Getting to Know the Customer

Understanding the customer is every business's top priority. You can have excellent products, creative campaigns and a sharp team, but without customers, nothing works: an engine with no wheels won't take you anywhere. Once you're offering a product or service that solves a real problem, the next step is understanding who buys it and why.


Customer behavior is shaped by psychological factors, buying patterns, preferences and needs. These elements let us anticipate behavior and adjust strategies. For example:


  • Buying patterns: some customers buy frequently, others sporadically; their behavior can range from impulsive to planned.
  • Perceived value: every customer interprets the value of a product or service differently.
  • Loyalty and retention: built on positive experiences and an emotional connection with the brand.
  • External and internal factors: advertising, trends, expectations or past experiences all shape the decision.

To organize all of this, there are analysis tools, and one of the most useful is RFM. With it, we can segment our customers into groups with similar characteristics and act accordingly: reward VIPs, nurture those with growth potential, win back the ones at risk, or design strategies for low-spend customers. This lets us move from guessing to managing with real data.

What Is RFM?

RFM captures three dimensions that let us read customer behavior:


  • Recency: time since the last purchase or interaction. The more recent, the higher the engagement.
  • Frequency: number of purchases within a period. High frequency usually signals loyalty.
  • Monetary: total spend within a period. Key to identifying the most valuable customers.

Combining these dimensions reveals clear patterns. A customer with high recency, frequency and spend is a VIP; one who bought recently but spends little has growth potential; a customer with low recency and frequency is at risk, and a stable, low-spend customer contributes low volume but stays active. Each group calls for specific actions.


The industry tends to use standard labels for these segments, making them easier to read and communicate. The most common names are:


  • VIP / Champions / High Value: customers with high recency, frequency and spend.
  • Loyal / Regulars / Repeat Customers: frequent, though not always high-spending.
  • Potential / Promising: recent customers, though still with low frequency or spend.
  • At Risk / Churn Risk / About to Sleep: their frequency has dropped, or they haven't purchased in a while.
  • Hibernating / Lost: no activity for a long time, nearly lost.
  • Low Value / Price Sensitive / Occasional: low spend and low frequency, contribute little.

Labeling customers this way and representing them visually with consistent colors and legends (green for VIPs, yellow for potentials, red for at-risk, gray for inactive) makes it possible to get a clear picture of customer-base health at a glance.

Building an RFM Model

Applying RFM requires reliable data describing both the transactional relationship and the customer experience. Among the most important:


  • Transaction records: dates, amounts and products for each purchase.
  • Identification data: customer ID, email, phone number.
  • Behavioral data: website visits, app usage, social media interactions.
  • Purchase history: frequency, product variety, average order value.
  • Demographic information: age, gender, location and preferences.
  • Feedback: reviews, satisfaction surveys and NPS.

Once this data is modeled, visualization is key to spotting patterns quickly. This can be done with any analytics tool, like Tableau, Google Analytics, Power BI, among others. Some of the most representative charts for RFM are:


  • Histograms: to see the distribution of recency, frequency and spend.
  • Bubble chart: combines recency and frequency on a plane, with bubble size representing monetary value.
  • Heatmap: cross-references the three dimensions to highlight customer concentrations.
  • Time trends: show how segments evolve over time.
  • Cohort analysis: lets you observe customer retention based on sign-up date.