stock repurchase announcements: a test of market ... - Asbbs.org

Mining for Knowledge..Higher Ed
should. Association rule mining can also be applied to retail transaction data for optimal product
placement (Chen, et al, 2006).
Marketing departments can also benefit from data mining. Ways that marketing departments
might use data mining include customer profiling, targeting, and market-basket analysis.
Customer profiling helps organizations identify customers that will be the most profitable. It is
also a significant aspect of customer relationship management (CRM). According to Olson and
Shi (2007), CRM is about “targeting customers for special treatment based on their anticipated
future value to the firm” (p. 194). For example, marketing professionals can use data mining
techniques to help determine which customers might respond to a given advertisement or
promotion. By gathering customer demographic information and comparing it to recent sales
data, marketing staff are able to predict which customers to target for their next advertisement. In
this type of application of data mining, highly accurate data is required in order to produce an
accurate prediction model. Additionally, data quality has a significant impact on data mining
results. Database marketing is an application of data mining in which marketing data is used for
customer acquisition, development, and retention (Lo, 2002). Customer segmentation was one of
the first uses of data mining. In this scenario, data mining tools take massive amounts of data and
segment the data into categories, which can then be used to predict responses to advertising
campaigns (Olson & Shi, 2007). Through the use of customer relationship management (CRM),
organizations can also analyze the expected churn of its customers. According to Olson and Shi
(2007), large customers are “often targets of intense competition, and thus are high risks for
churn” (p. 195). Some of the data mining tasks used to analyze church include neural networks
and regression analysis.
According to a GAO Report (2004), many government agencies use data mining. Each year,
government agencies are surveyed to determine how each is using data mining. For example, the
Defense Intelligence Agency (DIA) had three operational data mining systems that are used for
“analyzing intelligence and detecting terrorist activities” (GAO, 2004, p. 30). One system at the
DIA, called PATHFINDER, is designed to analyze large volumes of government and private
sector databases rapidly. This particular system analyzes patterns related to terrorist activities. It
also contains personal information about citizens and matches data with data from other agencies.
Another example found in the Department of the Air Force is called the Integrated Space Warfare
Center Information System (SWC), designed to improve performance and service related to
personnel and warfighter needs. Most of these systems do not use private sector data, but do
contain personal information about individuals.
Solomon et al. (2006) investigated data mining as it pertains to traffic safety improvement. The
research examined how data mining can evaluate the effectiveness of cameras installed at
intersections in reducing driver and pedestrian fatalities. Solomon et al. applied a variety of data
mining models to traffic data gathered from the Washington, D.C. area after traffic lights were
installed at several busy intersections. Solomon’s research team applied decision tree models, kmeans
clustering, neural network, and market basket analysis to demonstrate how data mining
models can be used to reduce fatalities at traffic signal intersections. Results suggested that
installing cameras at strategically chosen intersections did, in fact, reduce fatalities. These
conclusions were based on real fatal accident data used for the data mining tasks. Finally,
Solomon et al. noted that it is imperative to collect data in a consistent and complete manner
when engaging in data mining (Solomon, Nguyen et al., 2006).
The retail industry uses data mining for strategic and tactical decision-making. A study by Chen
et al. (2006) investigated the spatial relationships between adjacent items on store shelves. While
much work has been done on the use of data mining in retail, prior work has not considered
ASBBS E-Journal, Volume 4, No.1, 2008 57