# Difference between the two segmentation method results

We presented in sections [Applying KMeans clustering technique for RFM scoring](article:22) and [Applying KMeans clustering technique for RFM scoring](article:22) how the two segmentation methods work. 
To help you choose the one that will fit the best your needs, let's present the differences we get with them.


# Context

We are still analyzing the our 2500 customers "total basket" distribution :arrow_down:: 

![total_basket_distribution.png](09velPXoCMT5)

Let analyze how the monetary value scores are distributed after each method :arrow_down:: 

## Total basket distribution, colored by monetary value scores
![monetary_value_scores_distribution_benchmark.png](1ePupNLZSmvc)


## Number of customer by monetary value scores
![monetary_value_scores_counts_benchmark.png](0CVw8fwP8EeU)


The difference should be now more apparent for you.
- The quantiles method will tend to create groups of customers having equal size: here, all groups have exactly 500 customers.
  - **NOTE**: Depending on your data content, you could have quantiles vampirizing others. This can occur if you have many duplicate values in your columns.
- The KMeans method will tend to group together customers having similar total baskets, leading the "**M**onetary value 1" group to have only 19 customers while the "**M**onetary value 5" group has 1087 customers.



