How to use machine learning to identify “good” customers vs “bad” customers

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Good profitable customers rarely become unprofitable. It is more likely that they were unprofitable from the onset.

Determining an approach to define customer value can be a complex decision. Traditionally, we use gross margin in identifying good and bad customers. For example, if your overhead costs are 25% of gross revenue, a good customer is anyone with a gross margin over 25%.  This method has become simplistic, as customers rarely consume overhead costs equally.

Activity Based Costing (ABC) has become a preferable method to identify profitable and unprofitable customers. ABC bases customer profitability analysis on the principle customers consume activities. With ABC, it’s common to find that 80% of overhead activity cost is consumed by 20% of your customers.

A model can be built which calculates loss ratio based on Activity Based Costing. Combining loss ratio with lapse probability, the goal is to identify and consider the customer segments that are good.

Self Organizing Maps is a clustering technique for grouping similar data together and is used for finding patterns in your data. K-means clustering algorithm can be run to identify the clusters in the data by plotting loss ratio on the y-axis and lapse probability on the x-axis.

We can drill-down into any clusters to identify the characters of the customers within the cluster.


Using this technique, we are able to identify the characteristics of our good customers, which allows us to optimize the customer experience for them, including:

  1. Targeted Campaigns
  2. Price Optimization
  3. Product Features

Clustering analysis has applications within many different industries including insurance, mining, and retail and is one technique to help ensure you are gaining maximum insights from your data.

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