Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data

Chapter 4. How can I characterize my customers from the mix of products that they purchase? 79

A simple visual inspection of Figure 4-8 and Figure 4-9 using the Shopper Type

as a guide seems to show that other than for the cluster number assigned and

the order in which they are arranged in the two output visualizers, the two sets

cluster results are very similar.

However, on closer inspection we will see that this is somewhat superficial and

that the segments produced are not as similar as this simple analysis would

suggest. So what is happening and how do we understand what the two sets of

results really mean? In the next section we explain it all.

4.6 Interpreting the results

In the previous section we looked at the steps we have to follow to get our mining

results using two different clustering techniques. The

sixth stage in our generic

mining method

is to interpret the results that we have obtained and determine

how we can map them onto our business. When you are first confronted with the

cluster results the first question that you are going to ask is “What does it all

mean?”. In this section we describe how to understand and read and interpret

the results from the different clustering techniques, but more importantly how you

can compare the results from different cluster techniques.

4.6.1 How to read and interpret the cluster results?

The cluster techniques that we have used both produce results that can be

displayed graphically, as we have seen. We can also obtain additional visual

information by highlighting individual clusters and even individual variables.

There is also another level of detailed information which gives us the statistical

information that we need to fully interpret what the clusters are telling us about

our customers. Although the visualized results are important in giving us an

overall impression of what is happening, the interpretation always needs to be

backed up with the statistical detail to confirm our understanding. In this section

we look at how this is done.

As we discussed in 4.2, “The data to be used” on page 49, the first thing you

need to understand about the visualized results is that the graphs and charts are

telling you about the characteristics of customers who have been put into the

cluster and how these customers differ from the population as a whole. It is not

telling you directly how the customers in one cluster differ from customers in

another cluster. This is an important distinction and we will return to this issue

later.

80 Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data

When we describe our clusters therefore, we are looking at the characteristic

variables of our customers in each cluster, and using these to describe how

customers in this cluster differ from the customers in all other clusters. To do this

we want to choose those variables that make the greatest difference, and

fortunately the order in which the variables are presented to us (using the chi-

square statistic) give us just what we need.

Figure 4-10 shows the expanded visualized result for Cluster 8 from our

demographic cluster results. In expanding the view we have also included some

of the characteristic variables that were not used to produce the cluster, but these

help us to interpret the results.

Figure 4-10 Demographic cluster 8

This cluster is the first of the two clusters that contained what our business rules

defined as Family Shoppers. You will remember that the business rule for this

group was: