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  • Writer's pictureAmel Mechalikh

Are Generative and predictive AI the future of User Research?

Updated: May 4

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.

Enter - an AI that turns qualitative data into patterns

In this article, we will explore why generative AI is the perfect technology to revolutionize user research and how is approaching this.

Generative and predictive artificial intelligence and the future of user research

The Problem with Traditional User Research Methods

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.

The Solution: Generative AI x Predictive AI

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.

How is Using Generative and Predictive AI to Revolutionize User Research 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.

How does work?

For its first versions, we focused on the most commonly used Research technique and the most painful to analyse : user interviews.

Here’s how works:

  1. The user uploads all their user interviews into the platform.

  2. The AI analyzes each interview and gathers all the knowledge the user expressed regarding their pain points, behaviors, needs, or requests.

  3. The AI aggregates the knowledge pieces expressing the same insight (pain point, behavior, need, etc.) and automatically forms patterns.

How does it help in removing bias from User Research ?

The patterns that 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, ensures that no bias is injected by the AI.

Closing Thoughts

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, 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 is at the forefront of this innovation.

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