Predicciones Visuales: Transformando Datos en Decisiones

How to make effective forecasts in BI?

In today's business world, collecting data and analyzing it is not enough. To gain competitive advantages, it is essential to be able to forecast behaviors and trends that allow us to make strategic decisions. In this context, Business Intelligence (BI) becomes a powerful tool. We will detail how, using Power BI, we can make predictions that will help us improve our operations and understand our customers' behavior in depth.

How to calculate the probability of customer churn?

The first step is to understand how likely customers are to abandon our platform, which can mean a significant loss of revenue. We can address this with the use of scorecards in Power BI. To perform this prediction:

1. Define the measure: Compute the probability of abandonment by considering the number of users who have shown purchase intent but have not completed the transaction. This is computed as:

   Probability of abandonment = (Number of abandoned users / Total number of users with purchase intent).

2. Formatting of results: Create a percentage measure and arrange it in data format: decimal to percentage for easy interpretation.

How to identify abandoned items in the cart?

To maximize sales conversions, it is crucial to identify those products that frequently remain in the shopping cart without the customer completing the transaction. Use the radar chart in Power BI for this:

1. Chart Setup: In the visualizations pane, select the radar chart. Assign the category of "items" and on the Y-axis, "number of items in cart".
2. Interpretation: You can observe which are the most frequently abandoned items, such as bicycles or watches, and work on specific strategies to convert these opportunities.

How to project future cart abandonment trends?

To anticipate potential consumer behavior, it is essential to visualize how these trends may evolve:

1. Plot online projections: use Power BI line charts. On the X-axis, select the date field and for the Y-axis, the number of users interacting.
2. Adjust visualization: Simplify at monthly and yearly level. Add a subcategory per month to have a clear representation of the behavior over time.
3. Define forecast: By activating the forecast option in the chart format, you will get a projection cone showing how the abandonment rate could evolve.

How to analyze future interaction based on clicks?

User interaction with our site is also a key indicator for business projections:

1. Running the analysis with line graphs: similar to cart analysis, place the date on the X-axis and the click count on the Y-axis.
2. Projecting future clicks: Apply the same forecasting technique to infer future interaction frequency and adjust to the potential margins of the operation.

In conclusion, Power BI is a must-have tool for those looking to go beyond simple data presentation. By turning data into forecasts and trend measurement, companies can make proactive decisions to improve their strategic direction. The techniques discussed here not only allow for a deeper understanding of customer behavior, but also for continuous optimization of the user experience and, therefore, conversions. Use these methods and make your BI approach a true ally for the growth of your business.