An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 32

Scoring with Classification Models
Once the classification model is trained and evaluated, the next step is to deploy it
and use the generated results to develop and carry out direct marketing campaigns.
Each model, apart from offering insight through the revealed data patterns, can
also be used as a scoring engine. When unseen data are passed through the derived
model, they are scored and classified according to their estimated confidence
scores.
As we saw above, the procedure for assigning records to the predefined classes
may not be left entirely to the model specifications. Analysts can consult the gains
charts and intervene in the predictions by setting a classification threshold that
best serves their needs and their business objectives. Thus, they can expand or
decrease the size of the derived marketing campaign lists according to the expected
response rates and the requirements of the specific campaign.
The actual response rates of the executed campaigns should be monitored
and evaluated. The results should be recorded in campaign libraries as they could
be used for training relevant models in the future.
Finally, an automated and standardized procedure should be established that
will enable the updating of the scores and their loading into the existing campaign
management systems.
MARKETING APPLICATIONS SUPPORTED BY CLASSIFICATION MODELING
Marketing applications aim at establishing a long-term and profitable relationship
with customers, throughout the whole lifetime of the customer. Classification
models can play a significant role in marketing, specifically in the development
of targeted marketing campaigns for acquisition, cross/up/deep selling, and reten-
tion. Table 2.5 presents a list of these applications along with their business
objectives.
All the above applications can be supported by classification modeling. A
classification model can be applied to identify the target population and recognize
customers with an increased likelihood for churn or additional purchase. In other
words, the event of interest (acquisition, churn, cross/up/deep selling) can be
translated into a categorical target field which can then be used as an output in a
classification model. Targeted campaigns can then be conducted with contact lists
basedondataminingmodels.
Setting up a data mining procedure for the needs of these applications
requires special attention and co-operation between data miners and marketers.