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Chapter 6 Foundations of Business Intelligence: Databases and Information Management 259

the effect of those decisions. The types of information obtainable from data
mining include associations, sequences, classifications, clusters, and forecasts.

• Associations are occurrences linked to a single event. For instance, a study
of supermarket purchasing patterns might reveal that, when corn chips
are purchased, a cola drink is purchased 65 percent of the time, but when
there is a promotion, cola is purchased 85 percent of the time. This informa-
tion helps managers make better decisions because they have learned the
profitability of a promotion.
• In sequences, events are linked over time. We might find, for example, that if
a house is purchased, a new refrigerator will be purchased within two weeks
65 percent of the time, and an oven will be bought within one month of the
home purchase 45 percent of the time.
• Classification recognizes patterns that describe the group to which an
item belongs by examining existing items that have been classified and
by inferring a set of rules. For example, businesses such as credit card or
telephone companies worry about the loss of steady customers. Classification
helps discover the characteristics of customers who are likely to leave and
can provide a model to help managers predict who those customers are so
that the managers can devise special campaigns to retain such customers.
• Clustering works in a manner similar to classification when no groups have
yet been defined. A data mining tool can discover different groupings within
data, such as finding affinity groups for bank cards or partitioning a database
into groups of customers based on demographics and types of personal
investments.
• Although these applications involve predictions, forecasting uses predictions
in a different way. It uses a series of existing values to forecast what other
values will be. For example, forecasting might find patterns in data to help
managers estimate the future value of continuous variables, such as sales
figures.
These systems perform high-level analyses of patterns or trends, but they
can also drill down to provide more detail when needed. There are data mining
applications for all the functional areas of business, and for government and
scientific work. One popular use for data mining is to provide detailed analyses
of patterns in customer data for one-to-one marketing campaigns or for
identifying profitable customers.
Caesars Entertainment, formerly known as Harrah’s Entertainment, is the
largest gaming company in the world. It continually analyzes data about its
customers gathered when people play its slot machines or use its casinos and
hotels. The corporate marketing department uses this information to build a
detailed gambling profile, based on a particular customer’s ongoing value to
the company. For instance, data mining lets Caesars know the favorite gaming
experience of a regular customer at one of its riverboat casinos, along with that
person’s preferences for room accommodations, restaurants, and entertain-
ment. This information guides management decisions about how to cultivate
the most profitable customers, encourage those customers to spend more, and
attract more customers with high revenue-generating potential. Business intel-
ligence improved Caesars’s profits so much that it became the centerpiece of
the firm’s business strategy.

Text Mining and Web Mining
However, unstructured data, most in the form of text files, is believed to
account for over 80 percent of useful organizational information and is one

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