Chapter 4. How can I characterize my customers from the mix of products that they purchase? 89
4.6.3 What does it all mean? Mapping out your business
The question we posed was which clustering technique should we use to
describe our customers? The answer is that we use both. In fact we can now
clearly see that we have a way of combining not only the two cluster results but
also our original business rule segmentation into a single view of our business.
The results of course show us what we knew all along, that we dont really have
distinct groups of customers but a continuum of different types of customer.
However, we can now create through the power of data mining, a completely new
way of visualizing our business. We can now picture not only where our
customers are in relation to segments to which they have been assigned, but
also in relation to each other and our other segments. It is also possible to
position individual customers onto this map, and so we can see exactly where
any specific customer is positioned.
We can also see that by increasing the number of neural clusters that we
generate we have an easy way of increasing the resolution of our picture. The
technique just gives us a better granularity as we increase the number of
clusters. So why do we need to combine the neural and demographic cluster
results? Where the neural clustering technique lays down the map the
demographic clustering technique provides a way of putting the contours onto
the map. Since demographic clustering uses the similarity between our
customers to group them together in an appropriate way, this produces contours
of similarity and gives us the scaling that we can compare our different groups of
Continuing the map analogy by combining the two types of cluster result we can
identify isolated hills on our map where we have areas of niche customer
behavior that may require special attention in comparison to the large plains
where we have more homogenous customer behavior. The areas between the
different groups are also interesting, particularly where there are valleys and
ridges of opportunity (for example, Clusters 20, 21, 25, and 26 in Figure 4-17).
Customers in these regions are almost certainly the ideal candidates for
When we map out our customers in this way, we can immediately see the
benefits of data mining to understand our customers better. It gives us a new
view on what they are doing and this immediately prompts us to start asking
more questions about what is going on.
90 Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data
Although we have not shown an example here, in the same way that you map
your business rule segments onto the neural segments, you can do the same
thing for other types of customer classification. As an example, you could
segment your customers in terms of profitability or any other business measure,
and then map this onto the neural clusters and see if there are regions where
particular type of customer behavior gives rise to particular categories of the
business metric. You will have to try this out for yourselves to see what we mean.
Data derived segments from transaction level data
In this section we have concentrated on segmenting customers who we can
identify using loyalty card information and for whom we can aggregate
transactions to produce the CLA data model. The main reason for doing this was
to show how to map the predefined business rule segments onto the data
derived segments. If you only have point of sale transaction level data, but no
means of performing customer level aggregation, then can you still perform data
derived segmentation?
If you perform clustering using the TLA model, then you will also produce a
segmentation that reflects the characteristics of your customers in terms of the
combinations of products they purchased during a single transaction. In this case
the picture is somewhat different from the CLA model results, as shown in
Figure 4-18.

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