When used wisely within Customer Relationship
Management applications data mining can significantly improve the bottom line.
It will end the process of randomly contacting a prospective or current
customer through a call centre or by mailshot. With the effective use of data
mining a company can concentrate its efforts on targeting prospects that have a
high likelihood of being open to an offer.
This in turn gives the ability for more sophisticated methods to be used
such as campaigns being optimised to individuals.
Businesses that employ data mining techniques will
usually see a high return on investment, but will also find that the number of
predictive models can quickly increase. Rather than just implementing one model
to predict which customers will respond positively, a business could build a
different models for each region and customer type. Then instead of sending an
offer to all prospects it may only want to send to prospects that have a high
chance of taking up the offer. It may also want to determine which customers
are going to be profitable during a certain time frame and direct their efforts
towards them. To be able to maintain this quantity and quality of models, these
model versions have to be well managed and automated data mining implemented.
Human Resources departments can also make a valid
case for using data mining. It will allow them to in identifying the
characteristics of their most successful employees. Information gained from
such as resource can help HR focus their recruiting efforts accordingly.
Another example of data mining , is that used in
retail. Often called market basket
analysis, it is, for example, when a
store records the purchases of customers, it could identify those
customers who favour silk shirts over cotton ones; or customers who bought
certain grocery items would also also buy the same specific item as well. This
is often highlighted in on-line stores when you are told that so many people
who bought a certain book or CD also bought XX as well.
Although some explanations of relationships may be
difficult, taking advantage of it is easier. The example deals with association
rules within transaction-based data. Not all data are transaction based and
logical or inexact rules may also be present within a database. In a
manufacturing application, an inexact rule may state that 73% of products which
have a specific defect or problem will develop a secondary problem within the
next six months.
Disclaimer: All information on this site is provided for informational purposes only! By no means is any
information presented herein intended to substitute for the advice provided to you by any health care or other professional
or organization.
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