94 Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data
Figure 4-19 Scoring using the data mining tool directly
There may be situations where it is not possible to perform this operation directly
from the data mining tool or where scoring all of your customers is either
unnecessary or desirable. For example, the clusters may have been developed
using a subset of customer records held in a separate database from the one
containing the customer records you want to score. In such cases the use of the
second method of scoring may be more appropriate. This is discussed below.
4.7.3 Using the cluster results to score selected customers
While scoring all of your customers can be performed as some form of batch
process, if you have large numbers of customers this may introduce a significant
overhead on database performance. If you have to score all your customers this
may be unavoidable, but in many situations there may only be a subset of the
customers that it is appropriate to score at any one time. This is increasingly true
for e-commerce applications where you may only want to score customers as
they use a particular service. Using the exported PMML models, it is possible to
score selected customer records directly within the database. This type of
scoring is performed using available database functions and therefore can be
done automatically, for example, as customer records are being updated or at
Chapter 4. How can I characterize my customers from the mix of products that they purchase? 95
specified times. An additional advantage of this approach is that the PMML
models themselves are stored in the database, and therefore the configuration
control of which models were used and when, can be handled by standard
database administration procedures. The process is illustrated in Figure 4-20.
Figure 4-20 Scoring customers using exported PMML models
Using this type of approach for deploying data mining solutions into your
business offers many advantages. It makes possible the deployment of the same
models into distributed applications being used across your business. The same
models, although developed by your operational research department, could be
being used by your sales and marketing organization (in OLAP and other forms
of reporting tool such as Business Objects, BRIO and so on), at the same time
the models can be deployed into your CRM systems (kiosks, Web-shopping and
similar customer touch points) and as part of your campaign management tools
(direct mailing, telesales and so on). As the models are updated or improved,
they can easily be replaced in all of the tools that you are using and consistency
is then maintained.
96 Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data
There are an increasing number of applications that can make use of the
capability to deploy data mining models in this way and as e-commerce expands
in scope, there will be many more opportunities to exploit this capability. In
Chapter 6, How can I decide which products to recommend to my customers?
on page 137, we give another example of how this capability can be used as part
of a personalized product recommendation system.
What needs to be stressed is that the deployment of the results of data mining
into your business can bring significant benefits and wide ranging benefits. It is
worth spending time considering how you are going to use the results before
embarking on any data mining activity.

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