Customer Segmentation

Our data team defines new and high-value customer segments through a rigorous analysis of their data. We execute RFM (frequency, frequency and monetary value of the purchase), as well as personalized segmentations.

Customer segmentation is a method of dividing customers into groups or clusters based on common characteristics. The market researcher can segment customers in the B2C model using several customer demographics, such as occupation, gender, age, location, and marital status. Psychographic characteristics such as social class, lifestyle and personality traits, and behavioral traits such as spending, consumption habits, use of products/services and pre-purchased items. The B2B model uses several characteristics of the company, such as the size of the company, the type of industry, and the location.

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Types of Segmentation.

One of the most used methodologies in customer segmentation is RFM analysis, which is based on three key dimensions: Recency, Frequency and Monetary Value.

• Recency: This aspect evaluates how recently a customer has made a purchase. The analysis calculates the number of days since each customer's last purchase, providing information about the customer's most recent activity.
• Frequency: Frequency measures how often a customer makes purchases. The total number of purchases made by each customer is analyzed, identifying patterns of buying behavior.
• Monetary Value: This aspect focuses on evaluating how much money a customer has spent in total. The sum of the purchase price is calculated for each customer, identifying those with the highest economic value.

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Implementing RFM analysis provides certain benefits to companies.

• Touchpoint Optimization: It allows companies to optimize each point of contact with customers, ensuring revenue generation and the retention of active users.
• Identifying Potential Customers: Facilitates the identification of customers with the potential to generate more profitable businesses, allowing managers to strategically direct their efforts.
• Effectiveness in Promotional Campaigns: It helps to execute more effective promotional campaigns by personalizing messages and services according to the buying behavior of each segment.

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RFM Analysis Implementation Process.

• Calculation of RFM Values: For each customer, the Recency, Frequency and Monetary Value values are calculated.
• Quartile Segmentation: Customers are grouped into segments using quartiles for each RFM dimension, allowing for a more accurate classification.
• Sorting RFM Scores: RFM scores are ordered in ascending order, making it easier to identify customer patterns and segments.

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Why is RFM segmentation important?

It separates your best customers from those who are less profitable or less engaged. This can be especially important for large companies, where you may have a huge customer base and need to know how to target the most valuable customers in the most cost-effective way.

This type of segmentation allows you to do this by ranking customers based on their frequency, frequency, and monetary value so that you can focus on those who are most likely to buy many of your products at a high price.

How to calculate it?

RFM is calculated by analyzing your customers' past behaviors and assigning them a score for each metric; these scores are then combined to give you a final RFM score for each customer.

To calculate recency, you need to look at how recently each customer made a purchase from your company. This can be measured in days, weeks, or months since your last purchase. Customers who made a purchase more recently will receive a higher current score than those who made their most recent purchase a long time ago.

Frequency is determined by looking at how many purchases each customer has made in the same period of time that was used to calculate the frequency: the more purchases you have made in that period of time, the higher your frequency score.

And the monetary value is based on how much money each customer has spent on your products during that same period of time.

Companies that apply RFM analysis experience a significant increase in customer retention, with 20% more loyal customers compared to those that don't implement it. In addition, there has been a 15% increase in revenue generated by customers identified as being of high value through this analysis.

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In short, customer segmentation, especially through RFM analysis, has become a fundamental tool in the arsenal of modern companies. The ability to understand and adapt to buying behaviors in a personalized way not only improves the effectiveness of marketing strategies, but also drives sustainable growth through stronger relationships with customers. The combination of statistical data and advanced methodologies positions companies at the forefront of competitiveness in today's market.