AI para el análisis de datos en Servicio al Cliente

How to use generative artificial intelligence in data analysis?

The power of generative artificial intelligence is transforming how we approach data analysis. In this context, platforms such as ChatGPT and Cloud 3.5 Soret are at the forefront, offering innovative methods to interpret data effectively. The ability of these tools to generate interpretive charts and data-driven suggestions is astounding. Every user has the potential to maximize these capabilities, guiding the way we understand and use information.

How do you get started with data analysis in ChatGPT?

To start analyzing data with ChatGPT, you first choose the intelligence model (GPT-4 OR with Canvas, for example) and load the file containing the data, either in CSV or Excel format. Once the data is loaded, there are two main approaches:

1. Free analysis: let artificial intelligence automatically suggest graphs and analysis.
2. Guided analysis: Define specific graphs that you want to obtain.

In both cases, the tool is able to generate graphs such as customer age distribution, monthly purchase frequency by gender, and average monthly spending by location. In addition, some graphs are interactive, allowing visual changes and additional details by hovering over them.

How to ask specific questions to obtain insights?

Defining questions is key to extracting specific insights from the data:

• Define the objective: Determine what you want to measure. For example, average spend and customer satisfaction.
• Structure the question: Ask if there is a relationship between average monthly spend and customer satisfaction.

Artificial intelligence will analyze the data and generate graphs, such as box plots to visualize the relationships between variables. You can go deeper with cluster analysis to group customers according to buying and spending patterns, allowing you to personalize the customer experience.

How to personalize the customer experience based on clusters?

Based on the identified customer clusters, specific strategies can be formulated:

• Loyal customers: Implement loyalty programs and exclusive promotions.
• Occasional customers: Use re-engagement campaigns and personalized offers to convert them into repeat customers.
• Low-spending customers: Educate about product benefits and offer attractive promotions.

These strategies enhance the personalized customer experience and increase engagement.

What does Cloud 3.5 Soret offer with its artifact component?

Like ChatGPT, Cloud 3.5 Soret offers advanced data analytics functionalities with a unique twist: the artifact. This component helps in data visualization and provides an interactive dashboard to better understand the insights generated.

How to use the artifact in Cloud 3.5 Soret?

To take advantage of the artifact:

1. Create a new project: set a name and a short description of the analysis you want to perform.
2. Upload the data and start the analysis: Request specific insights, such as the distribution of satisfaction levels among customers.
3. Generate a dashboard: View average metrics and distribution by city, all in a dynamic interface.

The artifact facilitates deeper analysis, strategic suggestions and a presentation similar to tools such as Power BI or Tableau.

What differentiates Cloud 3.5 Soret from ChatGPT?

The main difference lies in its ability to generate a detailed report that includes an executive summary, specific strategic recommendations, process optimization, and an experience management plan. This level of specificity exceeds the capabilities of many other generative artificial intelligence systems.

Both platforms offer robust analytics and significant customization options, inviting users to explore how these tools can improve their business decisions. In addition, further research into the many possibilities of data analysis supported by generative artificial intelligence, as well as the use of tools that generate visual content or even videos from analyzed data, is encouraged.