Page 153 - DCAP208_Management Support Systems

Management Support Systems

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Example: You can use data mining to find out which customers will respond favorably
to a direct mail marketing strategy. You can also use data mining to determine the effectiveness
of interactive marketing. Some of your customers will be more likely to purchase your products
online than off-line, and you must identify them.
While many businesses use data mining to help increase their profits, many of them don’t
realize that it can be used to create new businesses and industries. One industry that can be
created by data mining is the automatic prediction of both behaviors and trends. Imagine for a
moment that you were the owner of a fashion company, and you were able to precisely predict
the next big fashion trend based on the behavior and shopping patterns of your customers? It is
easy to see that you could become very wealthy within a short period of time. You would have
an advantage over your competitors. Instead of simply guessing what the next big trend will be,
you will determine it based on statistics, patterns, and logic.

Another example of automatic prediction is to use data mining to look at your past marketing
strategies. Which one worked the best? Why did it work the best? Who were the customers that
responded most favorably to it? Data mining will allow you to answer these questions, and once
you have the answers, you will be able to avoid making any mistakes that you made in your
previous marketing campaign.
Data mining can allow you to become better at what you do. It is also a powerful tool for those
who deal with finances. A financial institution such as a bank can predict the number of defaults
that will occur among their customers within a given period of time, and they can also predict
the amount of fraud that will occur as well.
Another potential application of data mining is the automatic recognition of patterns that were
not previously known. Imagine if you had a tool that could automatically search your database
to look for patterns which are hidden. If you had access to this technology, you would be able to
find relationships that could allow you to make strategic decisions.
Because your decisions are based on logic, you would increase the chances of being successful.
While data mining is a very valuable tool, it is important to realize that it is not a panacea. Even
if an automated technology should be invented, it will not guarantee the success of you or your
company. However, it will tip the odds in your favor.
Two critical factors for success with data mining are: a large, well-integrated data warehouse
and a well-defined understanding of the business process within which data mining is to be
applied (such as customer prospecting, retention, campaign management, and so on).
Some successful application areas include:

A pharmaceutical company can analyze its recent sales force activity and their results to
improve targeting of high-value physicians and determine which marketing activities
will have the greatest impact in the next few months. The data needs to include competitor
market activity as well as information about the local health care systems. The results can
be distributed to the sales force via a wide-area network that enables the representatives
to review the recommendations from the perspective of the key attributes in the decision
process. The ongoing, dynamic analysis of the data warehouse allows best practices from
throughout the organization to be applied in specific sales situations.

A credit card company can leverage its vast warehouse of customer transaction data to
identify customers most likely to be interested in a new credit product. Using a small test
mailing, the attributes of customers with an affinity for the product can be identified.
Recent projects have indicated more than a 20-fold decrease in costs for targeted mailing
campaigns over conventional approaches.

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