Evaluating the Results

When you compute RFM scores from transaction data, a new dataset is created that includes the new RFM scores.

Figure 1. RFM from Transactions dataset
RFM from Transactions dataset

By default, the dataset includes the following information for each customer:

  • Customer ID variable(s)
  • Date of most recent transaction
  • Total number of transactions
  • Summary transaction amount (the default is total)
  • Recency, Frequency, Monetary, and combined RFM scores

The new dataset contains only one row (record) for each customer. The original transaction data has been aggregated by values of the customer identifier variables. The identifier variables are always included in the new dataset; otherwise you would have no way of matching the RFM scores to the customers.

The combined RFM score for each customer is simply the concatenation of the three individual scores, computed as: (recency x 100) + (frequency x 10) + monetary.

The chart of bin counts displayed in the Viewer window shows the number of customers in each RFM category.

Figure 2. Chart of bin counts
Chart of bin counts

Using the default method of five score categories for each of the three RFM components results in 125 possible RFM score categories. Each bar in the chart represents the number of customers in each RFM category.

Ideally, you want a relatively even distribution of customers across all RFM score categories. In reality, there will usually be some amount of variation, such as what you see in this example. If there are many empty categories, you might want to consider changing the binning method.

There are a number of strategies for dealing with uneven distributions of RFM scores, including:

  • Use nested instead of independent binning.
  • Reduce the number of possible score categories (bins).
  • When there are large numbers of tied values, randomly assign cases with the same scores to different categories.

See the topic RFM Binning for more information.

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