Segmentación de Clientes con Campos Calculados en Looker Studio

Customer segmentation is a fundamental strategy for any business looking to better understand its audience and personalize its marketing strategies. Instead of settling for pre-determined classifications, we can create our own segmentations based on metrics relevant to our business. This allows us to make more informed decisions and develop more effective strategies for each customer group.

How to create a customized customer segmentation in Looker Studio?

In the dataset we are analyzing, customers are already classified into three types: new, frequent and VIP. However, this classification was inherited and we do not know the criteria used to establish it. Therefore, we are going to create our own segmentation using the metrics available in our data.

In the Looker Studio project, specifically in the customers tab, we can observe a visualization that shows the current distribution of customers according to their type. To create our own segmentation, we need to identify which metrics we can use.

What metrics can we use to segment customers?

In our dataset we have two main metrics:

• Purchase frequency: How many times each customer has purchased.
• Total spent: How much money each customer has spent on our products.

For this exercise, we will use purchase frequency as the basis for our segmentation. The first thing we must do is understand the range of values that this metric has in order to establish appropriate limits for each segment.

How to determine the ranges for segmentation?

To know the range of purchase frequency, we can create scorecards that show us:

1. The minimum purchase frequency value: 1 (the customer who purchased the least did so only once).
2. The maximum purchase frequency value: 50 (the customer who bought the most bought fifty times).

With this information, we can set the limits for our custom segmentation.

How to create a calculated field for the new segmentation?

To implement our segmentation, we will follow these steps:

1. Go to the customer dataset
2. Add a new calculated field
3. Name the field "n_segmentation" (the "n" indicates that it is new)
4. Create a conditional formula that classifies customers according to their purchase frequency.

The formula we will use is:

CASE WHEN purchase_frequency >= 1 AND purchase_frequency < 6 THEN "new" WHEN purchase_frequency >= 6 AND purchase_frequency < 20 THEN "frequent" ELSE "loyal"END.

This formula classifies customers as follows:

• New: Customers who have purchased between 1 and 5 times.
• Frequent: Customers who have purchased between 6 and 19 times
• Loyal: Customers who have purchased 20 or more times.

Once the formula has been created, we save the calculated field and proceed to visualize the results.

How to compare the new segmentation with the original classification?

To compare both classifications, we can create an additional pie chart showing:

1. The dimension of analysis: our new segmentation
2. The metric: total count of the database

When comparing the two charts, we see significant differences:

• New customers: went from representing 33% to only 6% of the total.
• Frequent customers: Decreased from 37% to 28%.
• Loyal/VIP customers: Increased considerably from 29% to 65%.

These differences reveal that the original classification was not aligned with actual customer buying behavior, which could have led to inefficient marketing strategies.

What other segmentations can we create?

Segmentation based on purchase frequency is just one of many possibilities. We could also create segmentations based on:

• Total spent: Classify customers according to their monetary value.
• Recency: when was the last time the customer made a purchase
• Combination of metrics: Create a RFM (Recency, Frequency, Amount) model.

Each type of segmentation gives us different insights into our customer base and allows us to design more personalized and effective strategies.

How to apply this segmentation in our business strategies?

Once we have created our customized segmentation, we can use it to:

• Develop segment-specific marketing campaigns
• Set more realistic conversion goals
• Identify opportunities to increase purchase frequency or average purchase value
• Implement loyalty programs for loyal customers.

The key is to align our segmentation with specific business objectives, ensuring that the categories created are actionable and relevant to our strategy.

Now that you know how to create your own customer segmentation in Looker Studio, we invite you to experiment by creating a calculated field based on total spend and completely revamping the customer tab of your dashboard. Share your results in the comments section and find out how this new perspective can transform your business strategy!