Let's assume you are a marketing manager selling products on Amazon.

You are faced with a challenge: user reviews often contain valuable feedback but are usually unstructured, written in different languages, and sometimes include typos.

Traditionally, analysing this data requires either structured feedback mechanisms or extensive manual processing, both of which are time-consuming.

Using GenAI, we can transform this unstructured data into actionable insights by mimicking human cognitive processes at scale.

In the photos below, I show you the process with a dataset of smartphone reviews from Amazon in various languages. Our goal is to extract meaningful insights regarding product issues.

Let's see how GenAI handled the case:

• It begins by generating hypotheses and formulating search queries to retrieve relevant data.

• For identifying issues, it queries reviews with low ratings (1-3 stars) and more than five helpful votes, ensuring that common and significant problems are highlighted.

• The AI then processes the reviews and provides specific issues, such as problems with SIM cards or decreased phone speed.

As you can see, this is very similar to what a human would do, but in comparison, you have now increased:

• Efficiency: What would take hours of manual work is done in minutes
• Accuracy: The AI can sift through large datasets without fatigue
• Scalability: GenAI operates 24/7

This simple yet powerful use case illustrates how GenAI can simply redefine full segments of our business processes.

Whether it’s user reviews, transcripts, or bank statements, it can make sense out of many data types for processes that are usually tedious and error prone.

Hugo Matthaey