.
In this article, we will explore why generative AI is the perfect technology to revolutionize user research and how Ween.ai is approaching this.
User research is a crucial part of product development. It helps teams understand what their customers want and need, and how they interact with the product. However, the process of collecting and analyzing user data can be time-consuming and resource-intensive.
UX researchers conduct user interviews, surveys, usability tests and lots of other techniques to collect qualitative data. They then manually analyze the data to identify patterns and themes.
However, this process is not only time-consuming but also susceptible to human biases. Depending on who analyzes the data, what stands out as “important” can be so different. Moreover, the analysis and consolidation phase is the heaviest and most time-consuming part of user research.
Even with tools like Dovetail that help with video transcripts and centralization of insights, researchers still need to manually tag all the data to find patterns. This manual tagging process is not only time-consuming but also susceptible to errors and biases.
Generative AI combined to Predictive AI is the combination technology to revolutionize user research. It can automatically identify patterns and trends from raw qualitative data, eliminating the need for manual tagging and consolidation. It can analyze large amounts of data in a fraction of the time it takes a human researcher.
Generative AI works by learning from a large dataset and generating new data based on what it has learned. On the other hand, predictive AI uses predictive models to analyze patterns in historical data. Both combined, we can extract knowledge from raw data then identify patterns and trends in this knowledge that may not be immediately apparent to a human researcher or skipped because of biases. It also helps removing those biases and human error from the analysis process.
Ween.ai is an AI powered platform that automatically identifies patterns and trends from raw qualitative data. It is designed to eliminate the need for manual tagging and consolidation, making user research more efficient and accurate.
Manual tagging is the classic way of aggregating qualitative data. Today tools like Dovetail help with video transcripts and centralization of insights but you still need to tag manually all the data to find patterns.
With all the AI capabilities, we strongly believe that manual tagging will soon become an outdated way to find patterns.
For its first versions, we focused on the most commonly used Research technique and the most painful to analyse : user interviews.
Here’s how Ween.ai works:
The patterns that Ween.ai generates are biasless. The AI analyzes user interview transcripts and extracts exhaustively any piece of knowledge. These pieces of knowledge are compiled to form patterns. A pattern is a group of knowledge items that are semantically similar. By doing so, Ween.ai ensures that no bias is injected by the AI.
In conclusion, the use of generative and predictive AI is crucial in solving the problem of manual data tagging and analysis in User Research. With Ween.ai, product teams can save significant time and resources and make unbiased and evidence-based decisions quickly. As AI capabilities continue to evolve, we believe that automated analysis of qualitative data will become the norm, and Ween.ai is at the forefront of this innovation.
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