El Futuro de la Medición de UX

Artificial intelligence is revolutionizing the way companies analyze user experience. In 2021, only 12% of organizations were using AI for this purpose, but today more than half have adopted this technology, with a clear upward trend. The impact of AI on the technology industry is undeniable, especially when it comes to analyzing data and creating visual narratives that facilitate strategic decision-making.

How to create effective storytelling with ChatGPT for UX analytics?

Storytelling with data is a powerful tool for communicating complex findings in a clear and actionable way. Using ChatGPT, we can transform data sets into structured visual narratives that any team can easily understand.

To begin this process, we need:

1. A relevant data set (in this case, traffic data and abandonment rates).
2. A well-designed prompt that states exactly what we need.
3. A clear structure for our visual narrative.

The prompt used in this example asks ChatGPT to generate a visual story structured in four graphs with concise text, analyzing:

• The problem
• The KPI affected
• The impact
• The proposed solution

Data analysis with AI: a case study

Running the prompt with our traffic dataset and abandonment rates for the last fifteen days, ChatGPT analyzes the information and generates a structured visual narrative. The results are surprisingly clear:

1. Identifying traffic patterns: The analysis shows traffic peaks on Mondays and Tuesdays at 7 PM, revealing that promotions work better on Mondays than on Tuesdays.

2. Detection of critical problems: An increase in the abandonment rate is identified during peak hours, specifically at 7 PM when the promotion starts, suggesting a problem at that specific time.

3. Correlation of variables: The analysis shows that the higher the number of users, the higher the abandonment at checkout, providing valuable information for the development team.

4. Proposed solutions: ChatGPT not only identifies problems, but also suggests strategies to reduce checkout abandonment, such as:

   • Server optimization
   • Checkout UX improvements
   • Implementation of preloading
   • Form simplification

What is most valuable is that AI not only analyzes the data provided, but also offers actionable solutions for the detected problems, thus completing the analysis cycle.

Where is the true potential of AI in user experience analysis?

The potential of artificial intelligence in user experience analytics is mainly concentrated in two key areas:

Predictive models.

Predictive models allow us to anticipate critical behaviors such as:

• The exact moment a user is likely to abandon the process.
• Whether a user will rate us positively or negatively
• Patterns of behavior that may indicate future problems

These models analyze large volumes of data faster and more strategically than any manual analysis, allowing us to act proactively before problems occur.

Advanced personalization

AI helps us deeply understand our users:

• What exactly they want
• When they need it
• How we can best deliver it to them

With this information, we can create highly personalized experiences, such as offering specific promotions at the most relevant times for each individual user, rather than generically optimizing for high-demand days.

For example, instead of simply improving the server for promotional days (Monday and Tuesday), we could predict the exact time a specific user is likely to want to have lunch and offer a personalized promotion right then and there.

The combination of data analytics, visual storytelling and predictive AI capabilities is radically transforming how we understand and optimize the user experience. These tools not only help us to identify problems, but also to anticipate and solve them in a proactive and personalized way. Have you implemented any of these techniques in your projects? Share your experience in the comments.